AI in the Workplace

Here are five important AI stories from the week.

AI to Measure Work Performance (The New York Times)

AI provides workers such as customer service representatives real-time feedback on how to be better customer service representatives, nudging them to be more cheery, to slow down or speed up, and more. In some cases, the AI decides which workers are the worst performers and, therefore, should be let go. AI to empower and surveil employees is on the rise.

The Ubiquity of Facial Recognition Technology (The Atlantic)

Everywhere you go, facial recognition technology will be there to identify who you are. This is both empowering and potentially scary. Here is a short video that describes the technology in good detail—a must watch for those that want to know more about how this all works.

The State of AI Today (MMC Ventures)

A phenomenal report on the state of AI today and where we are headed.

The House Always Wins (Bloomberg)

Casinos now have more data than ever before to know which gamblers to target and when and for how much. This includes use of facial recognition and digitally-enabled poker chips to gauge which gamblers have the highest risk appetite and, therefore, stand to lose the most money to the house.

Datafication of Everything (Matt Turck)

In this two-part read (see the second part here), Matt Turck, a VC at FirstMark, discusses the rise of data along with issues of data privacy and AI ethics and the latest trends in the space.

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State of AI Report 2019 (Nathan Benaich)

Ankur Patel
Living in the Age of AI

Here are five important AI stories from the week.


WIRED explores how AI is now becoming ubiquitous in everyday life around us in this 42-minute-long film. In just a few short years, AI has gone from an academic curiosity to something we cannot live without. I highly recommend watching this film.

An AI Film by Fortune (Fortune)

Budgets for AI are on the rise. This 12-minute-long film by Fortune explains just how AI transitioned from a nice-to-have to a must-have across businesses worldwide.

How Machine Learning is Used in Enterprise (

Very rarely do machine learning models get deployed in production across enterprise. This excellent read goes into why building AI products is just so difficult and what we could do to improve the odds that machine learning models make it to production.

Platform for Data Analysis by the Masses (TechCrunch)

Excel is the best data analysis tool of all time; even though more sophisticated tools involving code have cropped up over the past decade, the barriers to use these tools remain pretty high. But, the barriers are getting gradually lower. New companies like this Y Combinator-backed startup Intersect Labs are trying to lower the barrier for mainstream business analysts to work with data and get value from machine learning. Intersect Labs launched a graphical interface-based platform so business users could load and analyze data with just a few mouse clicks.

Investors Will Adopt AI or Die (MarketWatch)

In this op-ed, Michael Heldmann, the chief investment officer for the U.S. Systematic Equity team at Allianz Global Investors, argues that machine learning will not replace portfolio managers any time soon but machine learning has become a must-have for all investors—if they want to stay competitive.

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The Future with Virtual Reality and Artificial Intelligence (The New York Times)

Ankur Patel
AI to Fight Fake News

Here are five important AI stories from the week.

AI to Fight Fake News (Allen Institute for AI)

With the major breakthroughs in the field of natural language processing over the past 18 months, it is much easier than ever before to generate fake—yet seemingly real—news. To study and detect fake news, the Allen Institute developed a model that both generates fake news and detects whether an article was written by its AI—called Grover—or a human. I highly recommend you check out this demo.

AI to Generate Bill Gates’s Voice Synthetically (MIT Technology Review)

Facebook AI Research (FAIR) discovered a method to convert text to speech and produce near-realistic human-like audio. The Facebook model trained on Bill Gates’s voice so the conversion emulates his voice. This is a remarkable breakthrough but raises concerns about the potential explosion of fake audio content in the coming years. Fake news is already a major problem today. To check out the Bill Gates samples, please see the article.

The New Age of AI-Enabled Surveillance (Vice)

Given the advances in computer vision over the past six years, it is now possible not only to identify faces in video but also automatically notify authorities based on suspicious human actions in videos such as entering a forbidden area, loitering, urinating in public, etc. Powered by AI, surveillance will be ubiquitous in society, raising concerns of a total surveillance state similar to what is happening in China today.

Amazon Opens its Recommendation API to the Public (Amazon)

Amazon’s success in online shopping is partly due to its incredibly good recommendations engine. Now, Amazon is releasing its recommendation system—called Amazon Personalize—to the masses, allowing customers of Amazon Web Services to use the recommendation system in their own applications. This will lead to more personalized product and content recommendations, search results, and marketing campaigns for more businesses.

AI to Spot Fake Images (The Verge)

In addition to fake news and fake audio, fake images and fake video (e.g., a fake video featuring Mark Zuckerberg giving a sinister speech) are becoming commonplace. To combat fake content, Adobe—the creator of Photoshop—developed an AI to detect edited media. In its internal tests, Adobe’s AI spotted 99 percent of edited faces. In this age of disinformation, firms are developing tools to fight back.

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Top 34 NLP Startups (AI Startups)

AI to Detect Construction Site Accidents (MIT Technology Review)

Ankur Patel
A People-First AI Strategy

Here are five important AI stories from the week.

A People-First AI Strategy (Wharton)

To have the most success implementing AI in enterprise, people must feel empowered by AI, not threatened. People are the most important asset at most companies, and AI should support them. If AI remains a blackbox and is viewed as a substitute of humans rather than a complement to them, there will be substantial resistance to AI, limiting its adoption. We need a human-centric AI strategy to improve AI’s adoption rate.

AI at Amazon (Amazon)

A 94-second video on how artificial intelligence and machine learning drive operations at Amazon, enabling services such as one-day Prime delivery. These automation and optimization techniques are ubiquitous at the larger tech-enabled firms and will become a mainstay in corporate America in the next few years.

AI at Twitter (Variety)

Twitter acquires one-year-old AI startup Fabula AI to fight fake news. Fabula specializes in graph deep learning, which is a relatively new method to find relations and interactions in large and complex datasets. For Twitter, the ability to analyze how various Tweets, Retweets, Likes, and Twitter users are related to each other and how the interactions evolve over time is crucial. For more on how Fabula AI spots fake news, read this TechCrunch piece from earlier this year.

AI at Facebook (Facebook)

In a recent study, Facebook discovered that object recognition of common household items from low-income, typically non-Western countries, was very poor. This is another example of how machine learning models trained on large datasets of images from Western nations such as the United States have poor performance when applied to a global context. To reduce the bias of gender, race, cultural background, country of origin, and other socio-economic factors, Facebook is working hard to source more diverse, more globally representative datasets.

AI at Microsoft (Microsoft)

Amazon, Google, and Microsoft are engaged in a fierce race to push out machine learning as a service to enterprise clients. Recently, Microsoft upgraded its forecasting service with automated machine learning (AutoML). New features include a new forecast function, rolling-origin cross validation, and automated feature engineering using lags, rolling windows, and holidays. The lives of data scientists just became considerably easier. For additional resources, please visit the how-to guide.

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NLP as the Key to Digital Transformation (CIO)

The Rise of Conversational UI (Forbes)

NLP for Smart Devices (ITProPortal)

5 Examples of NLP (Algorithm-XLab)

A Simple Guide to NLP (Forbes)

PyTorch Implementation of OpenAI GPT-2 Small Model (Medium)

FastBert, an easy-to-use library for Google BERT (Medium)

Platform for Robots (Robust.AI)

OpenAI’s AI to Generate Music (OpenAI)

Ankur Patel