Twitter Inc. TWTR, 3.21% said Monday that it bought U.K.-based Magic Pony Technology, which develops machine learning and visual processing technology, for an undisclosed amount. The deal follows Twitter's previous acquisitions into the machine-learning space, starting with Madbits in July 2014 and Whetlab in June 2015. "Machine learning is increasingly at the core of everything we build at Twitter," said Twitter Chief Executive Jack Dorsey. Twitter's stock, which rose 1.6% in morning trade Monday, has tumbled 30% year to date, while the S&P 500 has gained 2.6%.

The media interest surrounding deep learning has grown exponentially in the last few years. But what does it actually mean, and how will it change business and society? Deep learning is a subset of machine learning that refers to mapping artificial neural networks to recreate some of the same processes that the human brain performs, and using algorithms with speech, images and text, to recognize, identify and understand patterns in the data. Although this sounds simple, it involves complex processes and functions – but once trained, the application of deep learning algorithms could be world changing. For instance, a machine that learns like a human, but can rapidly process thousands of images and recognize patterns, is already showing promise for applying deep learning to medical imaging.

One of the key assumptions of quantitative trading strategy evaluation is that Type II errors (missed discoveries) are preferable to Type I errors (false discoveries.) However, practitioners have known for long that the statistical properties of some genuine trading strategies are often indistinguishable from those of random trading strategies. Therefore, any adjustments of statistics to guard against p-hacking increase Type II error unless the power of the test is high. At the same time, the power of the test is limited by insufficient samples and changing market conditions. Furthermore, genuine strategies with statistical properties that are similar to those of random strategies may overfit due to favorable market conditions but fail when market conditions change.

This Social Experiment Used Free Wi-Fi To Try To Get People To Interact In Real Life Co.Exist...

Though deals to private artificial intelligence companies -- excluding incubator/accelerator rounds -- fell 10% in Q2'16, dollar funding reached an all-time high. That was partly thanks to 3 100M mega-rounds by companies using AI: a 154M Series A round went to China-based healthcare startup iCarbonX (with the participation of Tencent, Vcanbio), a 100M growth equity round was raised by New Jersey-based Fractal Analytics (from Khazanah Nasional Berhad), and there was a 100M Series D round raised by California-based cybersecurity unicorn Cylance (from investors including Blackstone Group, Insight Venture Partners, and Khosla Ventures). Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Nearly 70% of the deals went to startups in the United States in Q2'16. A majority of the startups raising funds were still in their early-stages: Nearly 60% of the deals went to startups raising seed/angel and Series A rounds, while mid-stage startups (Series B and C) received 12% of the deals.

But stepping back from it, I realized that Siri was everywhere in the roughly two-hour and fifteen-minute presentation. What we saw on Monday was next-level Siri. The software started as a pretty simple voice assistant, then graduated to digital assistant. It is now nothing less than an artificial intelligence -- one that can rain AI fairy dust on any number of services and third-party apps. Hello, SiriKit As predicted, Siri finally has an SDK -- sort of.

Artificial intelligence and robotics are leading a transformation in Silicon Valley. Artificial intelligence (AI) and robotics are leading a transformation in Silicon Valley, which is expected to be as significant as the personal computing industry and commercial Internet eras were. "Whenever there is a new idea, the valley swarms it," says Nvidia CEO Jen-Hsun Huang. "But you have to wait for a good idea, and good ideas don't happen every day." Some technologists see innovations such as deep learning establishing a platform for machines with human-level intelligence, and although past efforts in the field have come up short, many investors are confident the renewed interest in AI will bear fruit.

Sure, Facebook has "M", Google has "Google Now", and Siri's voice isn't always that of a woman. But it does feel worth noting that (typically male-dominated) engineering groups routinely give women's names to the things you issue commands to. Is artificial intelligence work about Adams making Eves? The response to this critique is usually about the voices people trust and find easy to understand. Adrienne LaFrance over at The Atlantic does a good job discussing those points, so go read her article.

John Melas-Kyriazi is a senior associate at Spark Capital interested in the AI and machine learning space. When I woke up this morning, I asked my assistant a simple question: "Siri, is it going to rain today?" Siri understood my intent, pulled the local weather data via an API and answered me in less than two seconds: "There's no rain in the forecast for today." In the not-too-distant past, this kind of human-computer interaction would have blown away technologists and delighted consumers -- but in 2016, it's nothing special. Conversations with Siri are commonplace, just like they are with Microsoft's Cortana and Amazon's Alexa.

Artificial intelligence won't be able to control all aspects of operating a motor vehicle for quite some time, said technology trend spotter Scott Kupor. "It's going to be a long time ... before we get to fully autonomous vehicles," the managing partner of venture capital powerhouse Andreessen Horowitz told CNBC's "Squawk Box" on Wednesday. "We're not going to go from where we are today to fully autonomous vehicles tomorrow. There are different stages of autonomous vehicles," he said, such as human-assisted autonomous driving. In the race to build self-driving cars, Silicon Valley heavyweights, including the Google unit of Alphabet and Tesla Motors -- and traditional automakers, such as Toyota and General Motors, are vying to own the road.