The Apple Campus 2 is seen under construction in Cupertino, California in this aerial photo taken January 13, 2017. The case against Big Tech seems to be building by the week. And interestingly, some of the most powerful evidence is being provided by those who really know what they're talking about: tech insiders. Full disclosure: I am a tech insider myself. I run a tech company in Silicon Valley.

We all know that today's state-of-the-art AI can learn all sorts of things. It can learn to recognize kittens in YouTube videos or, perhaps more importantly, how to characterize traffic around an autonomous vehicle. All of us use speech at some point talking to our phones, our Google Home, our TV remote, or our Amazon Alexa. Of course, it does many other things to like stock predictions, robot walking, delivery routing... there are just too many to list. But most of what we read about in the regular press involves vision and occasionally human speech.

An international team has developed an AI algorithm with social skills that has outperformed humans in the ability to cooperate with people and machines in playing a variety of two-player games. The researchers, led by Iyad Rahwan, PhD, an MIT Associate Professor of Media Arts and Sciences, tested humans and the algorithm, called S# ("S sharp"), in three types of interactions: machine-machine, human-machine, and human-human. In most instances, machines programmed with S# outperformed humans in finding compromises that benefit both parties. "Two humans, if they were honest with each other and loyal, would have done as well as two machines," said lead author BYU computer science professor Jacob Crandall. "As it is, about half of the humans lied at some point. So essentially, this particular algorithm is learning that moral characteristics are better [since it's programmed to not lie] and it also learns to maintain cooperation once it emerges."

How to handle it all, on Talking Tech. Your every move is being tracked. If you didn't realize it by now, get used to it. Because it will only become more widespread in the coming days, months and years. The Consumer Reports headlines this week about how smart TVs are monitoring your watching got a lot of attention, but it should hardly be a surprise.

Regardless of available budget or staffing, there are steps your institution can take to start learning how voice will play a part in your digital strategy. Subscribe to The Financial Brand via email for FREE!In 2017, we saw large institutions such as Ally Bank, U.S. Bank, and Capital One begin to engage consumers through Amazon Alexa devices, empowering their respective consumers to get fast answers about their finances, receive affordability insights, and pay bills, all via voice. Yet, just because these large institutions made the strategic investment in voice channels, should all financial organizations? This is a great question, but what about the value of simply learning about new voice channels? Waiting to learn about, and experiment with, these channels until the market has fully adopted them may put you too far behind to catch up with the pace of voice adoption by consumers.

Amazon's Artificial Intelligence (AI) Alexa-powered smart speaker Echo has received rave reviews from critics and consumers since its introduction in 2014 across the world. Being a pioneer in the niche segment, Amazon, though a bit late, was still the first to enter the Indian market in late 2017, before Google Home and Apple HomePod. I have been using Amazon Echo for a couple of months now, and here are my thoughts on the smart speaker. My review unit is a generic Amazon Echo Grey (Rs 9,999). It has a simple cylindrical exterior body with a ring of multi-color lights -- a nice touch -- on the top, which comes to life whenever the device is switched on or when we converse with Alexa. On the exterior, it is covered with a contoured fabric that exudes a natural-like feel when touched by hand.

Eminent industry leaders worry that the biggest risk tied to artificial intelligence is the militaristic downfall of humanity. But there's a smaller community of people committed to addressing two more tangible risks: AI created with harmful biases built into its core, and AI that does not reflect the diversity of the users it serves. I am proud to be part of the second group of concerned practitioners. And I would argue that not addressing the issues of bias and diversity could lead to a different kind of weaponized AI. The good news is that AI is an opportunity to build technology with less human bias and built-in inequality than has been the case in previous innovations.

We consider recommendation in the context of optimal matching, i.e., we need to pair or match a user with an item in an optimal way. The framework is particularly relevant when the supply of an individual item is limited and it can only satisfy a small number of users even though it may be preferred by many. We leverage the methodology of optimal transport of discrete distributions and formulate an inverse optimal transport problem in order to learn the cost which gives rise to the observed matching. It leads to a non-convex optimization problem which is solved by alternating optimization. A key novel aspect of our formulation is the incorporation of marginal relaxation via regularized Wasserstein distance, significantly improving the robustness of the method in the face of observed empirical matchings. Our model has wide applicability including labor market, online dating, college application recommendation. We back up our claims with experiments on both synthetic data and real world datasets.

Apple finally released its HomePod on Friday after pushing back its planned December release. The HomePod comes into the smart speaker market that already has various options for customers, including gadgets from Amazon, Google and Sonos. The HomePod is one of the priciest home speaker models out there, priced just $50 less than the Google Home Max. The HomePod is powered by Siri, which allows users to send messages, create lists, set timers, get the news, check sports, traffic and weather updates. Siri also converts measurements, reads text messages out loud and helps with translations. The voice assistant works as a DJ as it progressively learns more about it user's taste in music.

WalkMe Ltd., maker of a platform for understanding and improving user experience, has added predictive analytics capabilities to its intelligent assistant technology that interprets user behavior to predict next actions and provide context-sensitive responses. The company primarily targets its technology at e-commerce scenarios in which abandonment is a common problem, as well as at internal uses such as helping employees fill out forms or complete online training courses. "We saw that most users don't ask for help, so our engagement engine understands their problems and gives guidance automatically," said Rephael Sweary, WalkMe's co-founder and president. WalkMe AI Predictive Analytics works with any enterprise software or mobile application to observe user interactions and determine the statistical likelihood that a person will abandon a process because of confusion or complexity. The software collects hundreds of data points per second in real-time, including information that isn't personally identifiable such as browser type and time of day.