AI Articles and Analysis about Automation and robotics

Episode Summary: “Artificial intelligence (AI) can be seen as a progression in our scalability of labor.” This quote comes from this week’s guest, Naveen Rao, who received his PhD in Neuroscience from Brown before becoming CEO at Nervana Systems, which works on full stack solutions to help companies solve machine learning (ML) problems at scale. In this week’s episode, Rao speaks about certain domains in industry where he feels optimistic about machine learning (ML) making a difference in the next five to 10 years, providing interesting perspectives that include advances in the areas of agriculture and oil & gas.

In the first week of 2016, Facebook’s Mark Zuckerberg announced in a post that his goal for the year was to “build a simple AI to run my home and help me with my work.” He clarified, "You can think of it kind of like Jarvis in Iron Man.” Zuckerberg went on to describe his plan to explore presently available smart home technologies, implement them into his home, and train the system to coordinate with his family life and workaday. (Interestingly, Zuckerberg’s AI may utilize a number of devices, but he refers to the technology as a singular system, implying that he intends to develop a unified AI to oversee the many individual devices.)

In case we haven’t been cautioned enough about the threats of emerging artificial intelligence, a panel of academics addressed the American Association for the Advancement of Science (AAAS) on Sunday with a warning that advancements in intelligent and semi-intelligent automation could lead to overwhelming unemployment across many industries.
 
Machines’ ability to recognize patterns is yet to match our own, but their increasing sophistication in regards to tasks like speech recognition and data analysis has seen AI applied to real world applications such as autonomous driving. In this vain Bart Selman, professor of computer science at Cornell University, said, “For the first time, we’re going to see these machines and systems as part of our everyday life.”
The predicted success of self-driving cars may prove to be a blessing that greatly reduces car accidents, but – with 10% of U.S. jobs requiring some degree of vehicle operation – the technology will also undoubtedly effect the labor market. Moshe Vardi, professor of computer science and director of the Ken Kennedy Institute for Information Technology at Rice University, told AAAS, “We can expect the majority of these jobs will simply disappear.” He went on to suggest that the disconnect between the manufacturing industry and job growth is a result of automation. Though manufacturing volume is right now at its peak, U.S. manufacturing jobs are currently below the figures from the 1950s. He pointed to the 250,000 industrial robots in the U.S. and the increasing growth rate of their use.
What Vardi suggests will happen is “job polarization”, a phenomenon that emerges when high-skilled jobs demand complex human intelligence and low-skilled jobs are too expensive to automate. Thus, the middle ground jobs will be the easiest to automate, leading to greater economic inequality. Vardi also noted that although this issue is widely regarded as a threat that could make a huge impact on American economic life, there is no discussion of it in politics, particularly not in the presidential election. “We need to start thinking very seriously: What will humans do when machines can do almost everything?” he said. “We have to redefine the meaning of good life without work.”
Furthermore, Wendell Wallach, an ethicist at Yale University’s Interdisciplinary Center for Bioethics and the Hastings Center, said  “There’s a need for concerted actions to keep technology a good servant and not let it become a dangerous matter.” He also proposed that 10% of AI research funding should be put towards studying the impact that AI machines will have on society, echoing Vardi’s concern that politics has failed to address the tremendous issue. “We need strong, meaningful human control,” he said.

The Internet of Things (IoT) has the potential to fall into the general pit of buzzword-vagueness. Artificial intelligence (AI) often falls into the same trap, particularly with the advent of new terms such as "machine learning," "deep learning," "genetic algorithms," and more.

Episode Summary: Most of us forget that just about a decade ago, Facebook’s software was incapable of tagging people in a photo, but today can so without difficulty, sometimes without us even knowing. Machine vision has progressed to the point where it’s also common for computers to be able to pick out dogs from cats in images, another task that was not possible 10 years ago.

"Machine learning" is a term that's heard more often in startup and big data circles than "artificial intelligence", and interestingly enough, Google Trends confirms what's already heard through the technological grapevine:

Episode Summary: Dr. Ayers provides a comprehensive overview of his development of autonomous underwater robots, intended to help discover and destroy dangerous underwater land mines. He provides his perspective on two major obstacles facing robotics, including the concept of autonomy, providing valuable insight in light of the current events surrounding the development of autonomous AI.