… my view of where technology innovation will lead us



A totally Must-Read on what AI can do right now and what not: The Business of Artificial Intelligence (Erik Brynjolfsson, Andrew McAfee)

And again… if you read one Artificial Intelligence article this month, make it this one. I highly value Erik Brynjolfsson since I saw his TED talk (here is also a great recap and interview with him in the NPR TED Radio Hour “The Digital Industrial Revolution”). Together with with fellow MIT principal research scientist Andrew McAfee he draws a sharp and easy to grasp picture of what machine learning is really capable of today and what the outlook for the future is. This is really worth your time, providing first class insights into “no bullshit” artificial intelligence state of the nation. Here’s an intro… follow the link at the end of this post for the full article on Harvard Business Review:

“For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available…”

via The Business of Artificial Intelligence (@Harvard Business Review)

This is impressive progress made possible by Machine Learning: Google’s speech recognition technology now has a 4.9% word error rate (Emil Protalinski)

Google CEO Sundar Pichai today announced that the company’s speech recognition technology has now achieved a 4.9 percent word error rate. Put another way, Google transcribes every 20th word incorrectly. That’s a big improvement from the 23 percent the company saw in 2013 and the 8 percent it shared two years ago at I/O 2015.

The tidbit was revealed at Google’s I/O 2017 developer conference, where a big emphasis is on artificial intelligence. Deep learning, a type of AI, is used to achieve accurate image recognition and speech recognition. The method involves ingesting lots of data to train systems called neural networks, and then feeding new data to those systems in an attempt to make predictions…


Source: Google’s speech recognition technology now has a 4.9% word error rate | VentureBeat | Dev | by Emil Protalinski

Really insightful Long-Read: Alien Knowledge – When Machines justify Knowledge (David Weinberger)

The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

So wrote Wired’s Chris Anderson in 2008. It kicked up a little storm at the time, as Anderson, the magazine’s editor, undoubtedly intended. For example, an article in a journal of molecular biology asked, “…if we stop looking for models and hypotheses, are we still really doing science?” The answer clearly was supposed to be: “No.”

But today — not even a decade since Anderson’s article — the controversy sounds quaint. Advances in computer software, enabled by our newly capacious, networked hardware, are enabling computers not only to start without models — rule sets that express how the elements of a system affect one another — but to generate their own, albeit ones that may not look much like what humans would create. It’s even becoming a standard method, as any self-respecting tech company has now adopted a “machine-learning first” ethic…

via Our Machines Now Have Knowledge We’ll Never Understand

Where accelerating technological Development will lead us in the next 15-20 years (André Cramer)

I would like to share some of my thoughts on key developments that I believe will determine our lives in the upcoming two decades. Almost all of this is fueled by ever more accelerating technological progress and there are a lot of opportunities in it. As well as significant challenges.

Looking back at the perceived principle of the industrial age, where growth occurred or seemed to occur in a linear function, today we know about Moore’s Law. We have been able to observe it for the last 50 years where over time it became clearer that we have a doubling of computing power roughly every 1,5 years.

Now how does that apply in our everyday life? Where do we actually see that technologies get more and more “disruptive”? To show that this is not about buzzwords, here are a couple of examples for “wow” type of developments: Continue reading “Where accelerating technological Development will lead us in the next 15-20 years (André Cramer)”

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Why AI and machine learning need to be part of your digital transformation plans (Alison DeNisco)

AI and machine learning promise to not only improve the customer experience, but also change the way companies operate. For this reason, enterprises should consider integrating these technologies into digital transformation plans to stay competitive. By 2019, 40 percent of all digital transformation initiatives will be supported by cognitive/AI capabilities, according to IDC…

Source: Why AI and machine learning need to be part of your digital transformation plans | ZDNet

An Exclusive Look at How AI and Machine Learning Work at Apple (Steven Levy)

The iBrain is here — and it’s already inside your phone…

On July 30, 2014, Siri had a brain transplant. Three years earlier, Apple had been the first major tech company to integrate a smart assistant into its operating system. Siri was the company’s adaptation of a standalone app it had purchased, along with the team that created it, in 2010. Initial reviews were ecstatic, but over the next few months and years, users became impatient with its shortcomings. All too often, it erroneously interpreted commands. Tweaks wouldn’t fix it…

Source: An Exclusive Look at How AI and Machine Learning Work at Apple – Backchannel

Awesome & insightful Q&A Session with Yann LeCun, Director of AI Research at Facebook and Professor at NYU (Quora)

What are some recent and potentially upcoming breakthroughs in deep learning?

There are many interesting recent development in deep learning, probably too many for me to describe them all here. But there are a few ideas that caught my attention enough for me to get personally involved in research projects.

The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This is an idea that was originally proposed by Ian Goodfellow when he was a student with Yoshua Bengio at the University of Montreal (he since moved to Google Brain and recently to OpenAI)

This, and the variations that are now being proposed is the …


Source: Yann LeCun – Session on Jul 28, 2016 – Quora

Awesome Compilation: Getting Up to Speed on Deep Learning – 20+ Resources (Isaac Madan, David Dindi)

For good reason, deep learning is increasingly capturing mainstream attention. Just recently, on March 15th, Google DeepMind’s AlphaGo AI — technology based on deep neural networks — beat Lee Sedol, one of the world’s best Go players, in a professional Go match.
Behind the scenes, deep learning is an active, fast-paced research area that’s proliferating quickly among some of the world’s most innovative companies. We are asked frequently about our favorite resources to get up to speed on deep learning and follow its rapid developments. As such, we’ve outlined below some of our favorite resources. While certainly not comprehensive, there’s a lot here…

Source: Getting Up to Speed on Deep Learning: 20+ Resources — Life Learning — Medium

AI is absolutely essential for the Messaging Platform Business Model to take over the World (of B2C) (André Cramer)

Over the course of the last weeks and months you couldn’t escape news and stories about messaging platforms going after B2C use cases à la “order me some food”, “book me a hotel room” or “I need a ride downtown in 30 minutes”.

Pioneered and taken to huge success in Asia by platforms like Weixin/WeChatLINE and Kakao, especially Facebook with its two behemoth platforms Messenger and WhatsApp is taking decisive actions to bring businesses and consumers together on their platforms. Kik is even faster, having just launched such a botstore for brands. In their launch line-up are 18 well-known brands such as Sephora, H&M or The Weather Channel. And with these moves, communications platforms will tap into significant revenue streams in the form of rev shares and commissions for being the facilitator between businesses and consumers in everyday Transactions.
Continue reading “AI is absolutely essential for the Messaging Platform Business Model to take over the World (of B2C) (André Cramer)”

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