Category: Humans and Computers

A personal quest of mine is to prepare a TedX talk to pass on my insights to young parents who are wondering how in the world do we prepare our children for employment in the next 10+ years. You might think ten years is a long way off. The problem is time passes and you suddenly realize how behind the curve you are with the changes in society and technology you are required to use at work.

So I am on a mission to find sources of inspiration and insight that I can draw on from authors who are thinking about the AI augmented workforce. People who have given thought to the impact of AI in our lives and can speak to the practicality of what we will face soon and who over ideas on how to prosper. There is no end to the gloom-and-doom articles that persist about the dark aspect of AI. I don’t see much value in that as a central focus.

I’m interested in seeing what is possible with machines that can help me think and work at higher levels of abstraction by removing the tedious and repetitive aspects of knowledge work. These are the tools I want my grandkids to use and feel inspired continuously to keep learning.   An example of tools that are striving for this now is Brainspace 6.

Here is their promise:

Brainspace 6 is the industry’s most advanced software for digital investigations and unstructured data analysis. Built on our patented machine learning platform, Brainspace learns dynamically without the use of lexicons or taxonomies, giving users a robust suite of interactive visualizations and search tools to reveal the story within your data.

I’ve used Brainspace for two years. Brainspace reminds me of the first time the arrival of the family’s first (and only)  World Book Encyclopedia. I was seven years old and transfixed by a wealth of information at my finger-tips. I remember my mind was tingling with ideas. My thoughts became lost in a universe of possibilities with what seemed like unlimited access to facts and stories about countries and people around the world. Using Brainspace always brings back that same feeling of joy and discovery.

But back to the matter at hand. How do we prepare ourselves in the workforce for future versions of ultra-smart tools like this in the 2020-2030 time frame? What is the right way to mentor our children and grandchildren to be not only constant learners but inspired souls that seek a higher sense of what’s possible beyond just the operation of automated tools? In other words, how do we take the power of AI and the creativity, love, and intuition that makes us unique as humans and blend these for a better world?

One perspective is to look at this next decade as an opportunity for us to focus on being more who we are as humans as we team with machines that are becoming more capable at what they do best. In our case, we focus on enhancing our analytical technical acumen with skills that rely on empathy, collaboration, and socialization. An interesting article that raises this point is AI and the rise of the emotional economy.

If this is a topic you find interesting then consider getting these three books. I’m using them to prepare more public presentations on the topic. I’m asking for feedback every step of the way. So feel free to reach out to me with your ideas. I realize there are hundreds of software tools that are being revamped with enhanced AI abilities shortly. Not all of them are knowledge tools.  My point here is to remind ourselves to look at what this new level of mental augmentation does for us and to continue making mindful choices that impact the quality of our lives and our children’s lives as we evolve ourselves with these new tools.

Average is Over – Average Is Over: Powering America Beyond the Age of the Great Stagnation by Tyler Cowen
Only Humans Need Apply by Thomas H. Davenport and Julia Kirby
Smarter Faster Better  by Charles Duhigg
Homo Deus – A Brief History of Tomorrow by Yuval Noah Harari

If you only have time for one – get Only Humans Need Apply.

Tom

I worked at Symbolics and then MCC selling and marketing AI in the 80s, and it was a wild fun, exciting, insightful ride. Shown in the picture is the Symbolics 3640 Lisp Machine. Starting price was around $80,000 and then dropped to half that amount, but in the end, we could not compete with Sun Microsystems Motorola 68000 and Intel 8080 type processors running on PCs. Despite the amazing sophistication of the Lisp software and the promise that programmers would be vastly more productive and many ground breaking software developments, we could not compete.

Neural networks were the thing in the late 80’s and some of those initial technology innovations at MCC were spun off into profitable businesses.  Now we have computers that are 10,000+ times faster and smaller and we’re seeing a huge resurgence and a democratization of these technologies everywhere.  Businesses have a laser focus on the promise of AI now that the technology is catching up with the original AI ideas of years past.

This brings up a question, what is the right approach to selling and marketing AI, machine intelligence, neural networks, deep learning, big data as it’s introduced into the software we use at work and home. How is your job and your life being impacted as a result?

We used to think of AI as a something you developed and ran on an ‘AI machine’ with dedicated hardware for performance. Now AI runs on the ubiquitous cloud-based Intel process amongst others manufactured in the millions for 1/1000 the cost of a Lisp Machine.

AI software, for the most part, is invisible to us given so much of it runs in the cloud in large, data centers.  Yes, we have edge devices like Siri and Alexa Google Assistant, Cortana and so forth. And we will be using highly sophisticated dedicated hardware for image processing in self-driving cars. But the challenge remains of how to sell and market AI.  In the end, it’s not about AI as a technology; it’s about how AI solves a need and makes life better for us to become more who we want to be.  Let’s keep our eyes on that idea. The benefits versus the features. And from a more altruistic perspective consider benefits of social innovations the technology enables versus just features of new product inventions.

Why? Because selling AI as profit booster by reducing headcount is a slippery slope if the promise lives up to the anywhere near the hype of AI.  Personally, I think the emphasis needs to be on how we think and live and educate ourselves with higher levels of insight.  This is a promise I find more attractive. Let’s augment our humanness with more advanced machine technology. The reduction of headcount due to automation is part and partial to the introduction of new tools. Fair enough. I’m suggesting a more mindful approach that is a win-win to the employer and the employee.  I’m all for reducing the mind-numbing tasks at work provided I have the opportunity to exercise my creativity and insight to explore new ideas for business AND life at home. If this makes sense then consider reading about these ideas further in a book, the Fourth Economy. Or check out a review of this book  The Fourth Economy and The Future of Work.

It’s going to be interesting to see who the new winners will be in the coming few years.  Maybe all of us benefit versus just winners and losers. Call me optimistic if you wish.  I’m hoping for better tools that sharpen our ability to find new insights and solutions to complex problems.  New AI-based tools that all of us can use, even for free, in public libraries. But I digress for now.

As was quoted on the ALPHA GO docuementary in NETFLIX, “our quest now is to use machines to help us understand how we undestand.” That’s a noble cause I can related to because it helps all of us equally.

Now for a fun look back in time …

I recently found a news article from the NEW York Times By ANDREW POLLACK, Published: September 13, 1982. In many ways, much has changed with regards to the sophistication of AI but yet in some ways, much is the same around the promise of AI.  What hasn’t changed is the challenge of selling something called AI and the worry around the impact it will have in our lives.

Tom

More on the Lisp Machine history here.

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SELLING ARTIFICIAL INTELLIGENCE
NEW York Times By ANDREW POLLACK, Published: September 13, 1982.

Artificial intelligence, the science of making computers ”think,” has long been the preserve of theoreticians who were little concerned with practical applications.

”When they said ‘real things,’ they meant computers that can play chess,” said Dr. Roger Schank, chairman of the computer science department at Yale University. ”They were not going to talk to Wall Street, let alone own a suit.”

Now, however, business is taking an interest in artificial intelligence, or A.I., and some professors, such as Dr. Schank, are forming or joining companies to capitalize on the expected boom. But the new move toward commercialization is disrupting the academic community and provoking fears that university research will be hurt.

Some researchers welcome the business interest. Others, however, complain that corporations are outbidding the campus for scarce personnel, and that work is being diverted from long-term research to short-term problems with immediate application. They also say that scientists are becoming more reluctant to share research results. Effects on Research

”We perceive there’s a real potential for the existing quality of A.I. research to diminish,” said Ron Olander, who coordinates such research for the Defense Department’s Advanced Research Projects Agency. He made the remark during a panel discussion at the National Conference on Artificial Intelligence, held in Pittsburgh last month.

Artificial intelligence is concerned with making computers do things that are said to require intelligence when people do them. Commercial interest is centered on four areas:

– Vision systems, which would allow computers to interpret satellite photographs and allow industrial robots to identify objects coming down the assembly line.

– Natural language systems, which allow people who do not know computer languages to get information out of computer storage by asking for it in plain English.

– Expert systems, computer programs that mimic the behavior of human experts and that can do such things as diagnosing diseases and interpret geological data in exploring for minerals.

– Equipment and programs used by the artificial intelligence researchers themselves. Xerox and two start-up companies, Symbolics Inc. and Lisp Machine Inc., sell computers specially designed to handle Lisp, the programming language used by artificial intelligence researchers.

Several large companies such as Schlumberger, Hewlett-Packard, Digital Equipment and Texas Instruments have formed artificial intelligence groups, to design products for internal use and perhaps for outside sale.

Schlumberger, for instance, hopes to have expert systems interpreting data from logs of oil wells. Digital uses an expert system to help package computer systems and is developing a program to diagnose broken computers. RCA Government Systems and Lockheed’s Emsco division, meanwhile, advertised at the Pittsburgh conference for people to form artificial intelligence groups. Wall Street Notices

Wall Street is starting to take notice. F. Eberstadt & Company, a brokerage firm, has formed a special unit to analyze and possibly invest in companies in artificial intelligence.

More companies are being started, many of them drawing people from university research programs in a phenomenon similar to what occurred when genetic engineering was commercialized a few years ago.

Yale’s Dr. Schank, for instance, formed Cognitive Systems, which will sell natural language systems. It is designing a system for oil companies that will retrieve information on oil wells using plain English commands. Dr. Schank plans to develop computer programs that can do such things as give expert advice on taxes or wills.

Edward A. Feigenbaum, a computer science professor at Stanford University, has co-founded two companies – Intelligenetics, which aims to apply artificial intelligence to genetic engineering, and Teknowledge Inc., which designs expert systems for other companies. Teknowledge is designing a system for Elf Aquitaine, the French national oil company, to diagnose why a drilling bit gets stuck during drilling. Exodus From M.I.T.

Already, such university spinoffs have led to strains. The staff of the artificial intelligence laboratory at the Massachusetts Institute of Technology was decimated in 1980 when more than a dozen researchers left to form Symbolics. The company sells computers designed for artificial intelligence that the researchers developed while at M.I.T. About the only two staff research people who did not join Symbolics left M.I.T. to form Lisp Machine, a competing company.

”We took so many that it’s going to take years for M.I.T. to build back up,” conceded Russell Noftsker, president of Symbolics and former director of the artificial intelligence lab.

Marvin Minsky, an M.I.T. professor who is considered a founding father of artificial intelligence, agrees. ”Most A.I. labs cannot buy the machines they had a hand in designing,” he lamented. He also fears that universities will lack resources to develop the next generation of machines. Where Universities May Benefit

The commercial activity might have some benefit for universities, however. If artificial intelligence is considered commercially important, corporations might finance university research. The Carnegie-Mellon University has signed on several corporate sponsors for its robotics laboratory.

Also, the rise of the companies might make it easier for people who want to concentrate on basic research, because pressure from Government sponsors for practical results would be eased.

Some of the uneasiness in the university community stems from a difference in cultures. Academic researchers consider products coming out on the market unsophisticated and oversold.

”I don’t think they have anything to do with artificial intelligence – they have to do with the artificial intelligence of 10 years ago,” said David Waltz, professor of electrical engineering at the University of Illinois. Wrong Twice

An example often given is that of expert systems, the programs that can diagnose diseases or help explore for oil. Although the computer programs are fairly adept at making analyses, they cannot learn from experience. Given the same set of symptoms, for instance, an expert system will make the same diagnosis twice, even if the first one proves wrong.

”If you don’t have an expert that can learn and have memory, you get a little anxious,” said Dr. Schank, adding that expert systems are going on the market prematurely. His own company is often cited by others as an example of one that overly promotes its products. Cognitive Systems’ literature advertises that the company develops systems that offer ”all the benefits of having a human expert on your staff, but it never takes a lunch hour or goes on vacation.”

Those entering the business say that it is impossible to wait indefinitely for technology to be perfected before introducing it commercially. Lee Hecht, president and chief executive officer of Teknowledge, said there are many applications – from electronic circuit design to diagnosing nuclear power plant accidents – in which existing expert systems could save companies millions of dollars. Challenge for Management

Besides the question of how sophisticated their products are, start-up companies may face a bigger stumbling block – a lack of skill in managing a company and in focusing on specific market areas. They must also hang on until the market develops further and then face competition from the more established companies. ”What’s going to delay A.I. is that there isn’t an infrastructure for developing applied work,” said John H. Clippinger, president of the Brattle Research Corporation, a consulting and market research firm in Boston.

Those same problems afflicted genetic engineering companies formed by professors a few years ago. For lack of management talent, money or products, many of those companies have fallen on hard times. Some predict a similar shakeout in artificial intelligence.

”This field is even more university-bound than genetic engineering,” said one analyst who asked not to be identified. ”Some of those guys can’t manage their way out of a paper bag.”

NEW York Times By ANDREW POLLACK, Published: September 13, 1982.

Astoundingly positive things will come from artificial intelligence, but we would hardly know that from the onslaught of magazines and movies that paint a despotic future. Storytellers know fear sells. So let’s just admit, AI, like any technology, will have its dark side but can we stop obsessing about robots chasing after us and focus on the real opportunity? Let’s start teaching a new wave of employees the importance of teaming with AI. People will be more employable, and employers will build more competitive companies. As MIT Physics Professor Max Tegmar says, “Intelligence is the crux of the problem.” Our intelligence is the reason we have more power than any other animal on the planet. Let’s place hope in ourselves that the more we learn from each other and learn from these new AI enabled machines, the more we could build a better life with a focus on growing and sharing intelligence.

Recently, IBM’s Watson Team partnered with BMC to develop in-car AI capabilities using Watson IoT technology. The goal is to process in-car data alongside artificially intelligent assistant systems to help vehicles run more efficiently and provide a personalized driving experience. The end game: create a car that is way ahead of its competition. IBM’s Watson platform will incorporate machine learning, with an IoT component inside the car. Watson continually learns from the driver’s needs, habits and driving preferences, understanding and then adapting the driving experience to better suit the driver and passengers.

Drivers will be able to talk to their car, ask how it’s performing and the car will in turn reply. Watson vehicles will have learned the owner’s’ manual. Using natural language capabilities, Watson can understand the driver’s queries and provide the answers in a conversational style. This conversation can include anything from standard checks of recommended tire pressures to finding the handle to pop the hood. The idea here is Watson understands the car and the driving process and attempts to help you and learn from you as you drive.

I am reading Max Tegmark’s book as part of my research. I resonate with his thinking about the future of AI given my experiences with AI and related ‘smart technologies.’ I believe we need to move beyond the overly simplistic worry that that AI may be pushing some us out of the workforce. This may very well happen or not, but we are not helpless. We can encourage a more pragmatic perspective that both humans and AI need each other for success in business for decades to come. If that is the case, how do we position ourselves for gainful employment with more intelligence machines?

Let’s explore an example of AI in business, in particular sales and marketing. In a recent Harvard Business Review article, “How AI Is Streamlining Marketing and Sales” by Brad Power, some efficient applications of AI are in use now. In each case, the internal staff teamed with an AI assistant, multiplying the efficacy of the sales system.

I was recently talking with Steve Golab Senior Market Manager at Creative Alignments, in Austin about a company called InsideSales.com that has built a sales acceleration platform for predicting sales growth. InsideSales delivers a sales acceleration platform that leverages data science to help sales reps sell more and spend less time trying to figure out which accounts to spend the most time on. At the heart of the platform is a system called Neuralytics which is a big data predictive analytics system and an artificial intelligence engine that uses the industry’s largest collection of sales interaction data to help reps do their jobs better and more efficiently.

InsideSales.com is an assistant system that recommends prescriptions from Neuralytics for reps through a set of applications that run on top of their CRM. Those apps benefit from the predictive power of Neuralytics and, as reps make phone calls, send emails, and manage their forecasts, InsideSales apps automatically populate call data, email data, forecast data, etc. back into the CRM. InsideSales.com is an example of the kind of intelligent tool benefits from on-going refinement from the sales and support staff and, as a result, transforms the entire sales process to be more powerfully impactful on time management and close rates for everyone on the team.

I give presentations and talks about the importance of working with people who will be employed in the 2020-2030 decade to help them learn how to team with each other and team with machines to maximize their creativity and intelligence. It’s not an easy problem to find solutions to because it will vary widely from business to business. Dual teaming, with each other and smart machines, is what will give an employee the employment edge in the coming decade. It’s also what employers are going to stress in their hiring – people who work well in dynamic teams, always learning and always improving each other and the machines they use.

In another article, we will revisit what happened to Gary Kasparov, the reigning world chess champion who lost to an IBM supercomputer Deep Blue in a highly publicized match in 1997. He’s been thinking and observing the impacts of artificial intelligence in machines and how we partner with them to become more intelligent ourselves. I want to bring his ideas forward for everyone to benefit from in this new age of AI that is dawning upon now.

In my conversations with my adult children and seven grandchildren, I see that we need to focus on opportunities to learn with and from AI to become more who we are as humans. As AI becomes more capable, what is on offer is for us as humans to become more who we are: more compassionate, loving, insightful, intuitive, creative evolving beings. It’s not about becoming more machine-like.

When it comes to problem-solving in science, business or life, people will rise to handle the hard-to-define, difficult edge cases that machines just cannot or may never actually learn. That’s our value add. It’s not only a strategy for survival; it is our goal as humans.

For more about being human in the age of artificial intelligence, listen to this interview with MIT Physics Professor, Max Tegmark. – Life 3.0: Being Human in the Age of Artificial Intelligence. I think his book and his interviews provide a well thought-out, down to earth perspective on our future with AI. It can be a bright one, and he suggests we begin that conversation now.

An excellent podcast interview with Max Tegmark with Sam Harris’s Waking Up podcast. Sam Harris speaks with Max Tegmark about his new book Life 3.0: Being Human in the Age of Artificial Intelligence. They talk about the nature of intelligence, the risks of superhuman AI, a nonbiological definition of life, the substrate independence of minds, the relevance and irrelevance of consciousness for the future of AI, near-term breakthroughs in AI, and other topics. And yes I did borrow my title somewhat from Max Tegmark’s book.  So credit where credit is due. The book is both inspiring and insightful. Find a copy.

Another podcast interview with Max Tegmark is located at THINK with host Krys Boyd, from a slightly different perspective. This one is shorter with less technical depth.

Edited by Lily DOT Parish AT gmail.com

Featured Image Credit Photo by rawpixel.com on Unsplash

I comb through the following web sources for trending news relating to the evolution and social impact of these far reaching technologies – AI, Robotics, IoT and of course the biggie blockchain technology. The important perspective to keep in mind with blockchain technology is the impact is much broader than you may think. For a good baseline understanding of the wide impact of blockchain technology (on many industries), download the free E-Book: Blockchain for Dummies.  IBM’s web content does an excellent job of explaining blockchain to executives. Bottom line it’s not just about digital currency.

I’m particularly curious about the future impact of these technologies on employment in the coming years 2020’s and 2030’s. You see, I have seven grandkids, and I’m curious to learn how I can help them be better prepared to enter the employment market and capture good salaries.  I have already given two talks at Baylor School of Communication regarding employment preparedness in the age of AI and Machine Intelligence in the workforce.

Here are my top sources these days and yes these are free:

rightrelevance.com –  provides relevant information and intelligence on 50 thousand topics.

https://www.topbots.com – TOPBOTS educates business leaders on high-impact applications of modern machine learning and A.I. techniques and helps leading organizations adopt and implement emerging technologies.

Azeem Azhar: The Exponential View Newsletter – outstanding!

Robohub – a non-profit online communication platform that brings together experts in robotics research, start-ups, business, and education from across the globe. Our mission at Robohub is to connect the robotics community to the rest of the world.

Hackernoon Artificial Intelligence section Voicebot Weekly Voicebot.ai

Voicebot Weekly at http://www.Voicebot.ai

Benedict’s Newsletter  – a weekly newsletter of what he’s seen in tech and thought was interesting. Benedict works at Andreessen Horowitz and always has something insightful to pass on.

The best content, news, and resources for IoT – https://www.iotforall.com

More Resources

Emmanuel Sibanda List of Newsletters and articles for Blockchain and AI technology

NOTE: If you want to get a huge jump on others doing research I suggest subscribing to www.BrainSpace.com.  BrainSpace is all about augmenting intelligence to accelerate human potential.  I could not agree more. This is my go-to source when providing strategic advisory input for C level projects.  There is more to our future than the technology we’re chasing.

Tom

Twitter is @tparish
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512-782-4814

Tom Parish – Experienced VP marketing | writer engineer futurist | tracking the impact of artificial intelligence on our lives.