A lot of companies have job titles that include "Research" and/or "Scientist." Be aware that these tend to have stricter requirements on graduate degrees. But, also know that the same company may have Engineering roles which aren't as strict. Take a look at this pair from Recursion Pharma: Machine Learning Engineer vs Deep Learning Scientist. The requirement for a PhD is going to change over time. These techniques have been so cutting-edge that a graduate degree has been about the only way for employers to find candidates with a few years of experience.

December 27, 2016 --With excitement swirling around the possibility of a ninth planet, a rebound in the global tiger population for the first time in a century, and the DNA sequenced in space for the first time, 2016 has been a year full of scientific wonder. But as the year comes to a close, there are some breakthroughs particularly worth highlighting. In February, a century after Albert Einstein predicted their existence, an international team of researchers confirmed that they had actually detected a ripple in the fabric of spacetime for the first time. The detection of gravitational waves came across as a "chirp" across the detectors that make up the Laser Interferometer Gravitational-wave Observatory (LIGO), but the researchers say it was the result of two large celestial bodies, possibly black holes, colliding some 1.3 billion years ago. Then, in June, the scientists announced that the cosmos had chirped again.

As our desktop computers, laptops, mobile devices, etc. stand idly by for a huge portion of the day, the need for computing resources is growing at a fast pace. Large IoT ecosystems, machine learning and deep learning algorithms and other sophisticated solutions being deployed in every domain and industry are raising the demand for stronger cloud servers and more bandwidth to address the minute needs of enterprises and businesses. So how can we make a more economic and efficient use of all the computing power that's going to waste? Blockchain, the distributed ledger that's gaining traction across various domains, might have the answer to the dilemma by providing a platform that enables participants to lend and borrow computing resources -- and make money in the process. "There is a growing demand for computing power from industries and scientific communities to run large applications and process huge volumes of data," says Gilles Fedak, co-founder of iEx.ec, a distributed cloud computing platform.

In this AI Podcast, Host Michael Copeland speaks with NVIDIA's Will Ramey about the history behind today's AI boom and the key concepts you need to know to get your head around a technology that's reshaping the world.

Driving your car until it breaks down on the road is never anyone's favorite way to learn the need for routine maintenance. But preventive or scheduled maintenance checks often miss many of the problems that can come up. An Israeli startup has come up with a better idea: Use artificial intelligence to listen for early warning signs that a car might be nearing a breakdown. The service of 3DSignals, a startup based in Kefar Sava, Israel, relies on the artificial intelligence technique known as deep learning to understand the noise patterns of troubled machines and predict problems in advance. The startup has even chatted with companies about using their service to automatically detect problems in future taxi fleets of driverless cars.

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To companies grappling with complex data projects powered by artificial intelligence, a system that Nvidia calls an "AI supercomputer in a box" is a welcome development. Early customers of Nvidia's DGX-1, which combines machine-learning software with eight of the chip maker's highest-end graphics processing units (GPUs), say the system lets them train their analytical models faster, enables greater experimentation, and could facilitate breakthroughs in science, health care, and financial services. Data scientists have been leveraging GPUs to accelerate deep learning--an AI technique that mimics the way human brains process data--since 2012, but many say that current computing systems limit their work. Faster computers such as the DGX-1 promise to make deep-learning algorithms more powerful and let data scientists run deep-learning models that previously weren't possible. It costs $129,000, more than systems that companies could assemble themselves from individual components.

Key sectors of interest include Internet of Things, FinTech, Future of Work, Logis-cs/Transporta-on, eHealth, Security and others. BootstrapLabs is a Venture Capital firm based in Silicon Valley and focused on Applied Ar:ficial Intelligence About Us 3. Community of Founders, Intrapreneurs, AI/ML Experts, Execu-ves, Professors, Researchers, Investors focused on Innova-on, Technology and Entrepreneurship 30K PEOPLE Our Community 200K FOLLOWERS 1K ATTENDEES Our online community between BootstrapLabs core team and its closer advisors has over 200K followers. We see traffic on our website and deal flow referral coming from over 60 countries BootstrapLabs brought together over 1,000 people during 2016. Our community is a key pillar of our success and we organize many exclusive private and public AI centric events each year 4. Applied AI Digest #1 2016 Google's DeepMind Beats a Top Player at the Game of Go Zucks to create AI-Powered Jarvis JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC IBM Watson Head on the Future of AI Read Full Articles 5. Applied AI Digest #2 Artificial Intelligence Deals on the RiseCould AI Solve the World's Biggest Problems? Harvard is building an AI Engine as fast as the Brain 6. Applied AI Digest #3 Is Big Data Still a Thing?

"If the consumer visits the store again and takes a picture with the machine, it will be able to recognise his or her face and show the previous purchase history. It will also be able to remember customer's dining habits, and help place an order faster," Wu Zhongqin, Deputy Director, Institute of Deep Learning, Baidu Inc. With another machine with an augmented reality, or AR function, customers are able to interact with the machine, change facial expressions by shaking their heads in front of the machine, take photos, and save them to their phones.

There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human. I am certain that healthcare will be the lead industrial area of such a revolution and one of the major catalysts for change is going to be artificial intelligence. With the evolution of digital capacity, more and more data is produced and stored in the digital space. The amount of available digital data is growing by a mind-blowing speed, doubling every two year. In 2013, it encompassed 4.4 zettabytes, however by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes (!).