Two weeks ago Facebook released yet another glossy marketing infographic site and video touting how its state of the art technology, top engineers and teams of experts have made massive strides in conquering yet another scourge of the online world through the power of advanced algorithms. This past week its EMEA counterterrorism lead announced that its algorithms were now deleting 99% of all ISIS and al-Qaida terrorism content across the site. As with all of Facebook's announcements to date, neither of these proclamations made any mention of how often the algorithms that increasingly control its platform are wrong and whether they are actually right more often than they are wrong. After initially promising to provide a response, the company once again declined to comment on the false positive rates of its algorithms or why despite repeated requests it continues to refuse to release those numbers. Why is the company so afraid to talk about whether its algorithms are actually accurate?

A robot developed by Taiwan engineers moving chess pieces on a board against an opponent at the 2017 Consumer Electronic Show (CES) in Las Vegas, Nevada, on January 8, 2017. Artificial intelligence and the ability of machines to interact with and manipulate human beings raises suspicion.

Today's world is flooded with data, and machine learning has been transforming how businesses deal with that data. Even if you have not been following along in the details of machine learning and neural networks, these technologies are affecting your life. Amazon, Google, Facebook, Microsoft, and Baidu are all leveraging machine learning for tasks like interpreting human speech, translating between languages, recognizing images, and recommending products. But the impact of machine learning in today's business environment is too significant to be content with surface-level familiarity. From start-ups to Fortune-50 corporations, businesses increasingly rely on machine learning to remain competitive in a data-soaked environment.

Let's use the analogy of autonomous electric cars to explore the new opportunities for the intelligent enterprise. Autonomous cars use a variety of sensors that constantly gather information about what's going on--not just traditional indicators such as speed and temperature, but also the world outside, using cameras and advanced image recognition. All the data is processed and combined on the fly to provide an optimized journey. Organizations also now have much more visibility into business processes using sensors and the internet of things. These new technologies collect and connect data that was previously siloed and use it to recognize previously unseen patterns.

Beyond innovations in existing sectors, the rapidly improving price/performance of GPTs have led over time to the creation of whole new applications and industries. For example, the steady declines in the price of electricity-generated power and the improvement in the efficiency of electric motors led to the radical transformation of manufacturing in the early part of the 20th century with the advent of the assembly line. It also led to the creation of the consumer appliance industry. Similarly, as the semiconductor industry took off, it led to the historical transition from the industrial economy of the past two centuries to our ongoing digital economy. It's only been in the last few years that major advances in machine learning have taken AI from the lab to early adopters in the marketplace.

When 62-year-old computer scientist P Anandan started last September as CEO at the Wadhwani Institute of Artificial Intelligence (WIAI), he might have been apprehensive. He was signing up to work on AI in India after three decades at major global corporations and academia in the US and at home, including top teaching and research roles at Yale University, Adobe and Microsoft. Misconceptions abound about big data, machine learning, automation and other AI-related technologies in the context of job losses for humans. And unlike other major economies, India hadn't yet spelled out its vision for a future with AI. But when Anandan set out to work, he found support all around.

PanARMENIAN.Net - Apparently, teaching artificial intelligence to read our innermost thoughts or turning them in terrifying psychopaths isn't enough-now researchers are teaching AI systems to predict what humans will do in the future (and how long you'll be doing it) 'minutes or even hours' before you decide to do it, Outer Places says. It's fine when Google finishes your sentences when typing into a search bar, but this new technology might be able to recognize patterns in human behavior and perform tasks before you've even thought about asking. Like most tasks performed by artificial intelligence, this ability is tied to machine learning and neural networks. In the course of their research, a team from the University of Bonn in Germany tried out two models for their networks: one that made predictions and'reflected' before making new more, and one that was based on a matrix structure. Both networks were shown videos of people making relatively simple food dishes (especially breakfasts and salad) with the goal of teaching them to predict what the chef was going to do next.

Amidst a fresh cycle of reports last week, Facebook confirmed that it had data partnerships with no less than 60 device manufacturers, including four Chinese firms -- Huawei, Lenovo, Oppo and TCL. These companies maintained access to Facebook user data as well as information on a user's friends -- even though Facebook did not collect prior consent. Many have already highlighted the tremendous harm that such expansive sharing of data with third parties -- particularly with firms that have close associations with foreign governments that harbor their own agendas against the United States -- poses for American democracy. Beyond the obvious risk to individual privacy is the concern that this never-ending leakage of data could add fuel to the raging fire of political disinformation. Indeed, access to sensitive personal data offers exactly the foothold necessary for the propagators of disinformation -- both foreign and domestic -- to operate with effectiveness and precision.

Google began their foray into the AI and machine intelligence sectors thanks to the computing power they're able to leverage in the company's data centers. This worked out great as it allowed people all around the world to see how beneficial it was without needing to invest in new hardware. This was when they started to develop their own hardware to handle these computational cycles, and that saved the company a ton of money as this was better designed to handle the tasks required for quick and efficient machine learning algorithms. The latest extension of their progress has come in the form of on-device machine learning hardware. Today, the company showed how Google Translate benefits from using on-device machine learning technology.