A machine learning model is itself parameterised by a large number of different parameters (e.g., learning rate, number of hidden units, strength of weight regularization). How you set these hyper-parameters can have a big impact on the overall results achieved, but finding an optimal set of hyper-parameters is far from easy. Essentially it boils down to picking some sets of parameters and trying them to see how well they work. How do you choose which sets to pick though? Even with a relatively small number of parameters it's impossible to do an exhaustive search as the search space grows exponentially with the number of hyper-parameters.

Today's artificial intelligence market is not easy to quantify. Besides the lack of consensus on a coherent definition for "artificial intelligence" as a term, the field's nascent stage of development makes it difficult to carve out silos or hard barriers of where one industry or application ends, and another begins. In one of our more popular recent articles, we aimed to derive a valuation of the artificial intelligence market, based on current market research and our own insights. In this week's article, I've set out to determine more of a "lay of the land" of the AI industry, including it's various segments and application areas. If you're interested in how developments in machine learning and AI might impact your own company or business, then keeping an eye on trends of industry and application growth is pertinent; we hope that this article will be a good jumping off point to some of the most thought-out assessments of AI and it's "segments" as we could collate from the web.

Welcome to the final installment of our 4-part blog series. If you want to get started right now, download the complete Deep Learning and Artificial Intelligence white paper. If you haven't read part 3, it's worth visiting that post to learn more about the key considerations when selecting a database for new deep learning projects. As the following section demonstrates, developers and data scientists can harness MongoDB as a flexible, scalable, and performant distributed database to meet the rigors of AI application development. MongoDB's document data model makes it easy for developers and data scientists to store and combine data of any structure within the database, without giving up sophisticated validation rules to govern data quality.

I'm standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence. We're in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of "deep learning," the technique behind the current excitement about AI. "In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Of the researchers at the top of the field of deep learning, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team.

AI growth has dominated most of last year's technology news, from algorithms learning how to play Go without human input, through Elon Musk and Mark Zuckerberg disputing the dangers and benefits of AI to humanity, down to Russia and China declaring it to be a top priority. Looking ahead, 2018 will be a year of developing and deepening all available AI technologies. Deep learning, one of the most important subfields of research on artificial intelligence, will be especially promising. Generally speaking, the goal of artificial intelligence is to make computers as smart, or even smarter than human beings, by giving them human-like thinking and reasoning abilities. Among the many ways to achieve this, machine learning AI is the baseline technique, used widely across all industries.

Insights into the new computing model DEEP LEARNING TOP 5 October 6, 2017 DEEP LEARNING IS THE FASTEST-GROWING FIELD IN ARTIFICIAL INTELLIGENCE (AI) AS AI TECHNOLOGIES CONTINUE TO IMPROVE, MORE COMPANIES ADOPT DEEP LEARNING TO ACCELERATE THEIR BUSINESSES… TOP 5 1. Gartner releases the top 10 strategic technology trends for 2018 2. Oracle adds GPU Accelerated Computing to Oracle Cloud Infrastructure 3. Chemistry and physics Nobel Prizes awarded to teams supported by GPUs 4. MIT uses deep learning to help guide decisions in ICU 5. Portfolio management firms are using AI to seek alpha GARTNER RELEASES THE TOP 10 STRATEGIC TECH TRENDS FOR 2018 Gartner, Inc. announced its top strategic tech trends and predictions at the 2017 Gartner Symposium this week. "The first three strategic tech trends explore how AI and machine learning are seeping into virtually everything and represent a major battleground for technology providers over the next five years. READ ARTICLE ORACLE ADDS GPU ACCELERATED COMPUTING TO ORACLE CLOUD INFRASTRUCTURE Oracle announced at Oracle OpenWorld this week it is now offering NVIDIA's P100 GPU instances in its public cloud, with plans to add the more powerful V100 GPUs in the near future. "This is the first time Oracle has offered access to GPU acceleration, reflecting an industry-wide move to provide access to cloud hardware optimized for artificial intelligence and machine learning.

What will IT look like a decade from now? We've seen enormous growth in big data and analytics in the past decade, and this area of technology will continue to transform how business is done. Not just that -- but machine learning breakthroughs will bring new ways of analyzing and using that data. To start us off thinking about the future of IT, let's divide IT into three segments based on how problems are solved: by manually coding a model, by collecting and comparing lots of examples, or by automatic modelling (AI and deep learning). Procedural data processing is what we've traditionally been doing in IT: Solving a problem by modelling its parameters, collecting the relevant data and calculating a singular result. We call it structured processing or "if…then" logic.

Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE's Avitas Systems invested in deep learning supercomputers. These are a few of many milestones in 2017.

The inaugural edition of the Talking Data podcast for 2018 features James Kobielus, analyst, Wikibon, who helps us take the racing pulse of data today. AI, machine learning, deep learning and analytics all come in for consideration. Buckle your seat belt, listen to the podcast and get ready for another tumultuous ride down the big data slope.

In recent months, Microsoft, Google, Apple, Facebook, and other entities have declared that we no longer live in a mobile-first world. Instead, it's an artificial intelligence-first world where digital assistants and other services will be your primary source of information and getting tasks done. Your typical smartphone or PC are now your secondary go-getters. Backing this new frontier are two terms you'll likely hear often: machine learning and deep learning. These are two methods in "teaching" artificial intelligence to perform tasks, but their uses goes way beyond creating smart assistants.