If you haven't already, please study our free Bootstrapping course and the Investor Introductions page. This is a fantastic discussion on how to cold start an AI company, build it to scale, etc. Also, excellent guidance on white spaces around which to build new AI companies. Sramana Mitra: Let's start by introducing our audience to yourself. Tell us a bit about your background and all also introduce us to People.ai. Oleg Rogynskyy: I am the CEO and Founder of People.ai. I have been doing startups all my life. My previous company Semantria was also an AI company. I started it in 2011 and then sold it in 2014. People.ai was started in 2016 when I moved out here to Silicon Valley. I have been working on it ever since. Sramana Mitra: Where did you move from? Oleg Rogynskyy: Montreal. I am originally from Slovenia. I worked for one of the first AI companies called Nstein Technologies from 2006 to 2010. We sold it to Open Text. Our technology now is the Open Text AI. I

"Bias in AI" has long been a critical area of research and concern in machine learning circles and has grown in awareness among general consumer audiences over the past couple of years as knowledge of AI has grown. It's a term that describes situations where ML-based data analytics systems show bias against certain groups of people. These biases usually reflect widespread societal biases about race, gender, biological sex, age, and culture. There are two types of bias in AI. One is algorithmic AI bias or "data bias," where algorithms are trained using biased data.

Natural language processing (NLP) has been one of the hottest sectors in AI over the past two years. Will the string of big data breakthroughs continue into 2022? We checked in with industry experts to find out. There's been a veritable arms race to develop large transformer models over the past couple of years. It started in 2020 with OpenAI's GPT-3 with 175 billion parameters.

Governments and agencies are struggling to coordinate effective disaster relief programs. Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) can help. Natural disasters wreak havoc around the world every year. But it's hard to appreciate the scale of this damage. And that doesn't include the Northern California Wildfires or the biggest hurricanes.

Deep learning is the current darling of AI. Used by behemoths such as Microsoft, Google and Amazon, it leverages artificial neural networks that "learn" through exposure to immense amounts of data. By immense we mean internet-scale amounts -- or billions of documents at a minimum. If your project draws upon publicly available data, deep learning can be a valuable tool. The same is true if budget isn't an issue.

Lexalytics, the leader in "words-first" machine learning and artificial intelligence, announced that Zignal Labs, creator of the Impact Intelligence platform for measuring the evolution of opinion in real time, has chosen Lexalytics to extend its natural language processing (NLP) and text analytics capabilities to help marketers, communicators and analysts gain a greater understanding of perceptions across traditional and social media. Zignal Labs has incorporated Lexalytics' on-premises Salience engine to analyze media in real time, across multiple industries, including financial services, technology, healthcare, consumer products, sports, entertainment and more. With Lexalytics, Zignal's customers can understand what people are saying about products, services or current events, categorize discussions into separate groupings and themes, and evaluate the sentiment of media coverage across multiple languages. "With more people working from home, and the increase in online discourse caused by the COVID-19 crisis and social justice movements, we've seen an explosion in the amount of content we're analyzing for our customers in all parts of the world and had a need to expand our NLP capabilities for international languages," said Jonathan Dodson, CTO of Zignal Labs. "We chose Lexalytics because out of all of the market leaders we evaluated, they have the best combination of accuracy and performance, breadth of foreign language capabilities, scale and price, as well as an on-premises solution, offering maximum tuning and features while keeping data processing costs to a minimum."

AI in education is more than science fiction. One study found that 34 hours on Duolingo's app are equivalent to a full university semester of language education. But educational AI and the broader category of educational technology (EdTech) go well beyond language learning. Companies like Carnegie Learning and Fuel Education apply artificial intelligence to K-12 learning. One of the most popular EdTech platforms, McGraw Hill's ALEKS, is a web-based, AI-powered assessment and learning system that covers K-12, homeschool and even college content.

I actually got my degree in Business Administration, Political Science and International Relations. After graduating, my first job was in sales for a company that pioneered in Artificial Intelligence (AI) and Natural Language Processing (NLP) for enterprise back in 2006, called Nstein Technologies. This is where I fostered an interest in AI, NLP and machine learning and from there I moved to Lexalytics, where I saw the need for democratised, cloud-based analytics. I left Lexalytics and started Semantria which was later acquired by Lexalytics, before starting People.ai in 2016. How did you come to found People.ai and what were your reasons for believing in your proposition?

The old saying "retail is detail" has never been more true. From what's in stock to the size of the customer service desk, data science rules the decision-making process. While machine learning has been the darling of large chains for years, increasing accessibility, ROI and AI hype are encouraging uptake among mid-sized chains looking to maximize operational efficiency alongside delivering superior customer service. Here are some of the ways AI can be used to drive growth, efficiency and profit. Location is king in retail, and the difference between an okay and a prime location can have a significant impact on your bottom line.

Every new car comes with an owner's manual. Among the wisdom contained in that manual is a maintenance and repair logbook with recommendations for when to change out parts or flush the system. That's because all mechanical items are subject to wear and tear over time. Things break down, go awry or are subject to the whims of the weather. AI is the same – although to an even greater (and more expensive) extent.