The data volumes expected from the Internet of Things (IoT) are certain to be large – too large, in fact, for even an army of trained analysts to turn into useful information in a reasonable amount of time. This is why every solution aimed at the IoT relies heavily on automation, simply to manage the flow of information between devices and to centralized storage and analytics systems. But even this is not likely to be enough. To fully leverage the IoT, it's becoming obvious that the enterprise will have to utilize new forms of artificial intelligence and machine learning to basically allow the environment to makes its own use of available data and tell human operators what needs to be done. Already, this is emerging on leading IoT platforms.

The automobile is being dismantled, reimagined, and rebuilt in Silicon Valley. Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. In particular, it shows how valuable on-the-road data is likely to be in the evolution of automated driving. While the price tag might seem steep, especially with so many players in automated driving today, Mobileye has some key technological strengths and strategic advantages. It's also developing new technologies that could help solidify this position.

Genpact (NYSE:G), a global professional services firm focused on delivering digital transformation for clients, has signed a definitive agreement to acquire Rage Frameworks, a leader in knowledge-based automation technology and services providing Artificial Intelligence (AI) for the Enterprise. Terms of the deal are not disclosed. As part of its strategy to drive both digital-led innovation and digital-enabled intelligent operations for its clients, Genpact is investing in leading technologies, such as AI, that are transforming the way companies in many industries compete. Genpact will embed Rage's AI in business operations and apply it to complex enterprise issues to allow clients to generate insights and drive decisions and action, at a scale and speed that humans alone could not achieve. "As advanced technologies such as AI fundamentally change the definition of work, the ability for CXOs to find and leverage new solutions that combine the best elements of human expertise and machine intelligence, will be critical to their ability to gain and sustain competitive advantage," said NV'Tiger' Tyagarajan, president and CEO, Genpact.

Cloud accounting software provider Xero announced a new product that it says will bring machine learning and AI to the process of small business invoicing. The machine learning automation technology is currently without an official title, but Xero says it's designed to simplify and improve the way invoices are filed. More specifically, the technology will automatically suggest the appropriate account codes for an invoice based on past invoicing behavior of the business owner and their advisor. While the use case is indeed specific, Xero posits that small businesses frequently mislabel invoices, which in turn creates more work for themselves and their accountants down the road. "Before we work with a client, we always review their balance sheet to see if items are correctly allocated," said Andrew Erkins, director of technology at Xero Partner Digit Books.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. We're inviting 250 to exhibit at TNW Conference and pitch on stage!

IBM researchers have set a milestone in conversational speech recognition by achieving a new industry record of a 5.5 percent word error rate, surpassing its previous record of 6.9 percent, according to the company's blog post. The researchers conducted a difficult speech recognition task to achieve this record, where they recorded conversations between humans discussing typical everyday topics like "buying a car." This recorded corpus, titled "SWITCHBOARD", has been used for over two decades to benchmark speech recognition systems. To achieve the 5.5 percent record, the researchers focused on extending the company's application of deep learning technologies by combining LSTM (Long Short Term Memory) and WaveNet language models with three strong acoustic models. The first two models were six-layer bidirectional LSTMs, with one of the models being equipped with multiple feature inputs and the other being trained with speaker-adversarial multi-task learning.

Nice and steady does it. A video analysis system uses motion tracking and machine learning to assess how good surgeons are at suturing a wound. "Different surgeons all have different styles of suturing," says Aneeq Zia at the Georgia Institute of Technology in Atlanta, whose team developed the system. The team first captured footage of 41 surgeons and nurses practising their suturing and knot-tying skills on foam boards. Participants also wore an accelerometer on each hand to capture motion data, and a clinician then watched the videos and rated each person's skills.

My wife used to love reading but since her stroke has aphasia, no speech, limited vision and limited dexterity in her left hand only. She can select TV channels on a remote but she cannot read a short news story let alone a novel, so she listens to the radio and watches a lot of TV. I thought of getting her a Kindle e-reader but they don't seem to do text to speech any more. A shop assistant suggested a tablet with a text-to-speech app. It needs a really simple interface or my wife will not be able to use it without assistance.

Didi Chuxing President Jean Liu speaks at the WSJ D Live technology conference in Laguna Beach, California, on Oct. 25, 2016. The market for Silicon Valley engineers with expertise in artificial intelligence and automated driving tech keeps getting tighter. Didi Chuxing, the ride-hailing giant backed by Apple that forced Uber to abandon its efforts to win over the Chinese market last year, just opened an R&D center in the self-driving car capital with a big "help wanted" sign in the window. Mountain View, California-based Didi Labs will focus on "AI-based security and intelligent driving technologies," the company said on March 8. It will be led by computer scientist Fengmin Gong, vice president of the Didi Research Institute and a co-founder of Palo Alto Networks.

How do you ensure a smooth hand-off between data science and engineering on ML projects? How do you ensure a smooth hand-off between data science and engineering on ML projects? Excellent question, and one that I've been thinking a lot. At Quora, my data science team works closely with machine learning engineers. Joe Isaacson already provided a great answer to this question, and I will add some of my learnings.