Much has been written about how artificial intelligence (AI) will put white-collar workers out of a job eventually. Will robots soon be able to do what programmers do best — i.e., write software programs? Actually, if you are or were a developer, you’ve probably already written or used software programs that can generate other software programs. That’s called code generation; in the past, it was done through “next” generation programming languages (such as a second-, third-, fourth-, or even fifth-generation languages), today are called low code IDEs. But also Java, C and C++ geeks have been turning high level graphical models like UML or BPML into code. But that’s not what I am talking about: I am talking about a robot (or bot) or AI software system that, if given a business requirement in natural language, can write the code to implement it — or even come up with its own idea and write a program for it.
That is exactly what Forrester wants to find out - is there something behind the AI and Cognitive Computing hype? What my research directors ask, "Is there a there there?"
AI and Cognitive Computing have captured the imagination and interest of organization large and small but does anyone really know how to bring this new capability in and get value from it? Will AI and Cognitive really change businesses and consumer experiences? And the bigger question - WHEN will this happen?
It is time to roll-up the sleeves and look beyond conversations, vendor pitches and media coverage to really define what AI and Cognitive Computing mean for businesses, are businesses ready, where they will invest, and who they will turn to to build these innovated solutions, and what benefits will result. As such, Forrester launched its Global Artificial Intelligence Survey and is reaching out to you - executives, data scientists, data analysts, developers, architects and researchers - to put a finger on the pulse. We would appreciate you take a little time out of your day to tell us your point of view.
As a thank you, you will receive a complimentary summary report of the findings.
If you have a great story to share that provides a perspective on what AI and Cogntivive can do, what benefits is has provided your company, and can share you learnings and best practices, we are also recruiting for interviews.
Simply contact our rock star researcher, Elizabeth Cullen, to schedule 30 minutes. firstname.lastname@example.org
Knowledge is power. And in a time where insights drive business differentiation, knowledge is also the origin of power. In our daily routines as consumers, search is probably the most common application we use to find knowledge, and it forms the basis of our personal systems of insight. But at long last, search in the enterprise is catching up. A new wave of search-based applications and search-driven experiences are now being delivered by companies who understand the need to empower their employees and customers with immediate, contextual knowledge in an easily-consumable format.
In our new research, Mike Gualtieri and I look at how the emerging landscape of cognitive search experiences are incorporating advanced analytics, natural language processing (NLP), and machine learning to enable organizations to see across wide arrays of enterprise data and stitch together insights hidden among them.
You can't turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It's all so very cool!
But cool doesn't build a business. Results do.
Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:
What is artificial intelligence (or insert: machine learning or cognitive computing)?
What’s taken artificial intelligence (AI) so long? We invented AI capabilities like first-order logical reasoning, natural-language processing, speech/voice/vision recognition, neural networks, machine-learning algorithms, and expert systems more than 30 years ago, but aside from a few marginal applications in business systems, AI hasn’t made much of a difference. The business doesn’t understand how or why it could make a difference; it thinks we can program anything, which is almost true. But there’s one thing we fail at programming: our own brain — we simply don’t know how it works.
What’s changed now? While some AI research still tries to simulate our brain or certain regions of it — and is frankly unlikely to deliver concrete results anytime soon — most of it now leverages a less human, but more effective, approach revolving around machine learning and smart integration with other AI capabilities.
What is machine learning? Simply put, sophisticated software algorithms that learn to do something on their own by repeated training using big data. In fact, big data is what’s making the difference in machine learning, along with great improvements in many of the above AI disciplines (see the AI market overview that I coauthored with Mike Gualtieri and Michele Goetz on why AI is better and consumable today). As a result, AI is undergoing a renaissance, developing new “cognitive” capabilities to help in our daily lives.
I sat down with Steve Cowley, General Manager for IBM Watson, on Tuesday at IBM Insights to talk about Watson successes, challenges since the January launch, and what is in store. While the potential has always intrigued me, the initial use cases and message gave me more than a bit of pause: the daunting task to develop and train the corpus, the narrowness of the use cases, what would this actually cost? Jump ahead nine months and the IBM Watson world is in a very different place.
IBM is clearly in its market building phase. It is as much about what IBM Watson is and how IBM overall is repositioning itself as it is about changing the business model for selling technology. However, it is easy to get negative very fast on this strategy as seen with the tremors on Wall Street as IBM's stock has gone from a 52 week high of $199 to $164 at close on Friday 10/31, much of that happening in the past month since earnings release. Wall Street may not like company uncertainty during transitional periods, but enterprise architects care about what will make their organizations successful, make development and management of technology easier, and making sure costs don't sky rocket when new bright shiny objects come in. And, that is where IBM is headed with an eye toward changing the game.
IBM Watson delivers on information over technology.
Steve surprised me with this statement, "[With] traditional programmed systems, the system is at its best when it is deployed, because it is closest to the business need it was written for. Over time these systems get further and further away from the shifting business need and so either they fall in effectiveness, or require a great deal or maintenance." Steve pointed out that data is what is changing the game.*
I am just back from the first ever Cognitive Computing Forum organized by DATAVERSITY in San Jose, California. I am not new to artificial intelligence (AI), and was a software developer in the early days of AI when I was just out of university. Back then, if you worked in AI, you would be called a SW Knowledge Engineer, and you would use symbolic programming (LISP) and first order logic programming (Prolog) or predicate calculus (MRS) to develop “intelligent” programs. Lot’s of research was done on knowledge representation and tools to support knowledge based engineers in developing applications that by nature required heuristic problem solving. Heuristics are necessary when problems are undefined, non-linear and complex. Deciding which financial product you should buy based on your risk tolerance, amount you are willing to invest, and personal objectives is a typical problem we used to solve with AI.
Fast forward 25 years, and AI is back, has a new name, it is now called cognitive computing. An old friend of mine, who’s never left the field, says, “AI has never really gone away, but has undergone some major fundamental changes.” Perhaps it never really went away from labs, research and very nich business areas. The change, however, is heavily about the context: hardware and software scale related constraints are gone, and there’s tons of data/knowledge digitally available (ironically AI missed big data 25 years ago!). But this is not what I want to focus on.
Recent news of a a computer program that passed the Turing Test is a great achievement for artificial intelligence (AI). Pulling down the barrier between human and machine has been a decades long holy grail pursuit. Right now, it is a novelty. In the near future, the implications are immense.
Which brings us to why should you care.
Earlier this week the House majority leader, Eric Cantor, suffered an enormous defeat in Virginia's Republican primary by Tea Party candidate David Brat. No one predicted this - the polls were wrong, by a long shot. Frank Luntz, a Republican pollster and communication advisor, offered up his opinion on what was missing in a New York Times Op-Ed piece - lack of face-to-face discussions and interviews with voters. He asserts that while data collection was limited to discrete survey questions, what it lacked was context. Information such as voter mood, perceptions, motives, and overall mind set were missing. Even if you collected quantitative data across a variety of sources, you don't get to these prescient indicators.
The new wave of AI (the next 2 - 5 years) makes capturing this insight possible and at scale. Marketing organizations are already using such capabilities to test advertising messages and positioning in focus group settings. But, if you took this a step further and allowed pollsters to ingest full discussions in person or through transcripts in research interviews, street polls, social media, news discussions and interviews, and other sources where citizen points of view manifest directly and indirectly to voting, that rich content translates into more accurate and insightful information.
IBM's acquisition of Cognea, a startup that creates virtual assistants of multiple personalities, further reinforces that voice is not enough for artificial intelligence. You need personality.
I for one cheer IBM's investment, because to be honest, IBM Watson's Jeopardy voice was a bit creepy. What has made Apple's Siri intriguing and personable, even if not always an effective capability, is the sultry sound of her voice and at times the hilarity of Siri's responses. However, if you were like me and changed from the female to male voice because you were curious, the personality of male Siri was disturbing (the first time I heard it I jumped). Personality is what you relate to.
The impression of intelligence is a factor of what is said and how it is delivered. Think about how accents influence our perception of people. It is why news media personalities work hard to refine and master a Mid-west accent. And, how one presents themselves in professional situations says a lot about whether you can trust their judgment. As much as I love my home town of Boston, our native accent and sometimes cold personalities have much to be desired by the rest of the country. And we have Harvard and MIT! Oh so smart maybe, but some feel we are not always easy to connect with.
It looks like the beginning of a new technology hype for artificial intelligence (AI). The media has started flooding the news with product announcements, acquisitions, and investments. The story is how AI is capturing the attention of tech firm and investor giants such as Google, Microsoft, IBM. Add to that the release of the movie ‘Her’, about a man falling for his virtual assistant modeled after Apple’s Siri (think they got the idea from Big Bang Theory when Raj falls in love with Siri), and you know we have begun the journey of geek-dom going mainstream and cool. The buzz words are great too: cognitive computing, deep learning, AI2.
For those who started their careers in AI and left in disillusionment (Andrew Ng confessed to this, yet jumped back in) or data scientists today, the consensus is often that artificial intelligence is just a new fancy marketing term for good old predictive analytics. They point to the reality of Apple’s Siri to listen and respond to requests as adequate but more often frustrating. Or, IBM Watson’s win on Jeopardy as data loading and brute force programming. Their perspective, real value is the pragmatic logic of the predictive analytics we have.
But, is this fair? No.
First, let’s set aside what you heard about financial puts and takes. Don’t try to decipher the geek speak of what new AI is compared to old AI. Let’s talk about what is on the horizon that will impact your business.
New AI breaks the current rule that machines must be better than humans: they must be smarter, faster analysts, or they manufacturing things better and cheaper.