If you missed our run-down of the 20-11 most clicked articles from the 2016 CognitionX Briefings, do not despair – they’re here!
2016’s Top 10 shows that our community understands the wide-ranging applicability of A.I. and machine learning. Our Top 10 covers topics from market overviews, to the boom in M&A activity, tutorials on open source frameworks, as well as the challenges of building world-class teams. Our most popular article showed how AI is set to change, shape and critique our time as well.
The most clicked article of 2016 CognitionX Newsletters is… (drum roll please)… The London based experiment, that used AI to rank the historical leaders who communicate most like the U.S. President Elect. “Trump similar to Stalin, Saddam? Artificial Intel says hell yeah!” highlights the similarities in the communication styles of both contemporary and historical leaders.
10. How to build High Performance Data Science Teams – Mike Hyde
November 23rd | CognitionX Events
Mike Hyde (former Director of Data & Insights Skype. Previously pioneered Big Data projects at: AlixPartners, Opera Solutions) gave our Community a briefing full of advice on how to build the best data science team.
He started by reminding us it’s all about Build Measure Learn…not Build Measure DENY!
9. Business Tech Predictions: 10 Ways AI, Big Data, and Cloud Will Evolve in 2017
December 16th | Business Impact of AI
A lot has been written about the convergence of cloud infrastructure, Big Data, and AI this year. These three factors are only growing more intertwined. Companies and experts from all over the industry discuss here about how the convergence will continue to play out, and how AI, cloud, and data technology will also continue to evolve and morph on their own. Salesforce are combining AI and data management with its Einstein platform. The cloud players themselves, such as Google Cloud Platform and Microsoft Azure, are employing an arsenal of cognitive computing tools and ML algorithms to redefine business clouds. Others still are inching closer and closer—through the combined power of AI, cloud, and Big Data—to truly map an AI brain.
8. Why your brain has a Jennifer Aniston cell’
June 22nd, 2005 | Feed your mind over lunch
Obsessed with reruns of the TV sitcom Friends? Well then you probably have at least one “Jennifer Aniston cell” in your brain, suggests research on the activity patterns of single neurons in memory-linked areas of the brain. The results point to a decades-old and dismissed theory tying single neurons to individual concepts and could help neuroscientists understand the elusive human memory.
7. The Current State of Machine Intelligence 3.0
7th November | Business Impact of AI
Shivon Zilis and James Cham, partners and founding members of Bloomberg Beta, release their view of the ML Landscape.
There are one third more companies than their original landscape two years ago, and it’s likely this is going to continuing accelerating. They explain in O’Reilly “The world will give us more open sourced and commercially available machine intelligence building blocks, there will be more data, there will be more people interested in learning these methods, and there will always be problems worth solving.” They go on to point out “the value of code is different from data, but what about the value of the model that code improves based on that data?”
6. European Machine Intelligence Landscape
September 20th | Innovation
Research group Project Juno released a comprehensive European AI landscape, highlighting the talent, expertise and innovation of Europe’s AI technology pioneers. These are the companies whose business is machine intelligence itself. Whether building engines specifically for vision, language, speech or general optimisation problems, or working on algorithms and techniques intended to have very broad applicability (Artificial General Intelligence), these companies invest in proprietary methods and expertise. Google Deep Mind is the most prominent example in this category, having applied its methods to such seemingly disparate tasks as mastering the millennia-old Chinese game Go, cutting data centre energy use, and synthesising natural human speech.
5. Top 10 Strategic Technology Trends for 2017
October 14th | Research
Gartner’s top 10 trends will drive the future of the intelligent digital mesh. Enterprise architecture and technology innovation leaders must prepare for the impacts of these disruptive trends on people, businesses and IT departments, and determine how they can provide competitive advantage.
4. From Good To Great: A Top 10 List For Becoming A 10x Data Scientist
November 29th | Education and advice we rate
Yael Garten, Director of Data Science at LinkedIn, shared the following list of behaviours and qualities that distinguishes exceptional data scientists from the average performers, as “Request context and envision the answer before you start the work.”
LinkedIn is committed to data science because much of the LinkedIn website consists of data products built in collaboration between data scientists and product managers by using profile information, the social graph connecting members and other entities, and website user engagement data to power models or recommendation systems. Examples include the Homepage Feed, PeopleYouMayKnow, Course recommendations, and JobsYouMayBeInterestedIn.
3. MNIST For ML Beginners
December 20th | Education advice and training we rate
When one learns how to program, there’s a tradition that the first thing you do is print “Hello World.” Just like programming has Hello World, machine learning has MNIST. In this tutorial, we’re going to train a model to look at images and predict what digits they are. Our goal isn’t to train a really elaborate model that achieves state-of-the-art performance — although we’ll give you code to do that later! — but rather to dip a toe into using TensorFlow. As such, we’re going to start with a very simple model, called a Softmax Regression.
The actual code for this tutorial is very short, and all the interesting stuff happens in just three lines. However, it is very important to understand the ideas behind it: both how TensorFlow works and the core machine learning concepts. Because of this, we are going to very carefully work through the code.
Nearly 140 private companies working to advance artificial intelligence technologies have been acquired since 2011, with over 40 acquisitions taking place in 2016 alone. Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce, are competing in the race to acquire private AI companies, with Samsung emerging as a new entrant in October with its acquisition of startup Viv Labs, which is developing a Siri-like AI assistant, and GE making 2 AI acquisitions in November. Google has been the most prominent global player, with 11 acquisitions in the category under its belt (follow all of Google’s M&A activity here through our real-time Google acquisitions tracker).
1. Trump Similar to Stalin, Saddam? Artificial Intel Says so
November 8th | Ethics question for the day
Gyana, an artificial intelligence (AI) company based out of London, used AI to compile a list of 10 world leaders who are similar to Donald Trump.
They took 1000 prominent world leaders from history and gathered all text around them – speeches, quotes, reports, news articles, etc. Artificial Intelligence analysed this data and threw up a ranking of leaders – both historical and contemporary- most similar to Trump.