Watson Seeks to Fix Legal System
September 19, 2017
The United States imprisons more people than any other country in the world. The justice system, however, is broken and needs to be repaired. How can this be done? IBM’s Watson might have the answer. Engadget shares that: “Watson Is Helping Heal America’s Broken Criminal-Sentencing System” and it could be the start of fixing the broken system. One of the worst problems in the US penitentiary is overcrowding and that most of the incarcerated people are in a minority ethnic group.
Watson is being implemented to repair this disparity. Human judgment can be swayed by the smallest item, so implementing artificial intelligence may make the justice system more objective. AI is not infallible is can wrongly sentence convicts. The best solution right now is to use a mixture of AI and real human logic. IBM works hand and hand with Ohio’s Montgomery County Juvenile Court System to start a pilot program that provides a judge a summary of a child’s life in order to make better choices for his/her care.
Judge Anthony Capizzi is eager to use the AI care-management system, because it will help him synthesize information better and hopefully make more informed decisions.
With this system, however, the judge is afforded a more-complete view of the child’s life, her essential information displayed on a dashboard that can be updated in real-time. Should the judge need additional details, he can easily have it pulled up. [Capizzi said], ‘If I have 10 care providers in my region, can Watson tell me — because of where that child lives, their educational background, their limitations, their family — is there a better one for that child versus the nine others?’
The Watson-based system will deliver more accurate answers the more information fed into it. The hope is that it will be implemented in the other Ohio counties and other systems will be developed for other justice systems. There is still the potential that the Ai could become biased, but there is always a learning curve to make the system work and build a better justice system for the future.
Whitney Grace, September 19, 2015
IBM Watson: The US Open As a Preview of an IBM Future
September 12, 2017
I read a remarkable essay, article, or content marketing “object” called “What We Can Glean From The 2017 U.S Open to Imagine a World Powered by the Emotional Intelligence AI Can Offer.” The author is affiliated with an organization with which I am not familiar. Its name? Brandthropologie.
Let’s pull out the factoids from the write up which has two themes: US government interest in advanced technology and IBM Watson.
Factoid 1: “Throughout time, the origin of many modern-day technologies can be traced to the military and Defense Research Projects Agency (DARPA).”
Factoid 2: “Just as ARPA was faced with wide spread doubt and fear about how an interconnected world would not lead to a dystopian society, IBM, among the top leaders in the provision of augmented intelligence, is faced with similar challenges amidst today’s machine learning revolution.”
Factoid 3: “IBM enlisted its IBM Watson Media platform to determine the best highlights of matches. IBM then broadcasted the event live to its mobile app, using IBM Watson Media to watch for match highlights as they happened. It took into account crowd noises, emotional player reactions, and other factors to determine the best highlight of a match.”
Factoid 4: “The U.S. Open used one of the first solutions available through IBM Watson Media, called Cognitive Highlights. Developed at IBM Research with IBM iX, Cognitive Highlights was able to identify a match’s most important moments by analyzing statistical tennis data, sounds from the crowd, and player reactions using both action and facial expression recognition. The system then ranked the shots from seven U.S. Open courts and auto-curated the highlights, which simplified the video production process and ultimately positioned the USTA team to scale and accelerate the creation of cognitive highlight packages.”
Factoid 5: “Key to the success of this sea change will be the ability for leading AI providers to customize these solutions to make them directly relevant to specific scenarios, while also staying agilely informed on the emotional intelligence required to not only compete, but win, in each one.”
My reaction to these snippets was incredulity.
My comment about Factoid 1: I was troubled by the notion of “throughout time” DARPA has been the source of “many modern day technologies.” It is true that government funding has assisted outfits from the charmingly named Purple Yogi to Interdisciplinary Laboratories. Government funding is often suggestive and, in many situations, reactive; for example, “We need to move on this autonomous weapons thing.” The idea of autonomous weapons has been around a long time; for example, Thracians’ burning wagon assaults which were a small improvement over Neanderthals pushing stones off a cliff onto their enemies. Drones with AI is not a big leap from my point of view.
My comment about Factoid 2: I like the idea that one company, in this case IBM, was the prime mover for smart software. IBM, like other early commercial computing outfits, was on the periphery of many innovations. If anything, the good ideas from IBM were not put into commercial use because the company needed to generate revenue. IBM Almaden wizard Jon Kleinberg came up with CLEVER. The system and method influenced the Google. Where is IBM in search and information access today? Pretty much nowhere, and I am including the marketing extravaganza branded “Watson.” IBM, from my point of view, acted like an innovation brake, not an innovator. Disagree? That’s your prerogative. But building market share via wild and crazy assertions about Lucene, home brew code, and acquired technology like Vivisimo is not going to convince me about the sluggishness of large companies.
My comment about Factoid 3: The assertion that magic software delivered video programming is sort of true. But the reality of today’s TV production is that humans in trailers handle 95 percent of the heavy lifting. Software can assist, but the way TV production works at live events is that there are separate and unequal worlds of keeping the show moving along, hitting commercial points, and spicing up the visual flow. IBM, from my point of view, was the equivalent of salt free spices which a segment of the population love. The main course was human-intermediated TV production of the US Open. Getting the live sports event to work is still a human intermediated task. Marketing may not believe this, but, hey, reality is different from uninformed assertions about what video editing systems can do quickly and “automatically.”
My comment about Factoid 4: See my comment about Factoid 3. If you know a person who works in a trailer covering a live sports event, get their comments about smart editing tools.
My comment about Factoid 5: Conflating the idea of automated functions ability to identify a segment of a video stream with emotion detection is pretty much science fiction. Figuring out sentiment in text is tough. Figuring out “emotion” in a stream of video is another kettle of fish. True, there is progress. I saw a demo from an Israeli company’s whose name I cannot recall. That firm was able to parse video to identify when a goal was scored. The system sort of worked. Flash forward to today: Watson sort of works. Watson is a punching bag for some analysts and skeptics like me for good reason. Talk is easy. Delivering is tough.
Reality, however, seems to be quite different for the folks at Brandthropologie.
Stephen E Arnold, September 12, 2017
Smart Software: An AI Future and IBM Wants to Be There for 10 Years
September 7, 2017
I read “Executives Say AI Will Change Business, but Aren’t Doing Much about It.” My takeaway: There is no there there—yet. I noted these “true factoids” waltzing through the MIT-charged write up:
- 20% of the 3,000 companies in the sample use smart software
- 5% use smart software “extensively” (No, I don’t know what extensively means either.)
- About one third of the companies in the sample “have an AI strategy in place.”
Pilgrims, that means there is money to be made in the smart software discontinuity. Consulting and coding are a match made in MBA heaven.
If my observation is accurate, IBM’s executives read the tea leaves and decided to contribute a modest $240 million for the IBM Watson Artificial Intelligence Lab at MIT. You can watch a video and read the story from Fortune Magazine at this link.
The Fortune “real” journalism outfit states:
This is the first time that a single company has underwritten an entire laboratory at the university.
However, the money will be paid out over 10 years. Lucky parents with children at MIT can look forward to undergrad, graduate, and post graduate work at the lab. No living in the basement for this cohort of wizards.
Several questions arise:
- Which institution will “own” the intellectual property of the wizards from MIT and IBM? What about the students’ contributions?
- How will US government research be allocated when there is a “new” lab which is funded by a single commercial enterprise? (Hello, MITRE, any thoughts?)
- Will young wizards who formulate a better idea be constrained? Might the presence or shadow of IBM choke off some lines of innovation until the sheepskin is handed over?
- Are Amazon, Facebook, Google, and Microsoft executives kicking themselves for not thinking up this bold marketing play and writing an even bigger check?
- Will IBM get a discount on space advertising in MIT’s subscription publications?
Worth monitoring because other big name schools might have a model to emulate? Company backed smart software labs might become the next big thing to pitch for some highly regarded, market oriented institutions. How much would Cambridge University or the stellar University of Louisville capture if they too “sold” labs to commercial enterprises? (Surprised at my inclusion of the University of Louisville? Don’t be. It’s an innovator in basketball recruiting and recruiting real estate mogul talent. Smart software is a piece of cake for this type of institution of higher learning.)
Stephen E Arnold
IBM Watson Performance: Just an IBM Issue?
September 6, 2017
I read “IBM Pitched its Watson Supercomputer As a Revolution in Cancer Care. It’s Nowhere Close.” Here in Harrod’s Creek, doubts about IBM Watson are ever present. It was with some surprise that we learned:
But three years after IBM began selling Watson to recommend the best cancer treatments to doctors around the world, a STAT investigation has found that the supercomputer isn’t living up to the lofty expectations IBM created for it. It is still struggling with the basic step of learning about different forms of cancer. Only a few dozen hospitals have adopted the system, which is a long way from IBM’s goal of establishing dominance in a multibillion-dollar market. And at foreign hospitals, physicians complained its advice is biased toward American patients and methods of care.
The write up beats on the lame horse named Big Blue. I would wager that the horse does not like being whipped one bit. The write up ignores a problem shared by many “smart” software systems. Yep, even those from the wizards at Amazon, Facebook, Google, and Microsoft. That means there are many more stories to investigate and recount.
But I want more of the “why.” I have some hypotheses; for example:
Smart systems have to figure out information. Now on the surface, it seems as if Big Data can provide as much input as necessary. But that is a bit of a problem too. Information in its various forms is not immediately usable in its varied forms. Figuring out what information to use and then getting that information into a form which the smart software can process is expensive. The processes involved are also time consuming. Smart software needs nannies, and nannies which know their stuff. If you have ever tried to hire a nanny who fits into a specific family’s inner workings, you know that the finding of the “right” nanny is a complicated job in itself.
Let’s stop. I have not tackled the mechanism for getting smart software to “understand” what humans mean with their utterances. These outputs, by the way, are in the form of audio, video, and text. To get smart software to comprehend intent and then figure out what specific item of tagged information is needed to deal with that intent is a complex problem too.
IBM Watson, like other outfits trying to generate revenue by surfing a trend, has been tossed off its wave rider by a very large rogue swell: Riffing on a magic system is a lot easier than making that smart software do useful work in a real world environment.
Enterprise search vendors fell victim to this mismatch between verbiage and actually performing in dynamic conditions.
Wipe out. (I hear the Safaris’ “Wipe Out” in my mind. If you don’t know the song, click here.)
IBM Watson seems to be the victim of its own over inflated assertions.
My wish is for investigative reports to focus on case analyses. These articles can then discuss the reasons for user dissatisfaction, cost overruns, contract abandonments, and terminations (staff overhauls).
I want to know what specific subsystems and technical methods failed or cost so much that the customers bailed out.
As the write up points out:
But like a medical student, Watson is just learning to perform in the real world.
Human utterances and smart software. A work in progress but not for the tireless marketers and sales professionals who want to close a deal, pay the bills, and buy the new Apple phone.
Stephen E Arnold, September 6, 2017
Watson IBMs Only Chance at Avoiding Extinction
September 1, 2017
IBM is facing a massive problem as stock prices continue to drop – they aren’t relevant anymore. While new companies like Amazon and Facebook, along with fellow oldies Apple and Google, continue to grow in popularity and revenue, IBM is slowly but surely falling behind.
Forbes got straight to the point, recently, telling IBM to ‘go big or go home’. Their advice?
Rometty should aggressively rebrand IBM by simply naming it after the one thing in which IBM remains a market leader – Watson. All efforts in the cloud should be geared towards not just acting as a service provider but differentiating IBM by tailoring Watson’s services to the given client’s data so it can augment their decision-making. While they’re at it they can rename their cloud effort Watson Cloud.
Continuing with Forbes analysis of IBM’s situation, at the end of the day if the average millennial, I mean American, can’t use their AI technology in their day-to-day lives, they don’t care about it. The end. For IBM to catch up with the pack they must start routing their resources and attention to expanding Watson – and quickly.
Catherine Lamsfuss, September 1, 2017
IBM Watson Deep Learning: A Great Leap Forward
August 16, 2017
I read in the IBM marketing publication Fortune Magazine. Oh, sorry, I meant the independent real business news outfit Fortune, the following article: “IBM Claims Big Breakthrough in Deep Learning.” (I know the write up is objective because the headline includes the word “claims.”)
The main point is that the IBM Watson super game winning thing can now do certain computational tasks more quickly is mildly interesting. I noticed that one of our local tire discounters has a sale on a brand called Primewell. That struck me as more interesting than this IBM claim.
First, what’s the great leap forward the article touts? I highlighted this passage:
IBM says it has come up with software that can divvy those tasks among 64 servers running up to 256 processors total, and still reap huge benefits in speed. The company is making that technology available to customers using IBM Power System servers and to other techies who want to test it.
How many IBM Power 8 servers does it take to speed up Watson’s indexing? I learned:
IBM used 64 of its own Power 8 servers—each of which links both general-purpose Intel microprocessors with Nvidia graphical processors with a fast NVLink interconnection to facilitate fast data flow between the two types of chips
A couple of questions:
- How much does it cost to outfit 64 IBM Power 8 servers to perform this magic?
- How many Nvidia GPUs are needed?
- How many Intel CPUs are needed?
- How much RAM is required in each server?
- How much time does it require to configure, tune, and deploy the set up referenced in the article?
My hunch is that this set up is slightly more costly than buying a Chrome book or signing on for some Amazon cloud computing cycles. These questions, not surprisingly, are not of interest to the “real” business magazine Fortune. That’s okay. I understand that one can get only so much information from a news release, a PowerPoint deck, or a lunch? No problem.
The other thought that crossed my mind as I read the story, “Does Fortune think that IBM is the only outfit using GPUs to speed up certain types of content processing?” Ah, well, IBM is probably so sophisticated that it is working on engineering problems that other companies cannot conceive let alone tackle.
Now the second point: Content processing to generate a Watson index is a bottleneck. However, the processing is what I call a downstream bottleneck. The really big hurdle for IBM Watson is the manual work required to set up the rules which the Watson system has to follow. Compared to the data crunching, training and rule making are the giant black holes of time and complexity. Fancy Dan servers don’t get to strut their stuff until the days, weeks, months, and years of setting up the rules is completed, tuned, and updated.
Fortune Magazine obviously considers this bottleneck of zero interest. My hunch is that IBM did not explain this characteristic of IBM Watson or the Achilles’ heel of figuring out the rules. Who wants to sit in a room with subject matter experts and three or four IBM engineers talking about what’s important, what questions are asked, and what data are required.
AskJeeves demonstrated decades ago that human crafted rules are Black Diamond ski runs. IBM Watson’s approach is interesting. But what’s fascinating is the uncritical acceptance of IBM’s assertions and the lack of interest in tackling substantive questions. Maybe lunch was cut short?
Stephen E Arnold, August 16, 2017
Decoding IBM Watson
August 14, 2017
IBM Watson is one of the leading programs in natural language processing. However, apart from understanding human interactions, Watson can do much more.
TechRepublic in an article titled IBM Watson: The Smart Person’s Guide says:
IBM Watson’s cognitive and analytical capabilities enable it to respond to human speech, process vast stores of data, and return answers to questions that companies could never solve before.
Named after founding father of IBM, Thomas Watson, the program is already part of several organizations. Multi-million dollar setup fee, however, is a stumbling block for most companies who want to utilize the potential of Watson.
Watson though operates in seven different verticals, it also been customized for specialties like cyber security. After impacting IT and related industries, Watson slowly is making inroads into industries like legal, customer service and human resources, which comfortably can be said are on the verge of disruption.
Vishal Ingole, August 14, 2017
IBM Watson: Horning in on the WKS Model
August 7, 2017
If you paid attention in sociology class, you might have heard about the Willem Kleine Schaars Model or WKS for short. This is a reasonably well known way to make sense of individuals with psychological disabilities. I was interested when I read an email from IBM to me stating:
Tired of wasting time creating and fine tuning your custom machine learning models? Rules-based approaches can often shorten development time and improve accuracy. Register for our webinar, Accelerate WKS model development with a rule-based approach, on Wednesday, August 9 at 10AM PT to learn how to build rule-based models and save time by using them to pre-annotate machine learning models.
There is nothing so disappointing as the idea of a better way to to perform WKS classifications.
Bummer.
IBM’s WKS means the Watson Knowledge Studio. There’s no getting around the fact that humans or wizards like my goslings have to write rules and plug them into Watson.
My suggestion is:
Either get a better acronym or automate Watson’s expensive, tedious, error prone, difficult, and time consuming preparatory steps for a Watson deployment.
Otherwise I might slip into a different Willem Kleine Schaars’s category. Yikes!
Stephen E Arnold, August 7, 2017
Watson Powers New Translation Earpiece, No Connection Required
August 4, 2017
A start-up out of Australia is leveraging the prowess of IBM’s Watson AI to bring us a wearable translator, dubbed the Translate One2One, that does not require connectivity to function, we learn from “Lingmo Language Translator Earpiece Powered by IBM Watson” at New Atlas. Writer Rich Haridy notes that last year, Waverly Labs found success with its Pilot earpiece. That device was impressive with its near real time translation, but it did depend on a Bluetooth connection. Haridy asserts that New Atlas’ device is the first of its connection-independent kind; he writes:
Lingmo is poised to jump to the head of the class with a system that incorporates proprietary translation algorithms and IBM’s Watson Natural Language Understanding and Language Translator APIs to deal with difficult aspects of language, such as local slang and dialects, without the need for Bluetooth or Wi-Fi connectivity. …
The system currently supports eight languages: Mandarin Chinese, Japanese, French, Italian, German, Brazilian Portuguese, English and Spanish. The in-built microphone picks up spoken phrases, which are translated to a second language within three to five seconds. An app version for iOS is also available that includes speech-to-text and text-to-speech capabilities for a greater number of languages.
The device is expected to be available in July and can be pre-ordered now. A single unit is $179, while a two-piece pack goes for $229. Lingmo launched its first translation device in 2013 and has been refining its tech ever since. Who will be next in the field to go connection-free?
Cynthia Murrell, August 4, 2017
Lost in Translation?
August 3, 2017
Real-time translation is a reality with a host of apps. However, all these apps rely on real-time Cloud Computing for proverbial accuracy. Lingmo One2One Universal Translator seems to be different.
According to a product review published by Forbes and titled Lingmo One2One Universal Translator Preview, the reviewer says:
What gives me pause about the Lingmo, like the other universal translator devices, is the company has no track record in making hardware. Getting the translation stuff right is, I’m sure, hard enough. Getting all that to work in a portable device adds a whole other level of complexity.
Attempts have been made earlier to perfect the translation system, but so far no one has succeeded even decently. The problem is the complexity of human interactions. Though the device is powered by IBM’s AI program Watson, how it manages to store and process the humongous amount of text or voice based communication within the small box is not understandable.
Scientists have been trying to crack the natural language processing problem for a couple of years. Even with the vast amount of resources, it still looks like a distant possibility.
Vishal Ingole, August 3, 2017
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