How many researchers are expanding the capabilities of artificial intelligence (A.I.) systems? According to new data from Canadian startup Element AI, there are roughly 22,000 PhD-level researchers in the artificial intelligence field. "Element AI said it scoured LinkedIn for people who earned PhDs since 2015 and whose profiles also mentioned technical terms such as deep learning, artificial neural networks, computer vision, natural language processing or robotics," read the Bloomberg article breaking down the data. "In addition, to make the cut, people needed coding skills in programming languages such as Python, TensorFlow or Theano." That pool expanded to 90,000 researchers when Element AI incorporated PhDs earned before 2015, as well.

Earlier this month, I had the exciting opportunity to moderate a discussion between Professors Yann LeCun and Christopher Manning, titled "What innate priors should we build into the architecture of deep learning systems?" The event was a special installment of AI Salon, a discussion series held within the Stanford AI Lab that often features expert guests. This discussion topic – about the structural design decisions we build into our neural architectures, and how those correspond to certain assumptions and inductive biases – is an important one in AI right now. In fact, last year I highlighted "the return of linguistic structure" as one of the top four NLP Deep Learning research trends of 2017. On one side, Manning is a prominent advocate for incorporating more linguistic structure into deep learning systems.

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

There are new buzzwords on the net – AI, deep learning and machine learning. Although sometimes they're used interchangeably, deep learning and ML are just forms of AI. Today we're going to specifically look at both of them, as we cover AI basics. As a business owner, you've probably heard of these terms before but it sort of feels like a foreign language, unless you have a base understanding of the concepts. ML technology enables engineers to create programs or systems that can learn on their own over time with minimal training or input data, to begin with.

SYSTRAN and HarvardNLP are very pleased to hold the first OpenNMT Workshop in Paris on March 2nd at Station F, followed by the first ever OpenNMT Hackathon on March 3rd at Télécom ParisTech. OpenNMT is an Open Source project providing neural technologies for different tasks such as automatic machine translation, text generation and summarization. The OpenNMT project is a collection of implementations on multiple frameworks designed to be simple to use and easy to extend, while maintaining efficiency and state-of-the-art accuracy. Registration is FREE and OPEN to both the OpenNMT community as well as anyone interested in Deep Learning applications for natural language processing. During the daylong hackathon, we will provide hands-on training, but also development sessions to share good development practices and to kick off development of new features or interfaces.

Dr. Athanasios (Thanos) Gentimis, Assistant Professor of Math and Analytics at Florida Polytechnic University sees the future of analytics as a team effort, where subject matter experts (SMEs) collaborate in teams with Data Scientists and each team member plays to his or her strengths. He also sees a reliance on Deep Learning for creation of relevant questions and answers about data. Dr. Gentimis' field of expertise is Algebraic Topology, with a special interest in Geometric Group Theory. In a recent DATAVERSITY interview, Dr. Gentimis discussed how analytics is changing and what the future might look like. "If you go back into the 80s, analytics were performed on small data sets where somebody would come up with a survey," says Dr. Gentimis.

The technology of AI in the present times is witnessing'deep learning'. The futuristic applications such as self-driving cars rely heavily on deep learning and natural language processing.

ParallelM, one of the fastest growing new companies in machine learning management, today announced ParallelM MLOps, the first software solution for operationalizing machine learning (ML) and deep learning across the enterprise. Operationalizing machine learning poses a significant challenge, as current techniques have limited ability to tackle the unique intricacies of ML behavior patterns. Prevailing workarounds tend to be manual and brittle, inhibiting ML service scaling, and delaying the benefits of ML to the business. According to a recent survey of over 3,000 "AI-aware" C-level executives by McKinsey Global Institute, only 20 percent have deployed at least one AI technology and only 10 percent have deployed three or more. Further, out of 160 AI use cases examined, only 12 percent had progressed beyond the experimental stage.

As shown in the figure above, the computing power increased by 10,000 times since the year 2000. The cost of storing the data has also gone down by around 3000 times since the year 2000. There has been an exponential growth of data created due to the rise of the internet, the smartphone revolution and the social media. Data is ubiquitously available now. These three ingredients created a milieu for a perfect storm for deep learning.

For centuries, scientists have assembled and maintained extensive information on plants and stored it in what are known as herbaria -- vast numbers of cabinets and drawers – at natural history museums and research institutions across the globe. They've used them to discover and confirm the identity of organisms and catalog their characteristics. Over the past two decades, much of this data has been digitized, and this treasure of text, imagery and samples has become easier to share around the world. Now, complementary projects at the Smithsonian Institution in the U.S. and the Costa Rica Institute of Technology (ITCR) are tapping the combination of big data analytics, computer vision and GPUs to deepen science's access -- and understanding -- of botanical information. Their use of GPU-accelerated deep learning promises to hasten the work of researchers, who discover and describe about 2,000 species of plants each year, and need to compare them against the nearly 400,000 known species.