Artificial Intelligence, Machine Learning

Oh, Look! There’s a Revolution Going ON!

September 27, 2017

The Fourth Industrial Revolution is Here

Yoga Pants:$300 by Machine Learning

IBM achieves a breakthrough: Deep Learning on Steriods

Preparing for the Transition to Applied Artificial Intelligence

Jobs for Americans: A Lesson from Germany

Artificial Intelligence in Self-Driving Cars

IBM’S Watson at the US Open: Tennis for Robots?

“Stop Pretending You Know What Artifificial Intelligence Is and Read This:

China: Robots and Intelligence

Machine Learning Drives Trucking Job Loss

Machine Learning drives Self-Driving truck preparations and could cause “Big Time” Job Loss for Drivers.

Artificial Intelligence has put the self-driving technology into the fast lane. Machine Learning will prevent thousands if traffic accidents each year. It will also save millions of gallons of fuel each year. The savings from keeping self-driving trucks running at consistent speeds will be tremendous.

Commuters will save countless hours of driving each day. Some say that drive times that are now over 2 hours will be delighted.

It will cost approximately 15,000 Americans their jobs. This is an addition to the millions of jobs lost in the last decade. Autonomous vehicles are a tremendous opportunity but will cause a big-time workforce issue. Eliminating economic hardships that disrupt the least number of people is essential. The big question: how do we make sure we’re planning far enough ahead. These disruptions can also create job opportunities for people. This remains a pertinent question that requires an answer.

These $300 Yoga Pants by Machine Learning

Billie Whitehouse is famous for pushing boundaries. Her first product blending fashion and technology: NadiX yoga pants. The pants have five sensors sewn in to help the wearer improve form for 30 different yoga poses. Once in a pose, the sensors vibrate in specific ways to tell you how to adjust your hips, knees or ankles. For the downward dog pose, for instance, the pulses guide a person to ground the ankle and lift the knee.  The sensors highlight those micro muscles that you didn’t even know existed.

The idea seemed impossible to execute. Put electronics into garments still so new and so difficult? This was an engineer’s nightmare. The pants have removable, rechargeable batteries that last up to 90 minutes. The battery connects via Bluetooth to a smartphone app. People then choose the level of yoga they’re going to be practicing. Myth turned reality don’t you think?

IBM Achieved a Deep Learning Breakthrough

IBM engineers have developed Artificial Intelligence software that is faster. This makes the entire machine learning process faster and easier. Complex deep learning models on a single server will not be an issue. IBM managed to scale up distributed deep learning. Instead of taking days for a deep learning network to process models, it could now take only hours or even less. Wait time with deep learning training from days or hours to minutes or seconds. This also enables improved accuracy. Some individuals think that these Artificial Intelligence models are an achievement. Others think they seem too good to be true.

Preparing for the Transition to Applied Artificial Intelligence

Machine Learning gains more footholds in applications every day. It has become a topic that many engineers want to master.  Machine Learning solutions require more than training an arbitrary model on your data. It requires an understanding of the type of data you have. How and what biases your data contains. The statistical models need understanding. Is the model applicable to your particular dataset? The success of this model requires knowledge of the metrics to optimize your model’s output. Certain skills required before transitioning to Applied Artificial Intelligence include:

Statistics: understanding Machine Learning, requires solid knowledge of statistics fundamentals.

Concepts of Machine Learning Theory: is an understanding of the range of how different loss functions work. An understanding of why backpropagation is useful, and what a computational graph is.

Data wrangling is important for Applied Artificial Intelligence. The success of the model correlates with the quality and quantity of your data. The old saying: GARBAGE IN – GARBAGE OUT is true. Especially with the amounts of data and the speed of this software churns through it.

Debugging/Tuning models requires that a solid knowledge of the fundamentals is essential. The right architecture and parameters require solid theoretical fundamentals. Good infrastructure work is necessary to be able to test different configurations.

Software Engineering is in the mix. Applied Machine Learning will allow you to leverage Software Engineering skills. Sometimes this requires a little twist. Mastery of these concepts and skills is Artificial Intelligence.

Jobs for Americans: A Lesson from Germany

Germany is one of the world’s leading models of workforce development efforts. Apprenticeship models that combine on the job paid experience and classroom learning. This equips the students and sets them up to compete for jobs out of school. The apprenticeship programs are responsible for this. The unemployment rate for German youth is one of the lowest in any of the world’s advanced economies. Compare the rate of 6.5 percent as of January 2017. The estimate is 11.5 percent in the U.S.  The U.S. has seen a sudden increase in its own apprenticeship programs. These programs are working to prepare students for careers in middle-skill jobs.  German apprenticeship continues as a model for employment growth in the United States.  Support in public and private sectors for apprenticeship efforts will contribute to economic growth in the U.S.

Artificial Intelligence in SELF-DRIVING CARS

Saturday night rideshare doesn’t sound like fun. An ad appears touting a new restaurant over on the other side of town- free ride!

Why pay when you can get it for free?

Self-driving cars to restaurants, bars, pizza joints. Owners will be happy to provide the rides!

It might even be an answer to the dropping sales because people don’t want to go outside especially in winter.

What is going to happen to the Infrastructure? Cabs and public transportation will no doubt feel the impact.

Costs will be so low that businesses will provide rides right to their door. Car dealerships – buy a car? Why?

Hod Lipson is the author of “Driverless, Intelligent Cars and the Road Ahead”.  He is also an engineering professor at Columbia University. He predicts that innovation will bring an explosion of growth.

IBM’S WATSON brings Cognitive Highlights to Tech at the US Open

IBM launches Watson Media, a suite of solutions powered by Artificial Intelligence. Watson has the ability to analyze images, video, and language at the US Open.

Cognitive Highlights To recognize important match moments

Slam Tracker Keeps up with the scores, stats and other insights

IBM Cloud uses years of Slam Tracker Data’s  MACHINE LEARNING to understand player’s styles.

Elizabeth O’Brien is IBM Program Director of Sports and Entertainment Partnerships. Watson is at the center of a massive data analysis during the tournament. The idea is to take Watson’s capabilities to improve the fan experience. A way for the tournament to earn revenue is also a plus.

She explained that “If you are sitting there, there are 17 courts of play going on. Every point on each of the 17 courts is a potential highlight. So, if you’re a broadcaster and you have a highlight, you’re looking at thousands of potential highlights. Watson can look at them ‘These are the potential highlights’.  Watson is suggesting the potential solution that you are looking for!

It is debuting at the Open this year but Watson Media has been around for some time. Wimbledon and the Master’s golf tournament have been venues for Watson also.

Within the app is also a high-powered chatbot that IBM calls the ‘Cognitive Concierge’. The Artificial Intelligent chatbot uses natural language. It processes questions about food or drinks that someone might have during a match. Last year Watson answered 56,000 questions. This year on the pre-opening day it fielded 4,000 questions and on the first day answered 8,000. Questions are: where is a particular player is playing. Where to get tickets and what parts of the venue tickets give attendees access to are also questions.

Ms. O’brien said that IBM is watching the questions that are being asked and learning what people want to know. This is increasing the body of knowledge and the questions Watson can answer.

Watson generates revenue for the USTA. Watson is building engagement with tennis fans around the world via the USTA app.

Heineken brings highlights to Twitter. Mercedes-Benz and American Express are also sponsoring content through the Concierge.

London was the most popular city source for visitors with 39 percent. Montreal (31 percent), San Jose (26 percent) and Tokyo (4 percent) follow.   The numbers by country:  the US was the most popular followed by UK, Canada, Germany, Italy, and Australia.

The Open has 700,000 fans go thru the event over the course of two weeks. Lew Sherr, the USTA’s chief revenue officer said that compares to a “Knick’s season, a Jet’s season. It’s an NHL, NBA season packed in two weeks”. Watson’s universe is hundreds of millions of interactions with fans.

Roger Federer scores a point at Arthur Ashe Stadium and Watson recognizes that its a highlight and alerts the USTA. The USTA then blasts video out to fans via USTA’s social medium feeds. Executives from IBM and USTA say that Watson allows them to review highlights faster. The highlights then get to the fans faster. USTA fans have a ‘sense of what is happening point by point”.

IBM has been teaching Watson that, a fist to the face of Roger with a bent elbow, means a celebratory moment. It cross-references this data with the roars of the crowd. Add in statistical analysis and Watson determines that this is one of the best moments of the match. Watson auto generates highlights which simplify the work of the video production crew. It process and sets up the highlight-reel creation fo USTA. Noah Syken, who is IBM VP of Sports and Entertainment Partnerships. He says that so much action is being played that even the fastest video team has trouble keeping pace. There are 22 courts; 4 ‘show’ courts, 13 field courts and 5 practice courts. Watson is ‘watching’ the games in the same way that it ‘reads’ X-rays and MRIs scans for doctors. (

This is the first year that USTA is able to create experiences that will drive fan engagement. Watson is generating a “Highlight of the Day” and posts it on USTA’s Facebook page. Video highlights, player bio pages will appear at the IBM Watson Experience on the US Open Plaza.

IBM has ambitions to for the technology to grow over time. It is a will a tool for broadcasters or a tool to help develop player skills during training. Slam Tracker is the official scoring app for the US Open. It provides real-time scores, statistics, and point-by-point analysis. It also looks data about player and ball position. In January USTA opened a 64 acre, $63 million training facility in Orlando. The goal is to help in developing future American grown tennis champions.

Contributor Author Marty Swant ( He is a technology staff writer for Adweek. He specializes in digital marketing trends and social platforms. He also specializes in ad tech and virtual reality and artificial intelligence. Jen Booten is a senior writer at Sporttechie. She covers the many ways that technology is disrupting sports. ( sources for information are:

Digital Transformations collaboration and new business models ( Time for Brands is a firm that teaches how to grow again.(

signal-2655654__480 LIGHTHOUSE

My ‘in your face segment’

“Stop Pretending you know what ArtificiaI is and read this instead”

“You have probably heard the news: AI is going to take your job. Wait, No, It’s going to create a new job for you. AI is going to kill us all! Wait, AI is totally smarter than us at, like, all smart things. But that probably doesn’t matter? Neural Networks. Machine Learning. Deep Learning. OMG. HELP!”

What are we talking about? We may not be talking, arguing, thinking about the same things! Nah, that NEVER happens. Let’s check in with someone that KNOWS!

Harvard Professor Leslie Valiant to the rescue: “You have a right to be confused.”

The terms Artificial Intelligence and Machine Learning are being misused in the popular press. Trevor Darrell is a leading researcher at UC Berkley. He is a part of the DARPA-funded artificial intelligence research project of emerging technologies for use by the military.darpa 2-Optimized

You are right. Professor Valiant continues: “There is no precise distinction-they overlap.”

“Artificial intelligence,” he says, “is the general name for a field of study-the study of whatever answer to the question might be.” HUH? WHAT? I thought we were going to be deconfused?

OK. “Answer the question: “what are the requirements for artificial intelligence?”

“AI is more like a goal than a thing.” Technically speaking it isn’t an ‘it’ at all. Think of a big box. In the box, we put foggy AI and lots of ‘things’ like – machine learning, deep learning, neural networks. Now these ‘things’ have something to chew on, some edges surrounding them. They are a precise name for various scientific, mathematical and engineering methods. These are methods that people use the fields of AI.

The phrase “artificial intelligence” sounds straight and above board. This phrase that clouds the issue of precision in discussing science. Elon Musk, the guy from SpaceX who is landing first stage rockets back on a platform in the ocean and reusing them. The CEO of Tesla Ince; the co-chair of Open AI; the CEO of Neuralink. The founder co-founder and chairman of Solar City. The co-founder of Zip2; the founder of which merged with Confinity and later took the name PayPal. Yes HIM. He’s the guy that calls AI “the demon”. Who claims that “AI”, not a nuclear bomb, not white separatists and KKK “is what we should fear most.” It falls somewhere between ‘smart as a puppy’ and some subset of a cockroaches’ brain. (Stephen Hawkings agrees with the premise – more on that later.)

“George Orwell proclaimed, “the slovenliness of our language makes it easier for us to have foolish thoughts.” Hmm, going to the scientists didn’t help much did it? Google CEO Sandi Pichai (he is smart, watched his TED talk the other day) and Elon Musk maker of ‘a self-driving car’ and space vehicles are famous businessmen (Elon just sold 145,000 self-driving semi-trailer trucks to UPS!). Elon makes machines capable of speech. He is cornering the market on ‘things to fear (his words)  that are going to take our jobs away. Doesn’t help much, does it? Elon says that we have more to fear from AI than anything else.

We should avoid using ‘artificial’ and ‘intelligence’ together. We have to go back to the beginning of computer lingo and ask Alan Touring. Yes, the guy who defined what a computer is. He thought that defining’ intelligence’ was too hard. He favored describing it by “intelligence is as intelligence does.” Does that help?

Invent more ‘things’ made with machine learning, deep learning and, neural networks. Then we can point to one ‘thing’ and say ‘smartphone’. See what I’m saying? The ‘thing’ defines’ itself. No other words are necessary. Everyone knows what a smartphone is. Right? (A little aside here: Sundar Pichai of Google is really impressed with a neural network. That is what powers Google’s ‘Assistant’ on your smartphone. You can use the ‘Assistant’ even on an iPhone as long as you are using Google Chrome. Anyway, I wish you would have been able to use it to be your Christmas presents! Half the time. Half the money.  Half the running around. Seriously. It knows sooo much.)

People in undeveloped countries use smartphones. Their use outpaces the use of other ‘intelligent’ things. In 2013 a median of 45% across 21 developing countries used the internet or owned a smartphone. In 2015 the number rose to 54% much of that in China, Brazil, and Malaysia.

By comparison, 8% of people in developed countries use the internet and smartphones. One of the biggest divides is between ‘Millenials’ 18-35 and those that are older. 76% of internet users in emerging countries use social networks. This compares with the US where more than 71% use smartphones! In Europe 65% across 6 European countries use smartphones. Ten years the iPhone didn’t exist!

PEW RESEARCH CENTER Source: Spring 2015 Global Attitudes survey Q71 & Q72

Author: John Pavlus, September 6, 2017. Titled So-So Semantics in Quartz (the guide to the new global economy for people excited by change.

Editor: I can vouch for the absolute insanity in Quartz. A whole different way to communicate with someone/thing smarter than you – your smartphone. Not to be believed events occurring. Imagine editing your chromosomes – while they are still in your body! Check out Hugo too! Your very own AI bot to converse with. A great study buddy.

China: Robots and Intelligence

China is installing robots like crazy. Shipments of robots to the country rose 27% last year. At 90,000 it was more than any other nation. It is on track to account for one-third of the world’s total. The push for automation could depress wages for Chinese workers. It will exacerbate income inequality. It will hurt consumption. This shift will have worldwide ramifications economists warn. It will dent prospects for a more balanced global economy. American projected job loss 47%. EU 35 %. Pay attention folks!

Elon Musk, the Tesla Man automatically gave drivers of Tesla Model X and S vehicles ‘more power’. He gave away ‘free software-upgrades’ of car batteries to people struggling to survive the ‘summer of floods’. The same batteries that were lasting 60-kilowatt hours were now lasting 75-kilowatt hours. The increase in capacity was from 200 miles per charge to 242 miles per charge an increase of 17.3%. The upgrades were temporary. If you want to buy the upgrade, it is an ‘over the air’ installation of $2,000. You don’t even have to go to your dealer for the upgrade!


Just Announced:

The Verge:

Artificial Intelligence recreated Super Mario Brothers by watching someone else play it. (Not confirmed at press time. ed.)