Virtually everywhere you look, Bay Area tech businesses are running into walls. Smartphones were revolutionary and lucrative, but the U.S. market is saturated, and Apple's iPhone sales have fallen for three quarters. The "app economy" has matured, with more people using existing apps than downloading new ones. And Facebook, which has filled users' news feeds with so many ads it can barely add more, is predicting its revenue growth will slump next year. Silicon Valley needs its next big thing, a focus for the concentrated brain power and innovation infrastructure that have made this region the world leader in transformative technology.

Virtually everywhere you look, Bay Area tech businesses are running into walls. Smartphones were revolutionary and lucrative, but the U.S. market is saturated, and Apple's iPhone sales have fallen for three quarters. The "app economy" has matured, with more people using existing apps than downloading new ones. And Facebook, which has filled users' news feeds with so many ads it can barely add more, is predicting its revenue growth will slump next year. Silicon Valley needs its next big thing, a focus for the concentrated brain power and innovation infrastructure that have made this region the world leader in transformative technology.

Virtually everywhere you look, Bay Area tech businesses are running into walls. Smartphones were revolutionary and lucrative, but the U.S. market is saturated, and Apple's iPhone sales have fallen for three quarters. The "app economy" has matured, with more people using existing apps than downloading new ones. And Facebook, which has filled users' news feeds with so many ads it can barely add more, is predicting its revenue growth will slump next year. Silicon Valley needs its next big thing, a focus for the concentrated brain power and innovation infrastructure that have made this region the world leader in transformative technology.

LeadGenius, which uses machine learning to provide a marketing and sales tool for businesses, has raised $4 million in a new round of debt and equity funding. Above: LeadGenius cofounders David Rolnitzky (left), chief product officer, and Prayag Narula, CEO. The Berkeley, Calif.-based company said the investors in the round included SJF Ventures, as well as existing investors Lumia Capital and Javelin Venture Partners. LeadGenius uses a combination of machine learning and real human researchers to power its business-to-business (B2B) service platform. The company will use the money to add multi-channel capabilities to LeadGenius for outbound marketing and sales.

ARM servers are devalued partly because many applications don't work with the chips. But ARM has acquired Allinea Software with the hope of partially resolving the compatibility issue. Allinea provides software development, debugging, and porting tools, which should make it easier for people to write applications for ARM-based servers and supercomputers. The acquisition will "provide a channel to thousands of developers using supercomputers and give us better first-hand knowledge of the issues being addressed as software is ported to new ARM-based systems," Javier Orensanz, general manager of the development solutions group at ARM, said in a blog entry. The development tools will also be used for ARM chips in deep-learning systems, which require large-scale server deployments for analytics. ARM's competition will come from deep-learning and HPC software tools offered by Intel, Nvidia, Google, Microsoft, and more recently, AMD.

Etsy, the online marketplace where you can buy handmade and unique items, is eleven years old. IPO'ing in 2015 and with millions of products Etsy is shifting more than just crafts though. I wanted to know more about what's going underneath the bonnet of this juggernaut. It turns out Etsy is boasting solid results this year; in the third quarter it reported 33% revenue growth, 19% growth in gross merchandise sales, 50% growth in seller services, and a 110% increase in adjusted EBITDA (with a margin of 14.9%). It also raised its full-year guidance.

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Increased investor interest in AI startups – from around 10 deals in Q1'11 to over 120 in Q2'16 – can be attributed to recent advances in machine learning algorithms, particularly "deep learning" technology, a souped up version of AI. Just this week, Google integrated deep learning into its Google Translate tool; Baidu announced the launch of DeepBench, an "open source benchmarking tool for evaluating deep learning performance across different hardware platforms"; and NVIDIA introduced Xavier, a deep learning-based supercomputer for driverless cars. In the private market, Google put deep learning in the spotlight back in 2014 when it acquired 4 startups focused on this AI tech in quick succession: DeepMind, Vision Factory, Dark Blue Labs, and DNNresearch. Apple, which joined the race in 2015, most recently acquired Turi, which has developed a deep learning toolkit, among other AI-based solutions. Not to be outdone, Intel has acquired around 5 AI startups since January 2015, including deep learning startup Nervana Systems and, more recently, Movidius.

In recent years, advanced machine learning techniques have enabled computers to recognize objects in images, understand commands from spoken sentences, and translate written language. But while consumer products like Apple's Siri and Google Translate might operate in real time, actually building the complex mathematical models these tools rely on can take traditional computers large amounts of time, energy, and processing power. As a result, chipmakers like Intel, graphics powerhouse Nvidia, mobile computing kingpin Qualcomm, and a number of startups are racing to develop specialized hardware to make modern deep learning significantly cheaper and faster. The importance of such chips for developing and training new AI algorithms quickly cannot be understated, according to some AI researchers. "Instead of months, it could be days," Nvidia CEO Jen-Hsun Huang said in a November earnings call, discussing the time required to train a computer to do a new task.

In recent years, advanced machine learning techniques have enabled computers to recognize objects in images, understand commands from spoken sentences, and translate written language. But while consumer products like Apple's Siri and Google Translate might operate in real time, actually building the complex mathematical models these tools rely on can take traditional computers large amounts of time, energy, and processing power. As a result, chipmakers like Intel, graphics powerhouse Nvidia, mobile computing kingpin Qualcomm, and a number of startups are racing to develop specialized hardware to make modern deep learning significantly cheaper and faster. The importance of such chips for developing and training new AI algorithms quickly cannot be understated, according to some AI researchers. "Instead of months, it could be days," Nvidia CEO Jen-Hsun Huang said in a November earnings call, discussing the time required to train a computer to do a new task.