Imagine training a neural network and watching its representations wander through this space. You can see how your representations compare to other "landmark" representations from past experiments. If your model's first layer representation is in the same place a really successful model's was during training, that's a good sign! If it's veering off towards a cluster you know had too high learning rates, you know you should lower it. This can give us qualitative feedback during neural network training.

Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as "cognitive computing" or "machine intelligence", while others incorrectly interchange AI with "machine learning". This is in part because AI is not one technology. It is in fact a broad field constituted of many disciplines, ranging from robotics to machine learning. The ultimate goal of AI, most of us affirm, is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence.

Shivaram Kalyanakrishnan is an assistant professor in the department of computer science and engineering at the Indian Institute of Technology-Bombay. He specialises in artificial intelligence (AI) and is the only author from India who is part of an 18-member study panel of the Stanford University-hosted report titled Artificial Intelligence and Life. Kalyanakrishnan's expertise broadly fits in the area of machine learning. Called reinforcement learning, it defines what actions software agents should take to maximize a certain type of reward after learning from reward and punishment. In an interview, he urges people to be more optimistic about the things AI can do rather than be obsessed with the fear around AI machines.

Artificial intelligence (AI) can be understood as a science, engineering and deployment of machines, which perform tasks with intelligence as similar to humans. Since its inception 60 years ago, AI has observed significant growth in recent years. Initially, AI was considered as topic for academicians, though in recent years with development of various technologies, AI has turned into reality and is influencing many lives and businesses. Additionally, evolution of various other supplementary technologies such as cloud computing, machine learning and cognitive computing are collectively paving the growth of the market for AI. Many IT giants and start-ups are investing heavily in development of AI software solutions and hardware products. Some the prominent players in AI market in the recent times are Intel Corporation (U.S.), Google Inc. (U.S.), Microsoft Corporation (U.S.), Amazon.com,

With the explosion of Big Data and Analytics there are several related terms that are being used frequently that may not be completely understood. Below are definitions and details for a few of the high profile terms. Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends as defined by Webopedia. Predictive analytics provides probabilities of results not guarantees. Predictive Analytics of one sort or another has been done for decades through tools like SAS and SPSS. There are more contemporary solutions as exemplified by companies like Alpine Data Labs and KNIME.

Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. Even though model training and inference might be useful, for some areas that have complex simulation-driven workflows, there are great efficiencies that could come from deep neural nets, but integrating those elements is difficult. The oil and gas industry is one area where deep learning holds promise, at least in theory. For some steps in the resource discovery workflow, deep learning could lead to faster and more accurate results for potential discovery zones. Reservoir characterization is a critical step in this discovery process and is currently a hot area for explorations into how deep learning might be applied.

Over 1,000 attendees, 50 speakers and 30 exhibitors; this is a brief summary of what I was lucky enough to take part in during the first AI Europe 2016 conference held in London on the 5 and 6 December. The attendee list boasted the biggest names from the world of artificial intelligence such as Microsoft, Dell, Uber, Samsung and Nvidia, as well as several innovative start-ups, the likes of Blippar and DreamQuark whose innovations are based on machine or deep learning models. Even if we can say with a degree of certainty that further advances in artificial intelligence are yet to come, leading players are in agreement that most AI techniques and technologies are now well-advanced. Therefore, their major preoccupation today is more about the quality of the data sets being used to train and validate their machine and deep learning models. Whether it's Dell or Uber, Microsoft or Blippar, they all have one thing in common: they all agree on the fact that as of now, the quality of the data used in AI for machine learning is of the utmost importance.

Artificial intelligence is one of the hottest technology trends for 2017. And perhaps no company in the AI sector is hotter than NVIDIA, which has pushed from the desktop into the data center, evolving into a major player in high performance computing. NVIDIA's graphics processing (GPU) technology has been one of the biggest beneficiaries of the rise of specialized computing, gaining traction with workloads in supercomputing, artificial intelligence (AI) and connected cars. This trend is expected to accelerate in 2017, with more custom chips being introduced to target these workloads. After building a major beachhead in hyperscale data centers, NVIDIA's ambitions now extend to the enterprise data center. The company's new DGX-1 Deep Learning System is a "supercomputer in a box" – a hardware appliance designed to make AI data crunching more accessible.

Quoc Le, a software engineer at Google Brain, is one such human. Google Brain focuses on "deep learning," a part of artificial intelligence. Think of it as a sophisticated type of machine learning, which is the science of getting computers to learn from data. Deep learning uses multiple layers of algorithms, called neural networks, to process images, text and sentiments quickly and efficiently. The idea is for machines to eventually be able to make decisions as humans do.

Every post I read that says it is going to explain the relationship between artificial intelligence, machine learning, and deep learning goes way off the deep end into extremely technical terms and huge amounts of detail. So I decided to take a crack at a simple 2-minute explainer. Machine learning: Enabling computers to learn, usually "on their own," often with big datasets. Deep learning: Building multiple layers of abstractions on datasets to construct higher-level meaning. Sometimes, of course, it's easier to understand by walking through an example.