Microsoft Bing Has the Last AI Laugh
December 1, 2017
Nobody likes Bing, but because it is a Microsoft product it continues to endure. It chugs along as the second most used search engine in the US, but apparently first is the worst and second is the best for creating a database of useful information for AI. India News 24 shares that, “Microsoft Bing: The Redmond Giant’s Overlooked Tool” is worth far more than thought.
Every day millions of users use Bing by inputting search queries as basic keywords, questions, and even images. In order to test an AI algorithm, huge datasets are needed so the algorithm can learn and discover patterns. Bing is the key to creating the necessary datasets. You also might be using Bing without knowing it as it powers Yahoo search and is also on Amazon tablets.
All of this has helped Microsoft better understand language, images and text at a large scale, said Steve Clayton, who as Microsoft’s chief storyteller helps communicate the company’s AI strategy. It is amazing how Bing serves a dual purpose:
Bing serves dual purposes, he said, as a source of data to train artificial intelligence and a vehicle to be able to deliver smarter services. While Google also has the advantage of a powerful search engine, other companies making big investments in the AI race – such as IBM or Amazon – do not.
Amazon has access to search queries centered on e-commerce, but when it comes to everything else that is not available in one of their warehouses. This is where Bing comes in. Bing feeding Microsoft’s AI projects has yet to turn a profit, but AI is still a new market and new projects are always being worked on.
Whitney Grace, December 1, 2017
Watson Works with AMA, Cerner to Create Health Data Model
December 1, 2017
We see IBM Watson is doing the partner thing again, this time with the American Medical Association (AMA). I guess they were not satisfied with blockchain applications and the i2 line of business after all. Forbes reports, “AMA Partners With IBM Watson, Cerner on Health Data Model.” Contributor Bruce Japsen cites James Madera of the AMA when he reports that though the organization has been collecting a lot of valuable clinical data, it has not yet been able to make the most of it. Of the new project, we learn:
The AMA’s ‘Integrated Health Model Initiative’ is designed to create a ‘shared framework for organizing health data , emphasizing patient-centric information and refining data elements to those most predictive of achieving better outcomes.’ Those already involved in the effort include IBM, Cerner, Intermountain Healthcare, the American Heart Association, the American Academy of Family Physicians and the American Medical Informatics Association. The initiative is open to all healthcare and information stakeholders and there are no licensing fees for participants or potential users of what is eventually created. Madara described the AMA’s role as being like that of Switzerland: working to tell companies like Cerner and IBM what data elements are important and encouraging best practices, particularly when patient care and clinical information is involved. The AMA, for example, would provide ‘clinical validation review to make sure there is an evidence base under it because we don’t want junk,’ Madara said.
IBM and Cerner each have their own healthcare platforms, of course, but each is happy to work with the AMA. Japsen notes that as the healthcare industry shifts from the fee-for-service approach to value-based pricing models, accurate and complete information become more crucial than ever.
Cynthia Murrell, December 1, 2017
IBM Can Train Smart Software ‘Extremely Fast’ an IBM Wizard Asserts
November 30, 2017
Short honk: If you love IBM, you will want to read “AI, Cognitive Realities and Quantum Futures – IBM’s Head of Cognitive Solutions Explains.” The article contains extracts of an IBM wizard’s comments at a Salesforce event. Here’s the passage I noted:
What we find is we always start with POCs, proof of concept. They can be small or large. They’re very quick now, because we can train Watson our new data extremely fast.
If this is true, IBM may have an advantage over many other smart software vendors. Why? Gathering input data, formatting that data into a form the system’s content processing module can handle, and crunching the data to generate the needed indexes takes time and costs a great deal of money. If one needs to get inputs from subject matter experts, the cost of putting engineers in a room with the SMEs can be high.
It would be interesting to know the metrics behind the IBM “extremely fast” comment. My hunch is that head-to-head tests with comparable systems will reveal that none of the systems have made a significant breakthrough in these backend and training processes.
Talk is easy and fast; training smart software not so much.
Stephen E Arnold, November 29, 2017
IBM Watson: Shedding Dreams?
November 27, 2017
I read a short item in “IBM to Retire Two Watson IoT Services.” IBM rolled out Watson in 2011, but the bits and pieces were floating around or acquired years before Watson won a TV game show. (Post production, anyone?)
The short write up reveals a factoid, which I assume not to be too fake. Specifically, IBM Watson is not longer providing two Internet of Things, Watson-infused services. These are or were Context Mapping and Driver Behavior. Presumably clever customers can whip up something to perform Watson-like services with other chunks of IBM code.
From birth to shedding functions in just 72 months. What’s next? Shall I ask Watson? No, I shall not. It seems to me that reality may be dawning in some IBM management circles. There is more to shed as baby Watson tries to generate money, not PR and marketing hyperbole.
Stephen E Arnold, November 27, 2017
Google Made AI Learning Fun
November 22, 2017
Games that are supposed to be educational and fun usually stink worse than rotten fruit (except for Oregon Trail). One problem is that these games are not designed by gamers, i.e. people who actually play games! Another problem is that when gamers do design games they lack the ability to convey in a learnable manner. Thankfully Google has both gamers and teachers. According to Engadget, Google has a fun way to learn about AI: “Google Created A Fun Way To Learn Simple AI.”
Google invented the Teachable Machine that teaches users simple ways to learn about AI with only a webcam and microphone. What is great about the Teachable Machine is that it does not require any coding experience in order to use it. Anyone from children to adults can use it and it has already been used to do silly and stupid things along with smart and practical uses.
Teachable Machine conveys just how important pattern recognition is becoming in the technology world. It’s used in photo apps to recognize faces and objects, but it also powers supercomputers like IBM’s Watson. Looking ahead, we might eventually be able to use similar machine learning techniques to train our smarthomes. For example, it could automatically turn on your living room lights and TV when it detects you’ve come home. Or a pet feeder could dispense more food when your cat sits in front of it.
It is neat to play around with Teachable Machine and get your computer to do simple commands. The article ends on a sour and scary note: machine learning technology will always be watching and listening to users to learn more. Yes, very creepy.
Whitney Grace, November 22, 2017
IBM Watson: A Fashionista Never Says Sorry
October 19, 2017
I haven’t paid much attention to IBM Watson since the popular media began poking holes in IBM’s marketing assertions. However, I feel compelled to highlight the information in “Presenting Intelligent Fashion: IBM’s Watson and Vogue Unveil the World’s First AI Inspired Saree.” There’s nothing quite like versatile software able to treat cancer and whip up a saree.
Here’s the passage that I found amusing:
Findability Sciences, an IBM ecosystem partner, used Watson’s Visual Recognition API to extract specific context around patterns and colors that were most dominant. Aura Gupta used this data via a custom-built IBM application, to design a never-before-seen saree-gown that was worn by the event’s MC and Emmy Award winning actress, Archie Panjabi. The designs embodied the achievements of every woman and two men, yet were unique to each individual winner.
Quite a case example.
Stephen E Arnold, October 19, 2017
IBM: A Roll Downhill?
October 18, 2017
I read “IBM Reports Marginal Dip in Quarterly Revenue.” I think the headline qualifies as politically correct information. I don’t have the energy to point out that Big Blue is making some stakeholders blue. I highlighted this passage from the Reuters’ news story:
IBM’s revenue has declined for nearly six years as the company continues to exit some legacy businesses, while bolstering its cloud services.
Yep, six years. No, I don’t want to ask Watson anything.
Stephen E Arnold, October 18, 2017
Is This the End of the Middleman?
September 20, 2017
The introduction of the internet began to reduce the need for professional intermediaries back in the 1990s, but that trend has accelerated with today’s AI capabilities. The Korea Times examines the matter in, “AI Invigorates ‘Scissors Economy’.” The term “scissors economy” harkens back to 1999’s Market Shock by Todd Buchholz, in which that author coined the phrase to describe the shrinking reliance on go-betweens prompted by online technologies.
Some of the businesses that have been affected by these changing circumstances included brick-and-mortar stores, travel agents, stockbrokers, and insurance agents. It should come as no surprise– technologies that give consumers more direct control necessarily abridge nearly any transaction, cutting out professional intercessors. Writer Yoon Sung-won observes:
Expectations are that the phenomenon of the scissors economy will gain more strength as industries expedite introducing AI technologies in actual businesses. For instance, financial institutions such as banks, brokerage houses and insurance companies have started to use AI-based technologies not just to recommend optimal financial products to their clients but also to make decisions such as whom to grant loans to and where to invest. In the process, less and less human intervention is needed. Online shopping malls are also rushing to adopt new type of services, also based on AI technologies. Upon the customers’ agreement, online shopping platform operators collect information on their preferences to recommend products for customers to purchase. Internet and gaming service providers also use AI technologies to analyze their users to understand consumption patterns. Advanced medical institutions such as cancer centers are also tapping into AI technologies. In Korea, multiple hospitals including Gachon University Gil Medical Center have introduced IBM’s Watson AI system to give medical advice.
Yoon cites an “industry source” when noting that not many workers have been directly replaced by AI systems yet, but that it is only a matter of time. We’re also cautioned—maybe those humans-in-the-middle are actually beneficial. What world will we create when we hand as much decision-making to algorithms as possible?
Cynthia Murrell, September 20, 2017
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
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