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What is the Job of an EA in the AI Era?
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What is the Job of an EA in the AI Era?

The landscape of artificial intelligence is evolving and its usage in the workplace is increasingly common. Companies that are looking for fast growth can find automation tools greatly beneficial for their efficiency. Executive assistants can be great allies not only in using AI tools, but also in implementing their usage across the organization.

Most AI tools are referred to as “assistants” and they’re intended to automate and facilitate daily tasks, especially the most repetitive ones. Tools like Calendly, Superhuman, Asana, and Slack help executives daily with calendar appointments, team collaboration, and email prioritization. Other AI tools like Siri, Google Assistant, or Alexa have conversational abilities and can help by making calls, reading texts, or setting up reminders. Sometimes, the support is so seamless that it leaves people wondering if AI will replace human assistants, like executive assistants (EAs). 

The answer is: Probably not. As an executive, the best approach is to avoid extreme postures. Being afraid of new technologies can leave EAs without the proper toolkit to perform efficiently. On the other hand, assuming that AI can do the job of a top-tier executive assistant is far-fetched. It’s crucial to strike a balance between the value of AI and the value of human support. 

How executive assistants are using AI 

Photographers use all sorts of digital tools in their trade. Most use a digital camera and software like Photoshop and Lightroom. These tools help them do their job faster and even better, but they still need a great deal of technical expertise, an eye for detail, and training to produce the best images. 

It’s a good example of how an executive assistant uses AI. EAs can use tools to record meetings and make transcripts or set automated replies or reminders for teams. They can leverage GenAI to build non-confidential decks or process playbooks. These are essential, useful tools. Yet, executive assistants need to show proactiveness, possess a high degree of intuition to make decisions, be ethical and trustworthy, and have ongoing training that will allow them to perform their duties efficiently.

The better we learn how to use AI tools and technology, the more benefit we’ll get from them. 

EAs’ diverse tasks, responsibilities, and audiences require an assortment of skills and tools. LLMs and automation are greatly appreciated and empowering for executive assistants and are starting to become tools of the trade. 

The following tasks are being documented and performed by the team of executive assistants at Viva. 

Building usage databases for the entire team

The team of executive assistants built a centralized database with the collective knowledge of AI and LLMs’ usage. Every team member has access to edit and add new information about how LLMs have worked for them. The two top LLMs they use are ChatGPT (multiple versions) and Copilot (Bing). 

The databases feature guides on usage like how to handle confidential information (and how to assess what is confidential), how to assess for ethical content, and how to confirm data. These guides also include prompt recommendations and basic structures for any team member’s use. 

Here’s an example of a basic prompt structure found in said database:

Basic prompt structure

Use cases of AIs and LLMs by executive assistants

Executive assistants use LLMs in a variety of ways. Here are some common EA tasks where LLMs prove useful: 

Project management and vendor research

  • Project management: Coordinate tasks and timelines, track progress, and ensure project objectives are met.
    • [Prompt example] Summarize these notes into a bulleted list overview, and write all the action items in a separate list.
    • [Prompt example] Create a detailed project plan for the [project name], including key milestones, deadlines, and task assignments. Ensure all tasks are clearly outlined and assigned to appropriate team members.
  • Vendor research: Gather information on potential vendors, compare their offerings, and present options to the executive for decision-making.
    • [Prompt example] Compare the offerings of the identified vendors, including pricing, services provided, and any unique features or benefits. Present the findings in a comparative table
    • [Prompt follow-up]  Gather customer reviews and testimonials for each vendor to assess their reliability and quality of service. Summarize the feedback and include it in the vendor comparison report.

Meeting and presentation support

  • Create pre-meeting briefs: Summarize key points, agenda items, and background information for upcoming meetings to ensure the executive is well-prepared.
    • [Prompt example] I’m meeting X for an {investor/sales/partnership/co-branding/etc} meeting. Create a pre-meeting brief about company XYZ (paste LinkedIn profile + webpage URL). Include these bullet points: the year it was founded, key information, industry, shared investors, business model, latest news, headquarters, fundraising stage, insights on anything else I’d need to know, and headcount.
  • Create presentations (slide outline and content of non-confidential information): Develop slide decks with relevant content and visual aids to effectively convey information during presentations.
    • [Prompt example] Ask ChatGPT to create a PowerPoint presentation and export it. Afterward, ask it to give you the VBA code. It will give you a code which you can then input in PowerPoint, and it will create the slides for you.

Writing and content creation

  • Generate content ideas for social media: Brainstorm and propose engaging content ideas for social media platforms to enhance brand visibility and engagement.
    • [Prompt example]  “Brainstorm 10 engaging content ideas for our company’s LinkedIn page to enhance brand visibility and engagement in the tech industry.”
    • [Prompt example]  “Propose a week’s worth of Instagram posts for our lifestyle brand, focusing on promoting our new product line and increasing follower interaction.Our URL is X”
  • Create questions for lead generation, customer satisfaction surveys, and interviews: Formulate questions to gather valuable insights from leads, customers, or interviewees to inform decision-making processes.
    • [Prompt example] “Formulate 15 questions for a customer satisfaction survey to gather insights on our new service and identify areas for improvement.”
    • [Prompt example] “Create 5 interview questions to ask potential leads during initial sales calls to understand their needs and how our solutions can meet them.”
  • Writing in your executive’s tone and style: Craft emails, memos, or other written communications in a way that reflects the executive’s tone.
    • [Prompt example] “Compose a memo from the executive to the team, encouraging them after a successful project completion and outlining the next steps, maintaining the executive’s tone of voice.”
    • [Prompt example] “Draft responses to comments on the CEO’s LinkedIn posts, ensuring the replies reflect the CEO’s professional and engaging tone. Here are 10 other previous responses she’s said as examples (attach screenshots).”
    • [Prompt example] “Create a template for responding to congratulatory comments on the CEO’s LinkedIn profile, expressing gratitude and maintaining their funny and appreciative style.”

Concept clarification

  • Understand difficult concepts: Seek explanations for or simplification of complex concepts or terms.
    • [Prompt example] “Explain the concept of blockchain technology in simple terms suitable for someone with no technical background.”
    • [Prompt example] “What are the most significant challenges facing the implementation of technology in healthcare, and how are companies addressing these issues?”

Optimizing productivity and workflow

  • Enhance productivity: Seek advice on optimizing personal workflow and time management strategies.
    • [Prompt example] “What are 5 ways to foster a collaborative environment as an executive assistant to a 20-person, Series A startup CEO based in the US?”
  • Automate repetitive tasks: Explore automation tools or methods to streamline repetitive tasks and reduce manual workload.
    • [Prompt example] “Explore methods to automate data entry tasks for our healthcare CRM system to save time and minimize errors.”
  • Avoid getting stuck: Seek guidance on how to approach unfamiliar tasks or situations to overcome obstacles and achieve objectives.
    • [Prompt example] “I’m an executive assistant, my executive just asked me to do {add task}. Where do I start?”
    • [Prompt example] “Provide guidance on handling {an unfamiliar software tool} that I need to use for an upcoming project.”

Personal queries

  • Personal questions: Seek information or clarification on various topics of personal interest or curiosity to broaden knowledge and understanding.
    • [Prompt example] “What are wellness activities that I can add to my daily routine to avoid burnout?”
    • [Prompt example] “Make a 5-day meal plan for busy people. Include plenty of vegetables and meat recipes only twice a week.”

AI’s proven impact on EA automation goes beyond the hype

The use of LLMs or AI in the workplace is nothing new and is clearly gaining traction. Think Grammarly, Calendly, Magical, and others. Executive assistants are leveraging all of this technology to better support the executives they work with. The fast-paced environment of startups seems to be calling for it. 

Here are some examples of what we mean:

  1. EAs use Grammarly to proofread and improve the quality of emails, reports, and presentations. It’s great for catching typos and ensuring the writing is clear.
  2. Calendly allows users to determine specific time slots in their calendars for meetings. Stakeholders can quickly self-schedule an appointment. EAs can make sure that there are spots for customers always available and arrange meeting agendas whenever a new appointment is scheduled. 
  3. EAs use Magical to automate repetitive tasks such as email templates, signatures, instructions, reminders, and greetings. Using a single command, they can easily retrieve these templates. 
  4. Otter.ai is used to transcribe meetings and interviews. EAs use transcripts to extract action items, create workflows or processes, and document information. These transcripts are shared with the leadership team for further reference. 

Concluding thoughts

In conclusion, the landscape of artificial intelligence is evolving and its usage in the workplace is increasingly common. Companies that are looking for fast growth can find automation tools greatly beneficial for their efficiency.

Executive assistants can be great allies not only in using AI tools, but also in implementing their usage across the organization. Having an executive assistant who is trained in these technologies is a contributing factor to success. Since technologies are ever-changing and evolving, an executive assistant also needs to be proactive and curious, with critical thinking and analytical skills. 

These days, adopting extreme views on AI—whether idolizing it or fearing it—is not the most effective approach to new technologies. Instead, recognizing that AI tools are designed to complement human effort can lead to remarkable outcomes. We see the future as EAs + AI. Not EAs replaced by AI. If you’re curious to learn more about how we do that at Viva, let’s chat.

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By ● Jun 18, 2024
The impact of AI on accounting careers: A boon, not a bane
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The Impact of AI on Accounting Careers: A Boon, Not a Bane

The threat of artificial intelligence (AI) replacing human workers in accounting careers is a common concern. But a closer look reveals a different story. By automating tedious tasks, AI can free up time to focus on more strategic and analytical work.

The threat of artificial intelligence (AI) replacing human workers in accounting careers is a common concern. But a closer look reveals a different story.

AI is poised to transform accounting careers, not eliminate them. It will free professionals from the shackles of repetitive tasks, allowing them to focus on more strategic and analytical work, ultimately making accounting careers more fulfilling and impactful.

This shift, however, necessitates a change in the skills required for career success. The days of mindlessly processing invoices are slowly fading. The future belongs to those who can leverage AI’s power to unlock valuable insights and contribute meaningfully to financial decision-making.

History repeats itself

Technology has transformed accounting before. The introduction of spreadsheets sparked similar anxieties about job losses. Yet, figures from Morgan Stanley show that bookkeepers didn’t become extinct. In fact, the number of accountants, auditors, and finance managers significantly increased.

The lesson? When technology automates tedious tasks, it creates opportunities for more analytical and value-added work.

Use case: How AI empowers AP teams

At its core, AI excels at automating repetitive tasks that consume a significant portion of an AP team’s time. Here are some examples of how AI is streamlining AP processes:

  • Touchless expense reports: Imagine a world where expense reports are generated automatically. Generative AI can create them from a simple snapshot of a receipt, complete with AI-powered categorization, saving time and reducing errors. Employees can simply photograph their receipts with their mobiles, and the AI can extract relevant information like date, vendor, and amount, automatically populating an expense report. This not only frees up employees from tedious data entry but also reduces the risk of human error.
  • Optical character recognition (OCR) for bills: AI-powered OCR technology can extract data from bills with remarkable accuracy, boosting efficiency in processing invoices and expenses. Previously, manually entering data from invoices was a time-consuming and error-prone process. OCR eliminates this burden by automatically capturing data points like vendor information, invoice number, and line items. This not only saves time but also ensures greater accuracy in data capture.
  • AI-based receipt matching: No more manually matching receipts to transactions. AI can automate this tedious task, freeing up valuable time for AP professionals. Matching receipts to corresponding transactions can be a cumbersome process, especially for companies that process a high volume of invoices. AI can streamline this process by automatically matching receipts to the appropriate transactions based on predefined rules and data points.
  • Automated vendor categorization: Gone are the days of manually assigning categories to vendors. AI can automate this process for physical card transactions, reducing the workload for AP teams. Manually classifying vendors into different expense categories can be a time-consuming task. AI automates this process by analyzing spending patterns and automatically assigning vendors to the appropriate categories. This not only saves time but also improves the accuracy and consistency of expense categorization.
  • Seamless bill & PO matching: AI can streamline the process of matching bills to purchase orders, ensuring accuracy and timely payments. Traditionally, matching bills to purchase orders involved manual verification to ensure accuracy. Today, this can be automated by comparing data points between bills and purchase orders, flagging any discrepancies for review. Automation reduces the risk of errors and ensures timely payments to vendors.

Beyond automation, AI as an analytical powerhouse

While automation is a clear benefit, AI’s true potential lies in its ability to handle vast amounts of data analysis.

However, to unlock this potential, finance professionals need to develop the right skillset. Nicolas Boucher, founder of AI Finance Club, emphasizes how AI can elevate the value finance leaders bring to their organizations. When used correctly, AI can assist with functions like:

  • Scenario analysis: Simulating different business scenarios to evaluate potential outcomes and make informed decisions. For instance, AI can be used to model the impact of changes in interest rates, currency fluctuations, or market conditions on a company’s cash flow. This allows businesses to make data-driven decisions and plan for potential risks.
  • ROI analysis: Measuring the return on investment for various initiatives, helping businesses prioritize resources effectively. AI can analyze historical data and financial projections to calculate the potential ROI of different projects or investments. This allows companies to allocate resources strategically and maximize their return.
  • Trend analysis: Identifying patterns and trends in financial data to gain insights into business performance. AI can analyze vast amounts of financial data to identify trends and patterns that might not be readily apparent to humans. This allows businesses to stay ahead of potential problems and capitalize on emerging opportunities.
  • Variance analysis: Investigating discrepancies between budgeted and actual financial results to identify areas for improvement. AI can compare budgeted figures to actual spending and pinpoint variances. This allows businesses to identify areas where spending is exceeding budget and take corrective action.

New skills for success

As technology takes over the mundane tasks, the essential skills needed for success in accounting careers are changing. The emphasis on spreadsheets, programming, and data generation is giving way to a greater focus on financial planning and analysis (FP&A).

While some may find the need for Python proficiency daunting, Boucher offers a silver lining, “Ask ChatGPT to open the realm of possibilities, that is use Python. Python is the best tool to automate finance. And I mean, automate finance.” ChatGPT can use Python to read and analyze data from spreadsheets, as demonstrated by Boucher’s examples of uploading data and receiving AI-generated visualizations and recommendations.

Is AI a cure for burnout?

A record number of accountants are leaving the profession, with burnout cited as a major factor. A study by Avalara shows that over 80% of CFOs face a talent shortage in their teams. Many accounting professionals can relate to the feeling of being bogged down by endless manual tasks. Strategic tasks that leverage education and challenge thinking are much more engaging.

AI in accounting: partner, not a replacement

The world of accounting is welcoming a powerful new partner. Contrary to fears of robots taking over, AI is actually poised to make accounting careers more fulfilling and impactful.

According to the State of AI in Accounting Report by Karbon, “82% of accountants are intrigued or excited by AI, yet only 25% are actively investing in AI training for their teams.”

The results speak for themselves. Artificial intelligence is here to stay and the ripples it has created is undeniable to ignore. A strong commitment to AI training only means that the role of accountant becomes more varied, helped by the invisible accountant, AI.

Staying ahead of the curve

No one is being replaced, but change has arrived. Here are some tips for forward-thinking accounting professionals who want to thrive in the age of AI:

  • Embrace the change: Explore the wealth of resources available, such as webinars, courses, and e-books on AI for accounting. Many professional organizations and online platforms offer training specifically designed to help accountants develop their AI skills.
  • Communication is key: The effectiveness of many AI tools relies on clear and concise communication. Learn how to interact with these tools to get the most out of them. Understanding the capabilities and limitations of AI tools is crucial for maximizing their effectiveness.
  • Data mastery: As the focus shifts to understanding data, develop your skills in pulling the right data for analysis. Finance professionals will need to be comfortable with querying databases, manipulating data sets, and using data visualization tools to extract meaningful insights.
  • Become technologically proficient: Take the time to explore and experiment with AI-powered automation tools.

The future of accounting is bright

Many accounting professionals can relate to the feeling of being bogged down by endless manual tasks. The good news is that AI offers a solution. By automating tedious tasks, AI can free up time to focus on more strategic and analytical work. This, in turn, can lead to increased job satisfaction and a brighter outlook for the future of accounting careers.

While solutions like Airbase can play a role in AP automation with its AI-powered features like automated invoice processing and real-time payment tracking, it’s important to keep the broader impact of AI in mind. Staying on top of trends and being willing to embrace the changes created by AI.

 

 

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By ● Jun 7, 2024
How Data Scientists Leverage AI for Enhanced Efficiency and Effectiveness
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How Data Scientists Leverage AI for Enhanced Efficiency and Effectiveness

AI is not just a tool for data scientists; it’s a powerful ally that enhances their capabilities, allowing them to focus on what they do best

In the rapidly evolving world of technology, AI is no longer just a buzzword; it’s the most disruptive technological innovation of the 21st century. According to a 2024 McKinsey report, 70% of companies are already harnessing AI to streamline operations and enhance decision-making processes, demonstrating its profound impact across industries.

Among those at the forefront of this revolution are data scientists. These modern-day alchemists turn raw data into golden insights, driving decisions that propel businesses forward. Sometimes even the wizards of data science need a little magic, and that’s where AI steps in. Let’s explore how data scientists are harnessing the power of AI to become more effective and efficient in their roles.

Automating the mundane

Data science is inherently complex and involves a multitude of tasks ranging from data collection and cleaning to analysis and interpretation. Traditionally, these tasks have been time-consuming and often tedious. However, AI has introduced a wave of automation that liberates data scientists from the drudgery of repetitive work, allowing them to focus on more strategic and creative aspects of their jobs.

Take data cleaning, for instance. This foundational step is crucial for ensuring the quality of insights but is often considered the least glamorous part of the process. AI-powered tools can now automate much of this task by identifying and rectifying errors, handling missing values, and normalizing data formats. A recent Gartner study revealed that data scientists spend up to 60% of their time on data preparation, but AI can reduce this effort by up to 40%, allowing them to focus more on analysis and strategy. This not only speeds up the process but also enhances accuracy, as AI algorithms are less prone to human error.

The future of predictive analytics

Predictive analytics is where data science truly shines, and AI is amplifying its power exponentially. Traditional statistical models have long been used to forecast trends and behaviors, but AI algorithms—especially those based on machine learning—offer a more robust and dynamic approach.

Machine learning models can process vast amounts of data at unprecedented speeds, learning and improving over time. This iterative learning process allows AI to uncover intricate patterns and relationships within the data that might elude human analysts. 

For example, in financial services, AI-driven predictive models can analyze market trends, customer behavior, and economic indicators to provide highly accurate investment forecasts. A Forrester report also found that companies leveraging AI for predictive analytics saw a 20% increase in forecast accuracy. This additional level of insight empowers data scientists to make more informed recommendations, driving better business outcomes and optimizing models for ROI

Natural language processing: Making sense of text data

A significant portion of the world’s data is unstructured, particularly in the form of text, and it’s being created quicker than you’d imagine. Emails, social media posts, customer reviews, and more hold valuable insights if one can store, clean, and decode them. Natural Language Processing (NLP), a branch of AI, equips data scientists with the tools to do just that.

NLP algorithms can parse through massive volumes of text data, extracting sentiment, identifying key themes, and even summarizing information. More advanced NLP models can even identify and correct coding errors, which allow data scientists to scale models with greater confidence. 

This capability is invaluable for businesses looking to understand customer sentiment, monitor brand reputation, gain insights into market trends, or drive operational clarity. For instance, a company launching a new product can use NLP to analyze social media feedback in real-time, enabling swift adjustments to marketing strategies based on customer reactions.

According to a 2024 IDC report, businesses utilizing NLP data insights experience a 30% improvement to customer satisfaction scores, as they can more effectively analyze and respond to customer feedback. 

Real-time data analysis

The ability to process and analyze data in real-time is a game-changer for many industries, and AI is at the heart of this capability. Real-time data analysis allows businesses to respond to events as they happen, providing a significant competitive edge. According to a recent Splunk report, 80% of companies have seen an increase in revenue due to the adoption of real-time data analytics, as it enabled faster decision-making and operational decision making. 

In sectors such as e-commerce, AI-driven real-time analytics can optimize inventory management, personalize customer experiences, and improve supply chain efficiency. For data scientists, real-time analysis tools mean faster and more accurate decision-making. They can set up automated systems that monitor data streams, trigger alerts for anomalies, and even take predefined actions without human intervention. This not only enhances operational efficiency but also ensures that businesses can capitalize on opportunities and mitigate risks promptly.

Enhancing model accuracy and robustness

Building accurate and robust models is a core responsibility of data scientists, and AI is playing a pivotal role in this area.

Advanced AI techniques such as deep learning can handle complex datasets with high-dimensional features, providing unparalleled accuracy in fields like image and speech recognition. Moreover, AI frameworks can perform automated machine learning (AutoML), which simplifies the model-building process, making it accessible even to those with less expertise. This democratization of data science tools means that businesses of all sizes can benefit from cutting-edge analytics, driven by AI-empowered data scientists.

Facilitating collaboration and knowledge sharing

AI is also transforming the way data scientists collaborate and share knowledge, with research from Stanford showing 25% average improvement in AI-enabled team productivity. Platforms powered by AI can facilitate better project management, version control, and knowledge sharing within data science teams. For instance, AI-driven code review tools can automatically check for errors, suggest improvements, and ensure adherence to best practices. This not only streamlines the development process but also enhances the overall quality of the work.

AI can also aid in the creation of more intuitive and interactive dashboards and visualizations, making it easier for data scientists to communicate their findings to non-technical stakeholders. By bridging the gap between complex data insights and business decision-makers, AI ensures that valuable information is not lost in translation.

The future of data science: continuous evolution with AI

As AI continues to evolve, its integration with data science will only deepen, bringing about new innovations and efficiencies. The future holds promise for more sophisticated AI models that can understand more nuanced context, learn from smaller datasets, and provide even more accurate predictions, driving unprecedented business value..

AI is not just a tool for data scientists; it’s a powerful ally that enhances their capabilities, allowing them to focus on what they do best: deriving actionable insights from data. By automating mundane tasks, enhancing predictive analytics, making sense of unstructured data, enabling real-time analysis, improving model accuracy, and facilitating collaboration, AI is transforming data science into an even more dynamic and impactful field. As we move forward, the synergy between AI and data science will continue to unlock new possibilities, driving innovation across industries.

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By ● Jun 5, 2024
Turing AGI Icons Sam Altman
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Turing AGI Icons: Charting the Future with Sam Altman

In the first-ever event of the Turing AGI Icons series, OpenAI CEO Sam Altman explained how artificial general intelligence (AGI) will impact businesses.

Turing AGI Icons is dedicated to spotlighting the influential figures propelling the rapid advancement of artificial general intelligence. This series shares insights directly from icons leading the charge toward developing accessible and beneficial AGI at some of the world’s most cutting-edge companies.

The first event in the Turing AGI Icons series featured a conversation between Turing CEO Jonathan Siddharth and OpenAI CEO Sam Altman.

Here are some takeaways from the event.

1. Building AGI and helping people use it—one of the greatest quests in human history

Altman shared that building safe AGI and helping people deploy it widely would be a remarkable quest in human history. “I certainly cannot imagine a more fun, exciting, important thing to work on,” he mentioned. Altman also lauded the prosperity that would come from truly abundant intelligence with the ability to do things beyond what humans can do on their own. 

He added that it’s incredibly fun to be in the room at the forefront of scientific discovery. “We get to see what’s going to happen a little bit before anybody else, and we get to figure out, what I think, is the most interesting puzzle I can imagine. And so that’s quite rewarding to work on,” Altman explained. 

2. AGI is much more than its definition—it’s a continuous journey 

As the figurehead of OpenAI, Altman helped pierce through the fog surrounding AGI and its definition.

“I don’t think [the definition] matters. Honestly, I think AGI means smarter systems than what we have today; systems that are coming in at some point in the relatively approachable future. But we’re on this one continuum of increasing intelligence,” Altman elaborated. 

He mentioned that there were impactful inventions before AGI and that there will be more in the future. Therefore, viewing AGI as a continuum—as a continuous journey—is one of the most helpful mental shifts to make. 

3. 2024 will be about smarter, better models 

Talking about AGI’s journey this year, Altman mentioned that the models will get generally smarter. The one word he used to describe AGI was “capable.”

“I think that’s the special thing. It’s not that we’re going to add this modality or that modality or that we’re going to get better at this kind of reasoning or that part of the distribution. The whole thing is going to get generally smarter across the board. The fact that we’re living through this sort of AI revolution is going to seem much crazier in the history books than it does right now,” he said. 

4. A culture that values research, engineering, and safety 

One of the principles that Altman and the team believed in from the very beginning was equally valuing research, engineering, and safety. 

“We knew how to build a good engineering team and a good engineering culture. So, we brought that and research culture together. We started with safety because we really care about it. We were going to try our hardest to figure out how to make the system safe. And we did those three things for a while,” Altman explained.  

He further explained that building a culture that valued all of those principles was one of the most interesting and hardest challenges of the job.  “It was not like there was one first-class citizen [among the three] and everything else was neglected. So, we got all of those different areas of expertise to work together towards one harmonious ‘we care and we’re going to get the details right’ thing,” he added. 

The final word

Altman’s discourse touched on myriad facets of AGI, from its current landscape, ethical considerations, and challenges to its potential, and he hinted at a future where AGI became an integral part of our lives. 

Additionally, the event offered exclusive insight into the operations of the company pioneering the GenAI revolution with ChatGPT, including its vision for constructing beneficial, accessible, and safe AGI to enhance the well-being of humanity as a whole.

The promise of AI is boundless 

In a world where AI transformation is the new digital transformation, generative AI solutions are key to unleashing your business potential and maximizing your competitive advantage. Keeping pace with the evolving AI landscape can be challenging for even the most tech-savvy leaders. 

Turing can help you. Turing uses proprietary AI to help companies build enterprise applications, train and enhance LLMs, and hire on-demand technical professionals. Innovate your business with AI-powered talent. Head over to Turing.com for more information.

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By ● Mar 29, 2024
Generative AI LLMs
AI Services ● For Developers ● For Employers ● LLM Training Services ● Tech ● Tips, Tools, and Trends

13 Generative AI and LLM Developments You Must Know!

Generative AI and LLMs have transformed the way we do everything. This blog post shares 13 developments in the field that are set to take the world by storm this year.

The tech world is abuzz with innovation, and at the center of this whirlwind are generative AI and large language models (LLMs). Generative AI is the latest and, by far, the most groundbreaking evolution we’ve seen in the last few years. Thanks to the rise of powerful LLMs, AI has shot onto the world stage and transformed the way we do everything—including software engineering.

These innovations have begun to redefine our engagement with the digital world. Now, every company is on an AI transformation journey, and Turing is leading the way. 

In this blog post, I have shared a few things related to generative AI and LLMs I find cool as an AI nerd. Let’s get started. 

1. Optimizing for the next token prediction loss leads to an LLM “learning” a world model and getting gradually closer to AGI.

What does this imply? 

This refers to the LLM training process. By optimizing for the next token prediction loss during training, the LLM effectively learns the patterns and dynamics present in the language. Through this training process, the model gains an understanding of the broader context of the world reflected in the language it processes. 

This learning process brings the LLM gradually closer to achieving artificial general intelligence (AGI), which is a level of intelligence capable of understanding, learning, and applying knowledge across diverse tasks, similar to human intelligence.

2. The @ilyasut conjecture of text on the internet being a low-dimensional projection of the world and optimizing for the next token prediction loss results in the model learning the dynamics of the real world that generated the text.

Ilya Sutskever, cofounder and former chief scientist at OpenAI, suggested that text on the internet is a simplified representation of the real world. By training a model to predict the next word in a sequence (optimizing for the next token prediction loss), the model learns the dynamics of the real world reflected in the text. This implies that language models, through this training process, gain insights into the broader dynamics of the world based on the language they are exposed to.

3. The scaling laws holding and the smooth relationship between the improvements in diverse “intelligence” evals from lowering next-word prediction loss and benchmarks like SATs, biology exams, coding, basic reasoning, and math. This is truly emergent behavior happening as the scale increases.

As language models scale up in size, they exhibit consistent patterns, also known as “scaling laws holding.” Improvements in predicting the next word not only enhance language tasks but also lead to better performance in various intelligence assessments like SATs, biology exams, coding, reasoning, and math. This interconnected improvement is considered truly emergent behavior, occurring as the model’s scale increases.

4. The same transformer architecture with few changes from the “attention is all you need” paper—which was much more focused on machine translation—works just as well as an AI assistant.

“Attention is all you need” is a seminal research work in the field of natural language processing and machine learning. Published by researchers at Google in 2017, the paper introduced the transformer architecture, a novel neural network architecture for sequence-to-sequence tasks. 

Today, with minimal modifications, this transformer architecture is now proving effective not just in translation but also in the role of an AI assistant. This highlights the versatility and adaptability of the transformer model—it was initially designed for one task and yet applies to different domains today.  

5. The same neural architecture works on text, images, speech, and video. There’s no need for feature engineering by ML domain—the deep learning era has taken us down this path with computer vision with CNNs and other domains.

This highlights a neural architecture’s adaptability to work seamlessly across text, images, speech, and video without the need for complex domain-specific feature engineering. It emphasizes the universality of this approach, a trend initiated in the deep learning era with success in computer vision using convolutional neural networks (CNNs) and extended to diverse domains.

6. LLM capabilities are being expanded to complex reasoning tasks that involve step-by-step reasoning where intermediate computation is saved and passed onto the next step.

LLMs are advancing to handle intricate reasoning tasks that involve step-by-step processes. In these tasks, the model not only performs intermediate computations but also retains and passes the results to subsequent steps. Essentially, LLMs are becoming proficient in more complex forms of logical thinking that allow them to navigate and process information in a structured and sequential manner.

7. Multimodality—LLMs can now understand images and the developments in speech and video.

LLMs, which were traditionally focused on processing and understanding text, now have the ability to “see” and comprehend images. Additionally, there have been advancements in models’ understanding of speech and video data. LLMs can now handle diverse forms of information, including visual and auditory modalities, contributing to a more comprehensive understanding of data beyond just text.

8. LLMs have now mastered tool use, function calling, and browsing.

In the context of LLMs, “tool use” likely refers to their ability to effectively utilize various tools or resources, “function calling” suggests competence in executing specific functions or operations, and “browsing” implies efficient navigation through information or data. LLMs’ advanced capabilities have now surpassed language understanding, showcasing their adeptness in practical tasks and operations.

9. An LLM computer (h/t @karpathy) made me reevaluate what an LLM can do in the future and what an AI-first hardware device could do.

A few months ago, AI visionary Andrej Karpathy touched on a novel concept that created waves across the world: the LLM Operating System.

Although the LLM OS is currently a thought experiment, its implications may very well change our understanding of AI. We’re now looking at a future not just built on more sophisticated algorithms but one that is based on empathy and understanding—qualities we’ve originally reserved for the human experience.

It’s time we rethink the future capabilities of LLMs and gauge the potential of AI-first hardware devices—devices specifically designed with AI capabilities as a primary focus. 

10. Copilots that assist in every job and in our personal lives.

We’re living in an era where AI has become ubiquitous. Copilots integrate AI support into different aspects of work and daily life to enhance productivity and efficiency.

AI copilots are artificial intelligence systems that work alongside individuals, assisting and collaborating with them in various tasks. 

11. AI app modernization—gutting and rebuilding traditional supervised ML apps with LLM-powered versions with zero-shot/few-shot learning, built 10x faster and cheaper.

AI app modernization is all the buzz today. This process involves replacing traditional supervised machine learning apps with versions powered by LLMs. The upgraded versions use efficient learning techniques like zero-shot and few-shot learning through prompt engineering. Moreover, this process is faster and more cost-effective, delivering a quick and economical way to enhance AI applications.

12. Building fine-tuned versions of LLMs that allow enterprises to “bring their own data” to improve performance for enterprise-specific use cases.

Building customized versions of LLMs for enterprise applications is on the rise. The idea is to “fine-tune” these models specifically for the needs of a particular business or organization. The term “bring your own data” suggests that the enterprise can provide its own dataset to train and improve the LLMs, tailoring them to address unique challenges or requirements relevant to their specific use cases. This focuses on adapting and optimizing LLMs for the specific needs and data of an enterprise to enhance performance in its particular context.

13. RAG eating traditional information retrieval/search for lunch.

Advanced generative AI is outperforming traditional information retrieval/search. If you’re considering leveraging it, think about

-how you should be applying generative AI in your company

-how to measure impact and ROI

-creating a POC before making it production-ready

-the tradeoffs between proprietary and open-source models and between prompt engineering and fine-tuning

-when to use RAG

and a million other technical, strategic, and tactical questions.

So, what do these LLMs AI developments mean for your business?

The world has changed. AI transformation has become indispensable for businesses to stay relevant globally. Turing is the world’s leading LLM training services provider. As a company, we’ve seen the unbelievable effectiveness of LLMs play out with both our clients and developers. 

We’ll partner with you on your AI transformation journey to help you imagine and build the AI-powered version of your product or business. 

Head over to our generative AI services page or LLM training services page to learn more.

You can also reach out to me at jonathan.s@turing.com.

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By ● Feb 19, 2024
AI Code Review: Improving Software Quality and Efficiency
AI Services ● Custom Engineering ● For Employers ● Tech ● Tips, Tools, and Trends

AI-Enhanced Code Reviews: Improving Software Quality and Efficiency

AI code reviewers blend the power of code analysis, NLP, and continuous learning to provide comprehensive, context-aware assessments of code quality.

Code reviews have long been a critical practice in software development. They serve as a quality control mechanism, ensuring that code is not only functional but also maintainable, secure, and efficient. However, traditional manual code reviews come with their own set of challenges, such as time consumption and the potential for human error. This is where AI code reviews come in. 

In recent years, the rise of artificial intelligence (AI) has ushered in a new era of code reviews, with AI-driven tools and processes offering the promise of improved software quality and efficiency. In this blog, we’ll explore the significance of code reviews, the emergence of AI in this domain, and how AI-enhanced code reviews can revolutionize software development.

Why are code reviews important?

Code reviews are really necessary for keeping software quality. They involve developers examining code to identify and rectify issues before they can impact the final product. Here’s why they matter:

  1. Quality Assurance: Code reviews act as a software quality assurance checkpoint, catching bugs and defects before they reach production. This ensures a more reliable and stable software application.
  2. Knowledge Sharing: They promote knowledge sharing among team members, allowing developers to learn from each other’s coding styles and best practices.
  3. Maintainability: Code that passes through thorough reviews tends to be more maintainable, reducing technical debt and making future updates and enhancements easier.
  4. Security: Code reviews help in identifying security vulnerabilities, a critical concern in today’s interconnected world.
  5. Consistency: They enforce coding standards and maintain code consistency across a project, enhancing readability and collaboration.

Traditional code reviews, while effective, can be time-consuming and resource-intensive.

This is why, AI code reviews

AI is playing an increasingly prominent role in code reviews, delivering several critical advantages:

  1. Speed and Scalability: AI-powered code review tools possess the remarkable ability to analyze code at an unprecedented pace. This rapid processing significantly shortens review times, facilitating quicker software development cycles and expediting time-to-market. In a landscape where software delivery speed is paramount, AI’s speed and scalability offer a distinct competitive edge.
  2. Consistency: Unlike human reviewers who can experience fatigue and distractions, AI remains tirelessly consistent. It maintains unwavering attention to detail, regardless of the code’s complexity or duration of review. This unwavering consistency is especially beneficial for globally distributed development teams, ensuring continuous, high-quality reviews around the clock.
  3. Pattern Recognition: AI excels in recognizing intricate patterns and uncovering anomalies within code that human reviewers might overlook. This pattern recognition capability is invaluable for detecting subtle issues and identifying potential vulnerabilities. With each review, AI refines its pattern recognition skills, further enhancing the accuracy and depth of code analysis.
  4. Data-Driven Insights: AI-equipped code review tools provide data-driven insights into code quality. They monitor code metrics, such as complexity and adherence to coding standards, across the entire codebase. These insights empower teams to identify trends, prioritize areas for improvement, and make informed decisions. Additionally, AI offers actionable recommendations based on historical data and best practices, guiding developers to write high-quality code from the outset.
  5. Reduced Bias: AI code reviewers operate without human biases, ensuring a more objective assessment of code quality. This impartiality can lead to fairer evaluations and less friction among development teams.
  6. Language Agnostic: AI can analyze code written in various programming languages, making it a versatile solution suitable for diverse development environments.

AI’s integration into code reviews represents a fundamental transformation in how software development teams operate. It not only automates and expedites the review process but also brings a level of consistency, pattern recognition, and data-driven decision-making that significantly enhances code quality and development efficiency. 

How do AI code reviewers work?

How do AI code reviewers work?

How do AI code reviewers work?

Understanding the inner workings of AI code reviewers reveals the power and precision of these tools. They employ a combination of advanced techniques, primarily centered around machine learning and natural language processing (NLP):

  1. Code Analysis

    AI code reviewers begin by scanning the source code thoroughly. This process involves identifying and flagging various aspects, including:
    • Syntax Errors: AI checks for violations of the programming language’s syntax rules, ensuring that the code is structurally sound.
    • Code Style Violations: They analyze the code against coding standards and guidelines, highlighting deviations in coding style.
    • Potential Bugs: AI utilizes its knowledge of common coding errors and bug patterns to identify potential issues. This proactive approach helps catch bugs early in the development process.

  2. Natural Language Understanding (NLP)

    In addition to code analysis, AI code reviewers incorporate NLP techniques to comprehend the context and intent behind code changes:
    • Comments and Documentation: NLP enables AI to understand comments, documentation, and commit messages associated with code changes. This contextual awareness helps AI reviewers make more informed assessments of code quality.
    • Semantic Analysis: NLP can perform semantic analysis of code comments, extracting meaningful information and identifying connections between code and comments. This aids in identifying discrepancies or misalignments.

  3. Learning from Data

    AI code reviewers continuously learn and evolve from historical code reviews and codebases. This learning process is fundamental to their ability to identify issues and provide recommendations:
    • Historical Data: AI draws insights from past code reviews, code repositories, and the collective knowledge of developers. This historical context helps AI reviewers become more effective over time.
    • Adaptive Recommendations: AI adapts its recommendations based on historical data. If certain types of issues have been addressed in specific ways in the past, AI can provide tailored guidance to developers.

  4. Auto-Correction (Advanced Feature)

    Some advanced AI code reviewers have the capability to go beyond flagging issues; they can suggest or automatically implement code fixes. This feature streamlines the development process, as developers can choose to accept or modify AI-generated fixes, significantly reducing manual intervention.

  5. Language Agnosticism

    AI code reviewers are designed to work across multiple programming languages, making them versatile and adaptable to diverse development environments.

AI code reviewers blend the power of code analysis, natural language understanding, and continuous learning to provide comprehensive, context-aware assessments of code quality. Their ability to identify errors, enforce coding standards, and even suggest fixes contributes to improved software quality and development efficiency. 

Benefits of AI code reviews

Benefits of AI code review

Benefits of AI code reviews

AI code reviews come with a plethora of benefits that have a profound impact on software development:

  1. Enhanced Error Detection: AI is highly proficient at identifying common coding errors, such as null pointer exceptions, memory leaks, and boundary condition problems. Its ability to catch these errors early reduces the likelihood of defects reaching production, resulting in more reliable software.
  2. Comprehensive Security: AI code reviewers excel at detecting security vulnerabilities, including potential entry points for cyberattacks. They can recommend patches and coding practices that bolster the software’s resilience against security threats, helping protect sensitive data and user privacy.
  3. Efficient Resource Utilization: By automating routine code reviews, AI frees up valuable developer time. Developers can redirect their efforts toward more complex, creative, and strategic tasks, such as architectural design and innovation.
  4. Scalability Without Resource Expansion: AI-powered code reviews are highly scalable. As project sizes grow, teams can handle the increased workload without the need for proportional expansions of human resources. This scalability is particularly advantageous for organizations with fluctuating development demands.
  5. Consistent Code Quality: AI maintains a consistent standard of code quality throughout a project. It enforces coding standards, best practices, and company-specific guidelines consistently, promoting uniformity in coding style and practices across the entire development team.
  6. Reduction in False Positives: AI code reviewers, when properly tuned, can significantly reduce the occurrence of false positives compared to manual reviews. This means developers spend less time investigating and addressing issues that aren’t actual problems, boosting productivity.
  7. Increased Code Review Coverage: AI can efficiently analyze and review a higher percentage of the codebase, including areas that might be overlooked in manual reviews. This extended coverage reduces the risk of undiscovered issues surfacing later in development.
  8. Faster Time-to-Market: The speed at which AI conducts code reviews accelerates software development cycles, leading to quicker time-to-market for software products. This agility is a competitive advantage in rapidly evolving industries.
  9. Enhanced Collaboration: AI code reviewers provide objective assessments, reducing subjective biases that can sometimes emerge in human code reviews. This fosters a more collaborative and constructive atmosphere within development teams.
  10. Continuous Improvement: AI-driven code review tools learn and adapt from each review. They gain insights from historical data and developers’ actions, improving their ability to identify issues and provide recommendations over time. This continuous learning benefits code quality and development efficiency.

AI code reviews offer a multitude of benefits that span error reduction, security enhancement, resource efficiency, scalability, consistency, and more. These advantages collectively contribute to the improved quality of code, shorter development cycles, and ultimately, a more competitive and agile software development process.

What are the challenges with AI code reviews?

What are the challenges with AI code reviews?

What are the challenges with AI code reviews?

While AI brings significant advantages to code reviews, it is not without its challenges and considerations:

  1. False Positives and Negatives: AI code reviewers may occasionally generate false positives by flagging issues that are not actual problems, or false negatives by missing real issues. This necessitates human intervention to validate and fine-tune AI recommendations. Striking the right balance between minimizing false alerts and capturing genuine issues is an ongoing challenge.
  2. Learning Curve: Implementing AI code reviews introduces a learning curve for development teams. They must adapt to new tools, processes, and workflows. This transition can require time and effort, potentially affecting productivity in the short term. Providing comprehensive training and support can help mitigate this challenge.
  3. Human Expertise: While AI is a valuable tool for automating code reviews, human expertise remains essential for making nuanced decisions. Developers bring domain-specific knowledge and contextual understanding that AI may lack. Project-specific requirements and business logic often demand human judgment for optimal decision-making.
  4. Over-Reliance on AI: Over-reliance on AI can be a concern. Teams may become complacent in performing manual code reviews, assuming that AI will catch all issues. This can lead to the neglect of critical aspects, particularly subtle or context-specific problems that require human judgment. Striking a balance between automated and manual reviews is crucial to maintain code quality.
  5. Privacy and Data Security: AI code reviewers analyze code, which may contain sensitive information or intellectual property. Ensuring the privacy and security of code repositories and review data is paramount. Implementing robust data protection measures and compliance with data regulations are essential considerations.
  6. Customization and Tuning: AI code reviewers often require customization and tuning to align with specific project requirements and coding standards. Teams must invest time in configuring AI tools to deliver optimal results. Regular adjustments may be necessary to adapt to evolving coding practices.
  7. Maintenance and Updates: AI models and tools require ongoing maintenance and updates to remain effective. Staying current with the latest AI advancements and ensuring that AI code reviewers evolve alongside changing coding practices is crucial.
  8. Ethical Considerations: AI code reviewers should be designed and used ethically. Developers and organizations must consider biases in training data and ensure that AI reviews adhere to ethical coding standards.

While AI significantly enhances code reviews, addressing challenges such as false alerts, learning curves, and over-reliance is crucial for its effective implementation. Organizations should approach the adoption of AI in code reviews thoughtfully, considering the specific needs and dynamics of their development teams and projects. Striking a balance between AI automation and human expertise is key to optimizing code quality and development efficiency.

Real-life use cases of AI code reviews

AI-driven code reviews have gained prominence in various industries and are being utilized by leading tech companies to enhance code quality and development efficiency:

  1. GitHub’s CodeQL

    GitHub, one of the world’s largest code hosting platforms, leverages CodeQL, an AI-powered static analysis tool. CodeQL’s sophisticated AI algorithms automatically identify security vulnerabilities in code. It doesn’t stop at detection; it also suggests fixes and patches. This AI-driven approach helps protect millions of open-source projects hosted on GitHub by proactively addressing security concerns. By finding and fixing vulnerabilities early in the development process, CodeQL contributes significantly to the overall security of the software ecosystem.

  2. Facebook Infer

    Facebook employs Infer, an AI-based code analysis tool, to enhance software reliability and prevent issues from reaching the production codebase. Infer uses static analysis to identify a wide range of programming errors and potential crashes, even in complex and large-scale codebases. By catching bugs and issues before they propagate, Infer helps Facebook maintain the high quality and stability of its applications while reducing costly post-release bug fixes.

  3. Google’s DeepCode

    DeepCode, developed by Google, is an AI-driven code review tool that goes beyond error detection. It provides intelligent suggestions for code improvements, offering specific recommendations to developers. By analyzing code patterns, coding styles, and best practices, DeepCode assists developers in writing cleaner, more efficient code. This not only reduces the likelihood of errors but also accelerates development by automating code enhancements. DeepCode is particularly valuable for optimizing development workflows and reducing coding errors, ultimately saving time and resources.

  4. Uber’s Aibolit

    Uber has developed its AI-based code analysis tool called Aibolit. Aibolit is designed to identify code smells, which are indications of potential issues in code quality. It helps Uber’s developers maintain codebases that are clean and efficient. Aibolit assists in ensuring code adherence to the company’s coding standards and best practices, ultimately contributing to a smoother development process and improved code maintainability.

  5. Microsoft’s IntelliCode

    IntelliCode, developed by Microsoft, enhances the code review process by providing AI-generated code completion suggestions and recommendations. By analyzing coding patterns and contextual information, IntelliCode assists developers in writing code faster and with fewer errors. This AI-powered tool integrates seamlessly with popular development environments, such as Visual Studio, improving productivity and reducing coding inconsistencies.

Conclusion

In the world of software development, where code quality directly impacts the success of a project, AI code reviews offer a powerful solution. They combine speed, consistency, and error detection capabilities that surpass human capabilities. While challenges remain, the benefits of integrating AI into your development workflow are undeniable. Embracing AI code reviews can significantly improve software quality and efficiency, ensuring that your projects meet the highest standards.

FAQs

    1. What is the AI code reviewer in GitHub?

      GitHub’s AI code reviewer, known as CodeQL, is a powerful tool that automatically scans code for security vulnerabilities. It not only identifies issues but also suggests fixes, helping developers enhance the security of open-source projects hosted on GitHub.

    2. Can AI be used for code review?

      Yes, AI can be employed for code reviews. AI-driven code review tools analyze code for errors, style violations, and security vulnerabilities, significantly improving code quality and development efficiency.

    3. Will AI code review process replace developers?

      No, AI code review processes will not replace developers. While AI enhances code reviews and automates certain tasks, human expertise, creativity, and decision-making remain essential in software development. AI is a valuable tool that complements developer skills but does not replace them.

    4. What is AI code reviewer?

      An AI code reviewer is a software tool that uses artificial intelligence and machine learning techniques to analyze and review source code. It scans for errors, style violations, security vulnerabilities, and more, providing recommendations to improve code quality and efficiency. AI code reviewers are used to enhance the code review process in software development.

    5. Can we use AI for code?

      Yes, AI is widely used in coding. It aids developers by generating code snippets, providing real-time code completion suggestions, and analyzing code for errors and vulnerabilities. AI-driven testing tools automate test case generation and execution, enhancing code quality and efficiency. While AI supports coding tasks, it complements, rather than replaces, human developers who bring creativity and problem-solving skills to software development.

    6. Is code review automation possible?

      Yes, code review automation is possible and increasingly common. AI-powered tools can automate the code review process by analyzing code for errors, style violations, and security vulnerabilities. They provide detailed feedback and recommendations, significantly speeding up the review process and improving code quality. However, human oversight and expertise remain valuable for addressing nuanced issues and making context-specific decisions.

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By ● Oct 1, 2023
AI for recruiting: Procure the Right People Trusting AI Vetting for Software Engineers
AI Services ● Custom Engineering ● For Employers ● Vetted Talent ● Vetting and Hiring

Procure the Right People: Trusting AI Vetting for Software Engineers

Procure the right people with Turing’s AI-powered vetting process that uses a structured approach to source, vet, and manage world-class developers.

Great employees are the foundation of successful companies. But great talent takes time to come by. And this is where AI for recruiting comes in. McKinsey’s Steven Hankin coined the term “war for talent” in 1997, and it continues to be true today. The competition has only gotten fiercer over the years, and while the recessionary conditions over the last year may suggest a surplus of talent in the market, getting the right talent is still difficult. A survey by Manpower Group* reveals that in 2023 nearly 4 out of 5 employers faced difficulty in finding the right talent. So how can AI for recruiting be beneficial? Let’s find out.

Reasons why hiring the right talent is hard

Today, hiring the right talent has become more difficult than ever. Here are a few reasons why: 

  1. Unhelpful resumes: Resumes often lack comprehensive or accurate information about a candidate’s technical and soft skills. Given these inconsistencies, hiring managers and talent specialists end up wasting precious hours going over hundreds (sometimes even thousands) of resumes. 
  2. Incorrect job descriptions: A job description is an important tool in finding and onboarding the right talent. Poorly translated job descriptions can create disconnects and inefficiencies in the hiring process, as the actual job requirements may differ from what hiring managers have in mind.
  3. Inefficient assessment process: Relying solely on unstructured interviews can result in random outcomes and wasted time for hiring managers.
  4. High competition: Intense competition for skilled professionals can make it challenging to attract and retain top talent.
  5. Cultural mismatch: Finding candidates who not only possess the required skills but also align with the company’s culture and values can be a significant challenge.
  6. Skill shortages: Shortages of qualified candidates in certain industries or roles further complicate the hiring process.
  7. Global sourcing challenges: For companies looking to hire talent from around the world, navigating different labor laws, immigration processes, and cultural nuances can add complexity to the hiring process.
  8. Inconsistent candidate experience: Negative experiences during the recruitment process can deter top talent from considering a company. Ensuring a positive candidate experience is essential to attracting and retaining the right candidates.
  9. High cost of hiring: The recruitment process can be expensive, from advertising job openings to conducting interviews and assessments. Managing these costs of hiring while finding the right talent is a delicate balance.
  10. Uncertain market conditions: Economic conditions, industry trends, and geopolitical factors can influence the availability of talent. Adapting to these external factors is an ongoing challenge for HR and talent acquisition teams.

What is AI for recruiting?

AI for recruiting refers to the use of artificial intelligence (AI) and machine learning (ML) technologies to enhance and streamline various aspects of the hiring and talent acquisition process. This includes automating tasks like resume screening, sourcing candidates, assessing qualifications, and even predicting candidate-job fit. AI in recruiting aims to make the process more efficient, data-driven, and fair by reducing human biases and optimizing candidate-job matches.

One of the primary benefits of AI in recruiting is its capacity to automate time-consuming administrative tasks. For instance, AI-powered systems can sift through vast numbers of resumes, screening candidates based on predefined criteria, and identifying the most promising individuals. This not only saves valuable time but also ensures a more objective assessment, reducing the risk of bias in the early stages of recruitment.

Additionally, AI can significantly improve the accuracy of candidate-job matching. By analyzing vast datasets, AI can identify patterns and correlations that may not be apparent to human recruiters. This results in a better fit between candidates and positions, ultimately leading to more successful hires and reduced turnover rates.

Let’s take a look at the major benefits of leveraging AI for recruiting. 

Benefits of AI for recruiting: How can AI improve the recruiting process?

AI for recruiting: What are the benefits of using AI for recruiting

What are the benefits of using AI for recruiting

Using AI for recruiting offers several benefits that can significantly improve the efficiency and effectiveness of the recruitment process. Here are some key advantages:

  1. Efficiency and time savings

    AI can automate various aspects of the hiring process, such as resume screening, candidate sourcing, and initial assessments. This reduces the time and effort spent by HR teams and hiring managers on administrative tasks, allowing them to focus on strategic aspects of recruitment.

  2. Improved candidate matching

    AI algorithms analyze a candidate’s qualifications, skills, and experience to match them with the most suitable job openings. This leads to more accurate candidate-job fit, reducing the likelihood of mismatches and improving overall hiring quality.

  3. Bias reduction

    AI can help minimize unconscious bias in the hiring process. By relying on objective data and criteria, AI-driven systems are less prone to biases related to gender, race, or other factors that can affect human decision-making.

  4. Cost reduction

    Automating parts of the hiring process can result in cost savings. Companies can reduce expenses associated with job postings, manual resume screening, and lengthy interview processes.

  5. Data-driven decision-making

    AI tools generate data and analytics that provide insights into the effectiveness of recruitment strategies. This data can help organizations make informed decisions about their hiring processes and optimize them over time.

  6. Enhanced candidate experience

    AI-driven chatbots and automated communication can provide candidates with timely updates, answer their questions, and offer a smoother application and interview experience, enhancing the overall candidate experience.

  7. Predictive analytics

    AI can predict a candidate’s likelihood of success in a role based on their skills, qualifications, and historical data. This helps organizations identify candidates who are more likely to perform well in the long term.

  8. Scalability

    AI can handle large volumes of candidate data and job openings simultaneously, making it easier for companies to scale their recruitment efforts when needed, such as during periods of rapid growth.

  9. Continuous learning

    AI systems can continuously learn and adapt to changing job requirements and candidate preferences. This adaptability ensures that the recruitment process remains up-to-date and effective.

  10. Global talent pool access

    AI-powered platforms can source candidates from a global talent pool, providing access to a diverse range of candidates with different skills and backgrounds.

  11. Reduction in human error

    Automation reduces the risk of human error in tasks like resume screening and data entry, leading to more accurate and consistent evaluations of candidates.

  12. Faster time-to-fill

    AI can identify qualified candidates more quickly, reducing the time it takes to fill job openings. This is particularly valuable for roles that require immediate staffing.

What are the challenges companies can face when adopting AI for recruiting?

AI for recruiting: What are the challenges companies can face when adopting AI for recruiting

What are the challenges companies can face when adopting AI for recruiting

While AI comes with a plethora of benefits if used for hiring, there are some challenges that companies might come across while using AI for recruiting. Here are the major challenges:

  1. Avoiding bias and being fair

    One big challenge is making sure that AI doesn’t make unfair decisions. Sometimes, AI can pick up biases from old data, which can lead to unfair hiring choices. It’s crucial to use AI in a way that’s fair to everyone.

  2. Getting good data

    AI needs good information to make smart decisions. Sometimes, companies struggle to find and use the right data. If the data is messy or not diverse enough, it can make AI hiring less effective.

  3. Being clear and honest

    AI can seem like a bit of a mystery. It’s not always easy to explain why AI picked one person over another for a job. Being clear and honest about how AI works is important to build trust.

  4. Following the rules

    Laws about hiring are pretty complicated. Companies need to make sure that AI hiring practices follow all the laws. These laws can be different depending on where you are, so it can be a challenge to keep up.

  5. Keeping candidates happy

    While AI can help speed up the hiring process, it shouldn’t make things worse for job applicants. Making sure that candidates have a good experience during the hiring process, even with AI involved, is important for hiring top talent.

How AI will change the recruiter role?

AI is poised to revolutionize the recruiter role in two key ways. Firstly, it will automate repetitive tasks like job description writing and interview scheduling, allowing recruiters to focus on relationship-building and candidate engagement. While AI can assist in outreach, recruiters’ human touch remains vital for building connections.

Secondly, AI will drive a shift towards skills-first hiring, reducing human bias. Recruiters will rely on AI to assess candidates based on skills and experience, fostering diversity and equity. As AI tools become integral, recruiters will need to emphasize soft skills like negotiation and storytelling. These skills will be essential in providing a personalized and efficient hiring process, ensuring candidates are a cultural fit and possess potential.

AI will transform recruiters into strategic partners who leverage automation for efficiency and equity while emphasizing essential human skills to engage candidates effectively, creating a more streamlined and inclusive recruitment process.

What are some myths about AI in recruiting?

AI for recruiting: What are some myths about AI in recruiting

What are some myths about AI in recruiting

There are several myths surrounding the use of AI in recruiting that can sometimes mislead businesses. Let’s debunk these misconceptions:

  1. AI replaces humans: Contrary to the belief that AI replaces human recruiters entirely, it primarily enhances efficiency in certain parts of the recruiting process, like resume screening and skill assessment, while human expertise remains invaluable in relationship-building and understanding nuanced candidate needs.
  2. AI simplifies candidate search: While AI provides valuable insights into the hiring process, it doesn’t necessarily make candidate sourcing easier. It can streamline certain aspects, but identifying the right talent still requires human judgment and context.
  3. AI eliminates jobs: AI tends to create more opportunities by generating high-level roles for those who can effectively utilize these tools. It complements human efforts rather than replacing them, leading to the emergence of new job categories.
  4. AI is only for large companies: AI is accessible to businesses of all sizes. It can benefit small and medium-sized enterprises (SMEs) just as effectively as larger corporations by improving recruitment processes and reducing operational risks.
  5. AI recruiting is expensive and complex: AI solutions vary in cost and complexity, with many offering free trials. These technologies can help analyze data and make faster, more informed decisions without requiring a substantial financial commitment.
  6. AI is only good for simple tasks: AI has evolved far beyond simple tasks and can excel in complex and specialized domains, from healthcare diagnostics to creative content generation.
  7. AI is just an algorithm: AI encompasses a broad spectrum of capabilities, and recruiting AI is a specialized application designed to automate and enhance recruitment processes through data analysis and predictive modeling.
  8. You can teach AI anything: Training AI requires time, effort, and continuous monitoring to ensure it understands and adapts to changing requirements.

Is there a solution?

Turing experts believe that having a system that collects proper representation and information on the following three fronts can help find the right candidate.

  1. Accurate qualifications and competencies of job seekers
  2. Actual job requirements aligned with reasonable expectations
  3. Valid and reliable assessment of the fit between the job and the candidate

A limited or regional talent pool can increase the difficulty of finding the right talent in a fast and cost-efficient manner. This is why Turing built an AI-powered platform that uses 20,000+ ML data signals to source, vet, and manage world-class developers. Following a structured approach, AI supports Turing’s vetting process and is Turing’s solution to the hiring conundrum.

Transforming Hiring with Turing’s Comprehensive AI Vetting Process

In the dynamic world of tech recruitment, Turing stands at the forefront with its innovative AI-based vetting system. With a global talent pool exceeding 3 million software professionals, Turing offers career growth opportunities for developers and the assurance of being vetted once for a lifetime of prospects.  In this video, Turing’s CEO Jonathan Siddharth explains how Turing uses AI to evaluate developers. 

Progressive Assessment Tailored to the Job

Turing’s AI vetting process revolves around two key vectors: role types and dominant tech stacks. This tailored approach ensures that candidates are assessed based on the specific skills required for their roles, whether they’re Front-end Engineers or Back-end Engineers, specializing in React, Node, Python, Java, and more.

Stage 1: Fundamental Knowledge Evaluation

The journey begins with a deep dive into candidates’ fundamental knowledge. Turing’s AI system conducts automated knowledge tests, examines code blocks, and tests language or technology concepts. Dynamic scoring based on question difficulty enhances candidate quality assessment. With over 150 automated tests catering to 10+ roles, foundational skills are thoroughly scrutinized.

Stage 2: Coding Proficiency Assessment

Successful candidates progress to Stage 2, where they face coding challenges aligned with their tech stack. These challenges assess algorithmic prowess, data structure understanding, and coding best practices, emphasizing efficiency and performance. Performance metrics, including testing, debugging, code deployment, and API interaction, ensure candidates possess practical skills.

Stage 3: Soft Skills Evaluation

Recognizing the importance of soft skills, Turing’s AI evaluates candidates’ core values, communication, collaboration, and remote work adaptability in Stage 3. This holistic approach ensures candidates meet the technical and interpersonal requirements for the job.

Stage 4: Perfect Match Assurance

In the final stage, Turing’s AI system meticulously matches job requirements with candidate skills, ensuring a harmonious fit between abilities and job demands.

Turing’s four-stage assessment process, encompassing technical proficiency, soft skills, role-specific knowledge, and job compatibility, revolutionizes hiring. With a commitment to excellence and a 97% retention rate, Turing has earned the trust of 900+ companies, including industry giants like Pepsi, Disney, and Dell. Transform your hiring experience with Turing’s AI-powered Talent Cloud and reap the benefits of precision and efficiency in talent acquisition.

FAQs related to AI for recruiting

  1. What is AI recruiting?
    AI recruiting involves using artificial intelligence to assist in various stages of the hiring process, from sourcing and screening candidates to assessing their qualifications and predicting job fit.

  2. What is the role of AI in hiring process?
    The role of AI in hiring includes automating repetitive tasks, enhancing candidate matching, reducing bias, providing data-driven insights, and improving the efficiency and effectiveness of recruitment.

  3. Can AI replace humans in recruiting process?
    While AI can automate many aspects of recruiting, it cannot fully replace humans. Human judgment, communication, and empathy remain essential in evaluating soft skills and cultural fit.

  4. How AI is changing the hiring process?
    AI is changing the hiring process by streamlining tasks, reducing bias, enabling data-driven decisions, and enhancing the candidate experience, ultimately making recruitment more efficient and fair.

  5. What are the challenges of using AI in hiring process?
    Challenges of using AI in hiring include potential bias in algorithms, data quality issues, transparency concerns, legal compliance, and ensuring a positive candidate experience.

  6. What are the benefits of AI powered recruitment?
    Benefits of AI-powered recruitment include efficiency gains, better candidate matching, reduced bias, cost savings, faster time-to-fill positions, predictive analytics, and improved overall hiring quality.

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By ● Sep 18, 2023
5 Key Considerations for Building an AI Implementation Strategy
AI Services ● Custom Engineering ● For Employers ● Turing Services

5 Key Considerations for Building an AI Implementation Strategy

AI implementation strategy building tips: 1. Define the problem the AI will solve 2. Ensure the data you feed the AI is of good quality 3. Choose the right AI

Artificial intelligence (AI) has been widely adopted across industries to improve efficiency, accuracy, and decision-making capabilities. As the AI market continues to evolve, organizations are becoming more skilled in implementing AI strategies in businesses and day-to-day operations. This has led to an increase in full-scale deployment of various AI technologies, with high-performing organizations reporting remarkable outcomes. These outcomes go beyond cost reduction and include significant revenue generation, new market entries, and product innovation. However, implementing AI is not an easy task, and organizations must have a well-defined strategy to ensure success. We’ll be taking a look at how companies can create an AI implementation strategy, what are the key considerations, why adopting AI is essential, and much more in this article.

5 key considerations for building an AI implementation strategy

5 key considerations for building an AI implementation strategy

5 key considerations for building an AI implementation strategy

Let’s discuss the five key considerations for building an AI implementation strategy.

  1. Problem definition

    Defining the problem that the AI system will solve is crucial. It is essential to identify the business objective and the specific task that the AI system will perform. Organizations must also decide on the metrics used to evaluate the performance of the AI system before jumping into the actual implementation of AI. For instance, if an organization is building an AI system to classify images of animals, it must define the types of animals it wants to classify, the accuracy rate it wants to achieve, and the evaluation metrics it will use, such as precision, recall, and F1 score. Identifying or establishing baselines and benchmarks is also key to evaluating the effectiveness of AI solutions.

  2. Data quality

    The foundation of any AI system is only as good as the data it is trained on. The data is just as important as the AI technology itself because AI builds upon the data. If data is not correct, precise, or relevant, then the AI will make decisions that may not be accurate. Data must be accurate, relevant, and consistent to produce reliable results.

    Before diving headfirst into creating an AI model, organizations must assess their data quality and take steps to improve it if necessary. Data cleaning and preprocessing techniques can be applied to eliminate errors, inconsistencies, and duplicate records. Additionally, organizations must ensure that their data is representative of the real-world scenario they are trying to model. For instance, if an organization is implementing AI in business to predict customer churn, it must have data that represents different types of customers and their behavior. In some cases, there is not enough data to train an AI model, forcing businesses to generate synthetic data sources.

  3. Model selection

    Choosing the right model that best fits the project requirement is one of the most crucial factors that an organization, no matter what size, must consider when creating an AI implementation strategy. Different AI models have different strengths and weaknesses, and organizations must choose the one that best fits their requirements. There are several factors to consider when selecting an AI model, such as the type of data, the complexity of the problem, the availability of labeled data, and the computational resources required. For instance, if an organization has a large dataset and wants to classify text, it can consider using a large language model to create vector representations of the text and feed them to smaller classifier models like random forests, support vector machines, or small neural networks.

  4. Integration with existing systems

    Another, often neglected factor in building an effective AI implementation strategy is integrating an AI system with existing systems. This is a complex process that requires careful planning, no doubt. The AI system needs to be consistently integrated into the broader system, meaning the predictions should be used in the right place with confidence. Additionally, organizations must consider the impact of the AI system on the workflows and processes already in place—it must be integrated in a way that minimizes disruption and enhances productivity.

    For instance, if an organization is implementing an AI system to automate customer service, it must ensure that the system integrates with the existing customer service platform and that the customer service representatives are trained to use the system. This will improve productivity and significantly help manage the overall cost of implementing artificial intelligence. Additionally, it will help minimize external upkeep and expenses that could otherwise be used for the improvement of existing systems.

  5. Ethical considerations

    It’s finally time to discuss the elephant in the room: the concept of handing over crucial factors to super-intelligent machines can make people uneasy. Organizations must consider the ethical implications of implementing AI in business and ensure that the system is fair, transparent, and unbiased. Additionally, organizations must consider the potential impact of the AI system on society and the environment. For instance, if an organization is building an AI system to make hiring decisions, it must ensure that the system is not biased against certain groups of people and that the decisions are transparent.

Why should companies adopt AI?

Why should companies adopt AI?

Why should companies adopt AI?

The adoption of AI is not a mere technological upgrade but rather a strategic upgrade that can help companies move forward at a much better pace. AI delivers tangible benefits, including improved efficiency, data-driven decision-making, revenue growth, etc. Let’s explore the benefits of AI

  1. Increased efficiency and productivity

    AI technologies play a pivotal role in enhancing efficiency and productivity across industries. By automating repetitive and time-consuming tasks, AI allows employees to focus on more strategic and creative endeavors. For instance, in customer service, AI-driven chatbots and virtual assistants can handle inquiries round-the-clock, providing instant responses and freeing up human agents to tackle more complex issues. This not only reduces operational costs but also ensures a seamless and responsive customer experience, ultimately improving overall efficiency.

  2. Enhanced decision-making

    The power of AI lies in its ability to process vast amounts of data quickly and accurately. AI algorithms analyze this data to provide actionable insights, enabling organizations to make informed, data-driven decisions. Predictive analytics, for instance, can forecast market trends and customer behavior, giving businesses the edge in adapting to changing market dynamics. With AI support, decision-makers can optimize resource allocation, refine strategies, and navigate uncertain waters with confidence, resulting in better decision-making across the board.

  3. Revenue growth and market expansion

    AI is a potent driver of revenue growth and market expansion. Personalization powered by AI algorithms tailors product recommendations and marketing campaigns to individual preferences. This results in increased sales and higher customer engagement. Moreover, AI’s capacity for market segmentation and customer behavior analysis enables organizations to identify unexplored market opportunities and niche segments. Armed with these insights, businesses can successfully enter new markets and expand their offerings, further driving revenue and market share.

  4. Improved customer experience

    AI revolutionizes the customer experience by delivering tailored solutions and prompt support. Personalization is key, as AI analyzes customer data to recommend products and services that align with individual preferences. Virtual customer service agents, powered by AI, offer round-the-clock assistance, swiftly addressing customer inquiries and resolving issues. These enhancements not only enhance customer satisfaction but also foster customer loyalty, as clients appreciate the personalized and efficient services AI brings to the table.

  5. Competitive advantage and innovation

    Early adopters of AI gain a substantial competitive advantage. By leveraging AI for operational optimization, market trend anticipation, and rapid response to customer needs, businesses can outpace competitors. AI’s capacity to identify new product ideas, streamline research and development processes, and enhance product quality through predictive maintenance fosters innovation. This continuous cycle of improvement not only keeps organizations ahead of the curve but also ensures they remain adaptable and innovative in the ever-evolving business landscape.

AI implementation strategy: How can companies select the right AI model? 

How can companies select the right AI model?

How can companies select the right AI model?

Selecting the right AI model is a crucial part of your AI implementation strategy. Here are the factors that you should consider while selecting the right AI model for your company:

  1. Data type
    • Different AI models excel at handling specific types of data, such as images, text, or time-series information.
    • Identify the nature of your data to determine which model is most suitable for your project.
  2. Problem complexity
    • Evaluate the complexity of the problem you’re trying to solve. Some tasks may be well-suited to pre-trained models, while others require custom-built solutions.
    • Tailor your choice to match the intricacy of your specific problem.
  3. Labeled data availability
    • Deep learning models often require a substantial amount of labeled data for effective training.
    • Assess the availability of labeled data for your project and consider techniques like transfer learning if data is limited.
  4. Computational resources
    • Consider the computational resources available to your organization. Large models like GPT-3 demand significant computing power, which may not be feasible for all companies.
    • Ensure that your infrastructure can support the computational requirements of the chosen model.
  5. Interpretability needs
    • Think about the level of interpretability required for your model, especially in domains like healthcare or finance where transparency is crucial for regulatory compliance.
    • Choose models that align with your interpretability needs and provide the necessary level of transparency.
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Selecting the right AI model involves assessing your data type, problem complexity, data availability, computational resources, and the need for model interpretability. By carefully considering these factors, companies can make well-informed decisions that set their AI projects on a path to success.

Also, read ChatGPT vs Software Developers: Is Generative AI the End of the Road for Developers?

What should be the AI implementation plan?

To successfully implement AI in your business, begin by defining clear objectives aligned with your strategic goals. Identify the specific challenges AI can address, such as enhancing customer experiences or optimizing supply chain management.

Next, assess your data quality and availability, as AI relies on robust data. Ensure your data is accurate, relevant, and comprehensive. If necessary, invest in data cleaning and preprocessing to improve its quality.

Select the appropriate AI models that align with your objectives and data type. Train these models using your prepared data, and integrate them seamlessly into your existing systems and workflows.

Prioritize ethical considerations to ensure fairness, transparency, and unbiased AI systems. Thoroughly test and validate your AI models, and provide training for your staff to effectively use AI tools.

Plan for scalability and ongoing monitoring while staying compliant with data privacy regulations. Continuously measure ROI and the impact of AI on your business objectives, making necessary adjustments along the way.

Consider partnering with AI experts or service providers to streamline the implementation process. With a well-structured plan, AI can transform your business operations, decision-making, and customer experiences, driving growth and innovation.

Now you’re ready to create your own AI implementation strategy. What’s next?

Implementing AI is a complex process that requires careful planning and consideration. Organizations must ensure that their data is of high quality, define the problem they want to solve, select the right AI model, integrate the system with existing systems, and consider ethical implications. By considering these key factors, organizations can build a successful AI implementation strategy and reap the benefits of AI. 

That said, the implementation of AI in business can be a daunting task when done alone and without proper guidance. However, there’s a simple solution. Implementing AI in business can be simplified by partnering with a well-established, capable, and experienced partner like Turing AI Services.

Turing’s business is built by successfully deploying AI technologies into its platform. We have deployed search and recommendation algorithms at scale, large language model (LLM) systems, and natural language processing (NLP) technologies. This has enabled rapid scaling of the business and value creation for customers. We have leveraged this experience to help clients convert their data into business value across various industries and functional domains by deploying AI technologies around NLP, computer vision, and text processing. Our clients have realized the significant value in their supply chain management (SCM), pricing, product bundling, and development, personalization, and recommendations, among many others.

Turing’s AI Services: Case studies of clients who used our AI implementation strategy and scaled their business

Turing AI Services has a proven track record of delivering impactful solutions across a spectrum of industries. Here are three compelling case studies that illustrate our expertise and the tangible results achieved through our AI-powered solutions:

  1. Revolutionizing healthcare and surgical operations:
    • In this case, we deployed AI to enhance critical aspects of healthcare, including surgical operations and supply chain management.
    • Our unique AI models, tailored to specific use cases, improved efficiency and accuracy in operating rooms and ensured the availability of essential equipment.
    • The result: a reduction in materials waste, improved product recall efficiency, and enhanced customer satisfaction.
  2. Optimizing product pricing strategies:
    • Turing AI Services partnered with a client looking to gain a competitive edge in the market by optimizing product pricing.
    • We developed an AI pricing recommender that analyzed historical and competitive data to determine the best pricing strategies, maximizing profits.
    • The outcome was an increase in product and bundled product sales, providing the client with a significant competitive advantage.
  3. Advanced chatbot models and AI coding
    • The client sought high-quality, advanced-level programs for training interactive chatbot models and AI coding models to identify and correct coding errors.
    • Turing AI Services developed a methodology for training complex models efficiently, resulting in an over 80% increase in model efficiency and a 300% increase in throughput.
    • Our expertise in AI model training and deployment significantly reduced errors and improved operational efficiency.
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These case studies showcase how Turing AI Services leverages AI and machine learning expertise to address complex challenges across various industries, ultimately driving efficiency, profitability, and innovation for our clients.

FAQs related to AI implementation strategy

  1. How can AI be implemented into a business? How to incorporate AI into your business?

    AI can be implemented into a business by first defining the problem it aims to solve, assessing data quality, selecting the appropriate AI model, integrating it into existing systems, and considering ethical implications. This involves a strategic approach to align AI with business objectives and requirements.

  2. Why implement AI in business?

    Implementing AI in business offers increased efficiency, data-driven decision-making, revenue growth, improved customer experiences, and a competitive edge. It enhances operations, boosts innovation, and helps meet evolving customer demands.

  3. What are the benefits of implementing AI?

    The benefits of implementing AI include improved efficiency, enhanced decision-making, revenue growth, improved customer experiences, and competitive advantage. AI optimizes processes, provides actionable insights, and drives innovation.

  4. What are 4 advantages of AI?

    Four advantages of AI are automation of repetitive tasks, data-driven insights, enhanced personalization, and improved accuracy in decision-making. These advantages lead to increased productivity, better customer engagement, and cost savings.

  5. What is AI and how is it implemented?

    AI, or Artificial Intelligence, refers to the simulation of human-like intelligence in machines. It is implemented by defining specific tasks, collecting and processing relevant data, selecting appropriate AI models, and integrating them into systems. AI systems learn from data and make decisions or predictions to achieve predefined objectives.
Talk to Turing Experts

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By , ● Sep 13, 2023
Building New AI Solutions for Intelligent Enterprises and Developers
AI Services ● Custom Engineering ● For Employers ● Tech ● Tips, Tools, and Trends ● Turing Services

Building New AI Solutions for Intelligent Enterprises and Developers

At Turing, where AI and ML are at the core of our company’s DNA, we’ve been hard at work developing initiatives to support intelligent businesses and developers.

AI transformation is digital transformation. The current speed of AI innovation presents a turning point for companies and developers alike. Future success now hinges on new opportunities to galvanize businesses and engineers through shared AI tools and knowledge.

At Turing, where AI and machine learning are at the core of our company’s DNA, we’ve been hard at work developing new initiatives to support intelligent businesses and developers in this new era. Anyone can sign up for the latest updates as we launch these new initiatives later this year.

AI Transformation Accelerator

AI Transformation Accelerator

AI Transformation Accelerator

Unlock the full potential of your organization. This 4-week, timeboxed offering is designed to investigate and evaluate the potential impact of AI methodologies on your organization. 

We work with you to identify AI and data-driven solutions that can solve your business problems. We’ll help you establish an impactful and optimized path forward, supported by existing data, and create a roadmap for the implementation of AI solutions. This includes data collection and ingestion, AI/ML modeling methods, and an ROI framework. 

AI Advisory Services

AI Advisory Services

AI Advisory Services

Get extensive AI knowledge and experience to revolutionize your business. Trusted by hundreds of companies and VCs, Turing is offering AI advice from our experts. 

This free service empowers companies to realize the numerous benefits of AI, gain a solid understanding of its fundamentals and core technologies, and ultimately unlock the full potential of AI and machine learning. With AI Advisory, companies will gain access to Turing’s extensive knowledge and experience, enabling them to frame their AI strategy and stay ahead of the curve.

AI-Accelerated Software Development

AI-Accelerated Software Development

AI-Accelerated Software Development

Supercharge software development with our AI-enhanced development pilot program. By leveraging our cutting-edge AI models, Turing developers can build with unparalleled speed, efficiency, and accuracy for faster project delivery, cost savings, and a competitive edge for your business. 

Discover streamlined workflows, enhanced productivity, seamless integration, expert support, and continuous improvement from the leader in AI-driven development.

Tech Talk Series – How to Code with an AI Copilot

Tech Talk Series - How to Code with an AI Copilot (1)

Tech Talk Series – How to Code with an AI Copilot (1)

Become a 100x developer with cutting-edge AI training. Sharpen your skills to reshape software development.

The rise of AI presents the opportunity to augment your abilities and elevate your importance in an evolving tech landscape. 

Join us for a series of tech talks featuring global experts on AI-assisted development. This initiative aims to provide developers with the knowledge and tools necessary to stay at the forefront of innovation.

Don’t miss out on these breakthrough initiatives

Sign up for updates as we roll out each of these exciting opportunities for intelligent enterprise and developer transformation.

Already far along on your AI journey?

Turing’s integrated consulting and delivery solutions can help solve your complex business challenges. Our AI services are tailored to each client’s specific needs and include AI strategy development, solution design, and implementation. Learn more and talk to an expert at turing.com/services/ai.

Are you a seasoned AI developer or ML engineer?

Turing is home to some of the best minds and best career opportunities for people like you. Enjoy a remote career with some of the top companies in the world. Learn more and sign up at turing.com/jobs.

Talk to Turing Experts

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Introducing TuringGPT – Your Ultimate AI Solution

Discover TuringGPT, the innovative chat app by Turing. Enjoy free access* to the most advanced generative pre-train transformer (GPT) in the world. Boost your creativity, productivity, and efficiency with our user-friendly interface and extensive library of prompt templates.

Free GPT4 access*: Enjoy cutting-edge AI creativity at absolutely no cost.

Deep prompt library: Jump-start conversations with our ever-expanding template library.

Easy output customization: Effortlessly harness the power of AI, regardless of your skill level.

Unlock the power of TuringGPT today.

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By ● May 12, 2023
Turing.com: Artificial Intelligence in medical field
AI Services ● Custom Engineering ● For Employers ● Tech ● Tips, Tools, and Trends

Reshaping the Healthcare Industry with AI

This post explores the significant healthcare challenges and how Turing.com can help healthcare industry leaders thrive in a remote-first world.

The COVID19 pandemic has affected every industry differently, but given the nature of the crisis, it’s no surprise that it has impacted the healthcare industry the most.

This post explores the significant healthcare challenges and highlights what leaders in the healthcare industry need to focus on to thrive in a remote-first world. 

The pandemic’s impact on the healthcare industry

Turing.com: AI in medical field

Impact of pandemic on healthcare

Healthcare has borne the brunt of the impact, mainly because our medical systems were ill-equipped to deal with this crisis. 

While healthcare systems were not equipped or efficient enough to deal with this unpredictable health challenge, the industry’s response has proved its resilience through the swift innovations brought into the market.

However, the threat is far from over, and the medical sector’s innovation capabilities must continue to rise to the challenge.

Here are the short-term and long-term perspectives for how the healthcare industry can proactively tackle these challenges:

  1. From the short-term perspective, there’s a high demand for digital healthcare, connected ecosystem, data management, security, and patient engagement systems.
  2. From a long-term perspective, there’s going to be a massive shift with consumer-centric, wellness-oriented, and digitally connected healthcare systems. 

Opportunity areas for the healthcare industry

Turing.com: Artificial intelligence in medical field

Artificial intelligence in medical field

The emergence of digital health and remote solutions has opened up tremendous opportunities for healthcare leaders. However, while digital transformation has been growing steadily for decades, the COVID19 pandemic has accelerated its adoption rate. 

Artificial intelligence in medical field in the form of medical devices, telemedicine, and electronic healthcare records powered by blockchain are just a handful of examples of how technology and innovation have transformed the healthcare industry.

Digital transformation has been steadily reshaping how people interact with healthcare professionals. 

The rapid advancements in machine learning, data mining, and artificial intelligence in medical field have allowed experts to develop digital biomarkers to optimize the process for early diagnostics. 

The advancements in these technologies will also help swiftly spot unforeseen patterns within these complex datasets to build a better preventive-care system or detect severe medical conditions in their earliest stages.

Therefore, it should come across as no surprise that AI-powered diagnosis and point-of-care platforms are significant opportunities for healthcare leaders in the modern world. 

What are the challenges healthcare industry leaders face today?

Businesses in nearly every industry have witnessed job positions staying vacant for months due to a shortage of talented workers.

The US Bureau of Labor Statistics revealed that the global talent shortage led to over 40 million unfilled job positions worldwide. 

According to the estimates, this number will rise to over 82 million by the next decade.

A recent McKinsey Global Survey showed that nearly nine out of ten managers and executives believe their firms are either experiencing or will experience skill gaps in the next five years.

However, this global talent shortage is perhaps the most severe when it comes to software development.

Technological advancements have fueled the growing demand for engineering experts specialized in automation, programming languages, and other emerging technologies like blockchain, machine learning, big data, DevOps, augmented reality, and artificial intelligence.

With the demand for digital skillsets continuously growing in healthcare segments, the supply hasn’t kept pace with it. This massive imbalance of demand-supply of skilled software developers is one of the significant challenges healthcare industry leaders face today.

According to a recent LinkedIn survey of more than 1250 professionals, most respondents agree that this gap in the demand and supply of digital talent affects their digital transformation program. Similarly, these companies lose their competitive edge because of this impact.

Therefore, the biggest challenge for healthcare leaders is to address and attract the right talent. 

However, there are quite a few obstacles for healthcare leaders to overcome when finding and recruiting talented remote developers.

  1. Building a large enough pipeline to attract truly talented developers.
  2. Developing a system to vet thousands of remote developers without consuming all of their team’s resources.
  3. Ensuring that remote development is simple, secure, and compliant.

The market has several competitive recruiting and staffing companies. However, these companies are not the best option for sourcing experienced remote developers.

  1. They don’t have a global reach.
  2. Since they don’t cater to a single vertical, they cannot attract highly experienced developers.
  3. They don’t have a specialized vetting process to recruit skilled engineers.

Turing.com helps tackle all these existing challenges in the market.

How is Turing.com positioned to help these healthcare industry leaders?

Turing.com helps bridge the supply and demand gap by developing digital talent clouds and assisting enterprises in building a global talent pool at scale.

How are we doing it?

Turing.com sources engineers from over 200+ countries, vets them to a Silicon-Valley standard, matches them based on customer requirements via AI, makes it easy for developers and enterprises to collaborate securely, and extracts the complexity of hiring global employees.

200+ enterprises are currently leveraging Turing.com to spin up their dream software engineering teams in multiple ways:

  1. Building a globally distributed workforce by taking advantage of Turing.com’s global community of 1M+ developers from all parts of the world.
  2. Addressing demand-elasticity, which can last anywhere between 6 to 18 months. Enterprises that fall in this category typically aim for speed and quality while hiring remote developers. Turing.com has taken a process that usually takes months and condensed it to mere days. 
  3. Developing a geo-focused development team by hiring talented developers from countries like Brazil and Argentina because they offer an excellent timezone overlap. Turing.com’s local community helps such companies source the right developers to build their geo-focused team at a reasonable timeframe. 

Furthermore, Turing.com takes care of the complexities of building a global workforce by handling the payroll, tax forms compliance, and all other complicated aspects of international hiring.

Sign up with Turing.com today, tell us what you’re looking for, and our Intelligent Cloud System will get you pre-vetted developers within days instead of weeks or months.

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By ● Feb 8, 2022