AI via machine learning and deep learning drives the “AI first” strategy penetrating enterprises. Embrace it to live-long and prosper!

What’s the significance?

In every conversation with government, industry, media, and academia, it is a consistent focus of attention. With businesses and startups, it’s a must for existence. For example, the Financial Services Roundtable (FSR) consists of the top 100 CEOs in US financial services representing 92.7 trillion USD in managed assets and 1.2 trillion USD in revenues. For context, the GDP output of the planet is 80 trillion USD.

In the FSR upcoming futures summit FinTech Ideas Festival, AI is a key theme. AI is a catalyst behind the best sellers 4th Industrial Revolution explained in a book by Professor Klaus Swab, founder and executive chairman of the World Economic Forum and with the Second Machine Age from MIT professors Brynjolfsson and McAfee. UBS talks about the implications of AI in their excellent white paper for the World Economic Forum. From the paper, the sectors having the highest productivity and highest disruption from AI are IT services and autos. There is the IBM Watson AI XPRIZE challenging teams from around the world to solve the world’s greatest challenges using AI with three finalist teams (perhaps even pure AI and robots) taking the stage at TED2020. OpenAI a non-profit with 1 billion USD in funding, has opened their OpenAI Gym to the world to experiment with machine learning supporting open source tools such as TensorFlow and Theano plus virtual robots, video and board games for testing.

The evidence is wide-spread with enterprise capable machine learning solutions and tools from Google, Microsoft, IBM, Amazon, and other major players. In addition, a proliferation of hardware solutions from NVIDIA, Movidius, IBM, Qualcomm, Samsung, and Chinese startup Horizon Robotics building chips.

This leads us to Google and parent Alphabet and their daily announcements centered on their AI-first strategy building on previous generations of cloud first and mobile first. A small sampling of their remarkable achievements leading the news: self-driving cars, Google Brain, Deep Mind, RankBrain, AlphaGo beating Lea Seedol, release of TensorFlo to open source, TPU (Tensor Processing Unit).

Meet Jeff Dean

Jeff Dean is one of Google’s first hires in 1999 and the legendary scientist driving all major developments within Google and, when he publishes, the world. The citation from the ACM InfoSys Foundation Award computing’s highest honour for young scientists illustrates this point. There is more in my 2013 chat with Jeff where he foreshadows the rapid gains with machine learning spotlighted in the 2016 July 7 ACM talk with link below.

Jeffrey Dean and Sanjay Ghemawat are the recipients of the 2012 ACM-Infosys Foundation Award in the Computing Sciences [contemporary innovation that exemplifies the greatest recent achievements in the computing field].”

“In their influential 2004 research paper, MapReduce: Simplified Data Processing on Large Clusters, the concept is used to power massive online applications by splitting the work into small pieces and spreading them across thousands of machines…This approach has led to the advent of cloud computing, where computing power is provided as a utility to consumers, with all the hardware and implementation details abstracted away…Since then, they have published a series of instrumental papers which have inspired a generation of systems researchers and generated considerable follow-on work in the field of distributed computing. The open publication of their advances has allowed others to build on their work. An example is Hadoop, the open source project that is widely used by many companies as well as academic researchers and educators.”

Further illustrating Jeff’s influence, he has co-designed/implemented five generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, systems infrastructure for statistical machine translation, and a variety of internal and external libraries and developer tools. He is currently working on large-scale distributed systems for machine learning. Jeff is currently a Google Senior Fellow in Google’s Research Group, where he leads Google’s deep learning research team in Mountain View, working on systems for speech recognition, computer vision, language understanding, and various predictive tasks.

This spotlights a unique opportunity for enterprises to come up to speed with machine learning with an upcoming free talk and Q&A hosted by the non-profit ACM.

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