Latest AI Articles:

1 - Self-Driving Cars: Feds Release Long-Awaited Rules

On Tuesday, the federal government released its long-anticipated new rules for self-driving cars. In a press release, U.S. Transportation Secretary Anthony Foxx stated,
“Automated vehicles have the potential to save thousands of lives, driving the single biggest leap in road safety that our country has ever taken.”
The guidelines are flexible and meant to evolve as the technology behind autonomous cars advances. While the suggested framework will be assimilated into state rules on regulation of human drivers, registration, traffic laws and enforcement, insurance and liability, the federal government foresees the National Highway Traffic Safety Administration maintaining control of regulation of motor vehicles and equipment, a move meant to encourage innovation but also set a bar for safety standards. The new 103-page policy includes a 15-point safety standard for "highly automated vehicle" (HAVs) manufacturers.

Typing "what is machine learning?" into a Google search opens up a pandora's box of forums, academic research, and false information - and the purpose of this article is to simplify the definition and understanding of machine learning thanks to the direct help from our panel of machine learning researchers.

Episode Summary: For some companies, big data remains an abstraction; for others, it's an integral part of the lifeblood of a business. Mat Harris is vice president at Sojern, a travel marketing platform that has leveraged big data to grow $3 billion in bookings and 1/3 of a billion traveler profiles across its platform. In this episode, Harris speaks about how Sojern and other businesses are using a combination of their data and other sources of data (what he calls third and "second" data sources) in order to make informed marketing decisions and better market their services to buyers. Harris sheds light on the direct ROI for big data in different businesses, and it's an interesting episode from the perspective of an executive who is using big data to make decisions on business directions.

1 - 10 Later-stage Startups Join the Seattle Machine Learning and Data Science Accelerator

Episode Summary: Companies looking to raise money are often asking what investors think of their company, their industry, and how they're making investment decisions in related companies. In this episode, I ask these questions of Robert Seidl, who is managing partner of Motus Ventures, an investment firm focusing funding early-stage software companies for autonomous Vehicles and the IoT. Seidl talks about various data sources and the people and networks from which investors draw information when they don’t have what they need on-hand, but need to make important investment decisions. He also shares his perspective on the high-energy and competitive investment world of AI, including his thoughts on the most exciting (and confusing) areas in the industry today.

As the term "machine learning" has heated up, interest in "robotics" (as expressed in Google Trends) has not altered much over the last three years. So how much of a place is there for machine learning in robotics?

Episode Summary: This week's interview was recorded live at Nuance's Silicon Valley office with guest Charles Ortiz, director of the AI and Natural Language (NL) Processing Lab for Nuance Communications in Silicon Valley. In this episode, Ortiz speaks about what he sees as the most important developments in natural language processing (NLP) over the last few years, what advancements brought us to where we are today, and where progress might take NLP in the coming years ahead (both at Nuance and beyond).

1 - WaveNet: DeepMind's Generative Model for Raw Audio

On Thursday, researchers at DeepMind introduced WaveNet, a "deep generative model of raw audio waveforms" that moves forward how sound is generated in text-to-speech (i.e. speech synthesis). The deep learning-based model is able to generate both increased human-like sounding speech  (DeepMind claims it has reduced the gap with human performance by over 50 percent), as well as music and other audio signals. WaveNet is different from the latest parametric TTS models because it directly models raw waveforms, as opposed to generating sound by passing outputs through vocoders (signal processing algorithms). On their blog, DeepMind provides both a link to their paper, as well as WaveNet sound clips of speech (in English and Mandarin) and generated piano music.

Episode Summary: Fifteen years ago, investing in AI may have seemed a bit far-fetched, but today it's not at all a rare occurrence; however, it's more rare to find entire firms dedicated to investing entirely in AI. In today's episode, we're joined by Saman Farid, co-founder of Comet Labs, an investment firm focused on investment in AI companies across industries. He speaks about his investment hypothesis in the future of AI, why he’s decided to hone his funds in this domain, and the different domains where he believes AI is ripe to disrupt on a global level in the coming few years.

Unlike other components to an enterprises' technology mix, determining the ROI of machine learning is a less-than-obvious process, particularly when solutions are new and little by way of case studies or benchmarks exist.