A gap already exists between companies' ability to collect data and managers' skills at putting it to use. Will AI increase the divide? The use of artificial intelligence in the criminal justice system offers a stark example of the contrast between knowing how to produce results and knowing how to consume them intelligently. Systems recommend bail and sentencing but offer little transparency about the basis for the recommendation, leaving the humans who digest the recommendations potentially under informed. What if we knew so little about the production processes of the food we eat?

Deep learning has transformed the field of artificial intelligence, but the limitations of conventional computer hardware are already hindering progress. Researchers at MIT think their new "nanophotonic" processor could be the answer by carrying out deep learning at the speed of light. In the 1980s, scientists and engineers hailed optical computing as the next great revolution in information technology, but it turned out that bulky components like fiber optic cables and lenses didn't make for particularly robust or compact computers. In particular, they found it extremely challenging to make scalable optical logic gates, and therefore impractical to make general optical computers, according to MIT physics post-doc Yichen Shen. One thing light is good at, though, is multiplying matrices--arrays of numbers arranged in columns and rows.

Recently Sam Altman from Y Combinator announced a new online Startup School. The idea is to teach everyone how to start a startup and help them along the way with guidance from people who've started companies. Startup School will have guest speakers covering a wide variety of topics, including Alan Kay, Jan Koum (WhatsApp), Patrick Collison (Stripe), Steve Huffman (Reddit), Jason Lemkin (SaaStr), Harry Zhang (Lob), Dalton Caldwell (YC), Emmett Shear (Twitch), Adam D'Angelo (Quora), Alex Schultz (Facebook), Tracy Young (PlanGrid), Michael Seibel (YC), Kirsty Nathoo (YC), Vinod Khosla (Khosla Ventures), Ali Rowghani (YC), Jess Lee (Sequoia), and Aaron Harris (YC). Our company ChatBottle was one of a few chatbot startups enrolled in the course. Each group has its own mentor, usually a founder of a well-known company or a YC alumni.

According to Google Trends, machine learning has shown a steady (almost threefold) increase in interest since 2015. Coursera and Udacity machine learning courses are both in the top ten related topics. It appears that many people want to learn more about it. If you have ever used Google, Netflix, Amazon, Gmail, then you have interacted with machine learning (ML). It has become an important component in online retail, recommendation systems, fraud detection and others.

Anima Anandkumar pioneered the research of finding global optimal in non-convex problems, a big pain point in machine learning. Our protagonist for this week's Techie Tuesdays, Anima is an academician who represents the best of both worlds--industry and academia. She has contributed significantly to major AI and ML projects at Amazon. This will be a treat for all machine learning enthusiasts. In my two hours of conversation with Anima Anandkumar, Principal Scientist at Amazon Web Services, I've had the most potent dose of technical knowledge ever injected. Not that I didn't expect it while talking to an ex-faculty of UC Irvine (soon to be an endowed professor at Caltech), known for her research on non-convex problems (in deep learning). Our Techie Tuesdays protagonist of the week, Anima has worked towards establishing a strong collaboration between academia and industry. She follows an unconventional style of teaching, the one she would have loved as a student.

This talk aims to gently bridge the divide by demonstrating how deep learning operates on core machine learning concepts and getting attendees started coding deep neural networks using Google's TensorFlow library.

Creative Destruction Lab, a technology program affiliated with the University of Toronto's Rotman School of Management in Toronto, Canada hopes to nurture numerous quantum learning machine start-ups in only a few years. This still new form of hybrid computing combines the computational speed and power of quantum computers with machine learning, the technical term for AI like Siri or Alexa. Currently, researchers are mostly focused using the emergent technology of quantum computers to help machine learning programs to solve problems quicker or to use typical machine learning builds to add stability and potency to quantum computers. However, the end goal of either direction is to use many AI programs based on quantum computers to comprehend or better datasets and results from larger quantum calculations. Unfortunately, this goal will not be achieved until quantum computers are fully built and operational.

Editor's Note: Many TOPBOTS readers have asked us for advice on learning modern techniques like deep learning and getting jobs in AI. No one knows more about this subject than Rachel Thomas, deep learning researcher and co-founder of Fast.ai. Fast.ai is committed to democratizing practical AI education globally and offers popular MOOCs to get you ramped up fast. Without further ado, I'll let Rachel share her wisdom about starting a career in AI without a machine learning PhD. I was recently asked questions by two readers with diametrically opposed premises: one was excited that machine learning is now "automated" by services like Google Cloud, the other was concerned that machine learning takes too many years of prerequisite study, citing a popular Hacker News thread as his source.

"Musicians and artists are going to grab what works for them and I predict that the music that will be made will be misunderstood by many people," Eck, told WIRED at Sónar D, a showcase of music, creativity and technology held this week in Barcelona. At the event, which is twinned with the Sónar dance music festival, Google held an AI demonstration where Eck showed a series of basic, yet impressive musical clips produced using machine learning model that was able to predict what note should come next. "In the same way that Instagram has democratised the process of taking and editing photos, we'll see a similar progression towards making more people musical creators – using assistive AI to help people make good music," he told WIRED at a recent talk on AI at the London studio. The move to AI-based music creation tools will be "as big a technological shift as the digitisation of music," he predicted, albeit cautiously.

Artist Patrick Tresset is using robots to draw and create art. The robot classroom is part of his latest exhibition in London called Machine Studies.