Latest AI Articles:

1 - Sucking Robot Arm Wins Amazon Picking Challenge

A Dutch robotics team was awarded the top prize in Amazon's most recent warehouse bot competition in Germany. Team Delft's bot excelled in two tasks: selecting and picking products from a container and putting them on a shelf, and performing those actions in reverse. Teams were given a computer file, five minutes before the start of the competition, that detailed the range of objects involved and instructions for moving each. Once the task began, the robots acted autonomously .The competition is held to with the ultimate goal of furthering collaboration between humans and robots in Amazon's warehouses. At present, robots move items around the building, but humans are still relied upon to stock shelves.

Episode Summary: If you’re in the San Francisco Bay area, it’s not all that novel to be trained in or working on some form of AI; however, to be doing so in the 1980s and 1990s was a more rare occurrence. Dr. Lorien Pratt has been working with neural nets and AI applications for many decades, and she does lots of consulting work in implementing these technologies with companies in the Bay area. In this episode, Lorien provides her unique perspective on decades of development and adoption in AI as we ask, where is the traction today in places where it wasn’t 5 or 10 years ago? We also discuss where Lorien thinks machine learning applications in business and government seem to be headed in the near term.

Episode Summary: When we think about applying AI and data science to different areas of business, we often think about those domains that offer a wide swath of quantitative metrics that we can feed a machine, like marketing or finance. Human resources (HR) normally doesn’t fit the bill. How we hired someone, how we felt about them when we hired them, how they perform qualitatively, these are things that are often difficult to discern in team dynamics. That being said, big teams like Google are applying machine learning (ML) to some of their HR choices, and our guest today believes more companies will be doing the same in future. CEO of Humanyze Ben Waber applies ML  to HR decision-making, helping people get better employees and better performance by measuring and improving using data science in new ways.

1 - Robot Eyes and Humans Fix on Different Things to Decode a Scene

A team from Facebook AI Research recently published a paper on their work in mapping active neural nets in both humans and machine learning systems when decoding visual objects. It turns out that humans and the machines involved in the study do not focus on the same details to features in order to make their determinations, lending an air of mystery two different complex systems scientists are constantly seeking to better understand. The task of decoding was not always as straight-forward as identifying a specific object; the humans and machines were both asked questions like "What is the man doing?" and "What number of cats are lying on the bed?" Humans still rule on visual imaging, but the question as to whether machine vision neural nets should be altered to more closely resemble those of humans is still up for debate.

Episode Summary: There are hedge funds and financial institutions that already use real-time data and sentiment analysis from social media, articles and videos in real-time to potentially make better trading decisions - but what does it mean when those same companies can use real-time satellite information to detect company activities and make trades based on that data? In this episode, Research Director of Capital Markets at Celent Securities discusses the focus on emerging technologies in trading and finance. He talks about the way that analytics and machine learning have affected the ways banks operate, the kinds of data that hedge funds and individual investors now have at their fingertips, and what that means for the future implications of AI-related technology in the finance world.

Episode Summary: Not more than 10 years ago, it would have been difficult to talk into your phone and have anything meaningful happen. AI and natural language processing (NLP) have made large leaps in the last decade, and in this episode Dr. Catherine Havasi articulates why and how. Havasi talks about how NLP used to work, and how a focus on deep learning has helped transform the prevalence and capabilities of NLP in the industry. For the last 17 years, Havasi has been working on a project through the MIT Media Lab called ConceptNet, a common sense lexicon for machines. She is also Founder of Luminoso, which helps businesses make sense of text data and improve processes, and one of a handful of female leaders in the AI field who we've had the privilege of interviewing in the past year.

It's been over a month since our last major artificial intelligence consensus (which covered 33 AI researcher perspectives on the 20-year risks of AI), and we decided that this time around, we'd speak with AI executives directly about the future of artificial intelligence and machine learning in consumer tech.
The media is awash with buzz-stories about autonomous vehicles, speech recognition, robotics, and more, but it seems difficult to glean a perspective on which consumer AI tech trends are likely to make the biggest impact in the coming 5 years.
While there's certainly no crystal ball, our preference as a market research firm is to combine research and news analysis with a strong consensus from dozens of experts in the field. When it comes to AI for consumer tech, we decided to ask executives and founders of artificial intelligence companies what they believe to be the most important AI consumer tech trends in the next half decade.
You can see a full list of the answers to our "AI Consumer Tech Trends" below in our large infographic.

Episode Summary: When it comes to data science and machine learning, what are the related skills that are getting people jobs and what are the industries that are supplying those in-demand jobs? These are two important questions that we discuss in this week’s episode with CrowdFlower’s CEO Lukas Biewald, whose company is providing a pragmatic perspective of the industry by focusing on assessing job listings and related information in the field of data science. If you’re a company that is interested in finding someone with in-demand data science and related skills, or if you’re in the market to find a position in this field, this episode will likely be very useful!

1 - You May Already Be Using Google’s AI Chips and Not Know It

At this week's 2016 Wired Business Conference in New York City, Google announced that it's already using its in-house artificial intelligence processing chips (and they may have been present in some products for the last year). Known as Tensor Processing Units (TPUs) after TensorFlow, the chips are being used in more than 100 projects, including Android's voice recognition system and Google's new cross-application search service Springboard. Cloud customers may also be tapping into the benefits of these AI-supported chips. According to Diane Greene, head of Google's cloud business, the TPUs can handle machine learning tasks more quickly than graphics processors or other chips on the market. While Amazon is still the leader in the cloud computing market, Google's custom chips could dent the market. Greene commented that over sales, Google's focus in this department is on engineering and innovation.

[This story has been revised and updated.]

Big data has turned out to be a key ingredient in turning machine learning from an abstract technology into a potentially invaluable tool of insight and foresight for businesses across industries. The burgeoning cognitive technologies of predictive analytics and data visualization are opening new windows of opportunity to companies trying to solve complex problems with multiple moving parts. From finding ways to retain new customers to more efficiently monitoring multiple performance metrics and easing performance volatility, more companies are gravitating towards machine learning-based data analysis tools in an effort to optimize operations and find innovative solutions and opportunities that were once too obscure for only the human eye.