Archive for the ‘IBM’ Category

Pizza Menus, Rebounds, and Blood Sugar: 3 Watson Use Cases

Tuesday, June 14th, 2016

We’ve been told countless times, what you get out of a computer is only as good as what you put in. But what happens if you’re feeding a computer that learns and reasons?

According to Caroline Ong, Business Analytics Leader at IBM Global Business Services Canada, cognitive computing is not necessarily programmed, but designed to understand context, reason (and provide that reasoning). Moreover, cognitive technology means the machine learns from feedback.

And because there are so many applications for this kind of technology, the system becomes specialized according to what it’s used for.

“It’s not one giant machine that takes knowledge from person 1, client X and system Y,” explains Ong. “Watson is a solution tailored to specific industries and specific client use cases.”

Here are three real-life cases from the IBM Watson team and their collaborators that use real life data in surprising ways.

Rebounds

https://www.youtube.com/watch?v=7zKLEyLTqNU

The Toronto Raptors’ system can call up and interpret game statistics from every member of the team – and perhaps more importantly, their prospective opponents, so that the team can be as prepared as possible.

“We’re working with the Raptors to reimagine their data instead of using pen and paper, to pick out who we should be drafting next, or what are some of the tradeoffs we need to make,” says Ong.

Data is an important aspect of this with a feature called “Tradeoff Analytics,” where the user can tweak various statistics and see possible results. But basketball isn’t just a numbers game – personality analytics of players and opponents can also provide some valuable intelligence.

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Blood Sugar Levels

Medtronic, which makes devices that measure blood sugar levels in diabetes patients, are exploring how to incorporate IBM Watson technology to ease the pressure on caregivers.

Ong said that the aim was to improve “quality of life for the patient and for the caregivers of the folks who are dealing with diabetes. The two teams in IBM and Medtronic worked together to see how we predict hypoglycemic levels and events, up to three hours in advance – as opposed to waiting for things to happen before we act on them.”

The aim is to get peace of mind for the caregivers without having them constantly monitor physically every single time.

Other health uses of Watson include Under Armour, data from wearable technology that can be used to make fitness recommendations, and a partnership with the BC Cancer Agency that uses patient data, a genetic profile and medical literature to make personalized treatment plans.

Pizza Menus

https://www.youtube.com/watch?v=jC0I08qt5VU

A robot concierge being piloted by Hilton hotels uses IBM Watson cognitive analysis to give guests advice, for instance, on what nearby Italian restaurants have kid-friendly cuisine.

Connie, the collaboration between IBM and Hilton, takes and interprets local information such as restaurant and tourist information using speech to text, dialog and natural language APIs, and ingests information from IBM travel advice too WayBlazer.

Ong says that the project behaves like a virtual assistant, “but this time the communication is done through talking to a robot in the lobby of the hotel.”

The more questions the robot is asked, the smarter it gets – and the robot keeps a log of questions that people pose it, which is helpful for the hotel itself.

“The good news is, you don’t have to tip the robot,” says Ong.

For a free trial of IBM Watson Analytics click here

Analytics4Life Takes The Stress Out Of Coronary Artery Disease Tests

Wednesday, June 8th, 2016

For a disease recognized as the most common global cause of death – 8.14 million worldwide in 2013 – coronary artery disease is extremely laborious to diagnose. A Canadian firm, Analytics4Life, is looking to cut out a large part of the labor involved – and the danger posed to patients – by leveraging data in new ways.

In a nuclear stress test, patients must exercise on a treadmill to measure blood flow after radioactive dye is injected. Physicians and hospital workers need to spend resources on managing radioactive nuclei. The test can take up to five hours and is only 75% accurate.

Analytics4Life, a startup based in Kingston, Ontario, is attempting to develop a much more straightforward method, using machine learning through neural networks and genetic analysis, with physiological information conventionally considered valueless.

“We’ve been able to demonstrate a very simple test that takes about three minutes to do where you don’t have to stress the patients,” says Shyam Ramchandani, co-founder and Director of Marketing and Business Development at A4L. “It’s just surface electrodes that go on patches on the body: seven of them. Three minutes later, they’re done, and then by the time their patches are off and their shirt’s on the result is on the doctor’s portal.”

Tech Portfolio Fact

This month, A4L is running its first machine learning tests with recruited patients already diagnosed with coronary artery disease, a condition where the vessels that supply oxygenated blood to the heart are obstructed by plaque; if the plaque builds up excessively and hardens, the condition leads to blood clots, angina and cardiac arrest.

After crunching what A4L calls “phase energy” data – which draws on a wide array of physiological signals – from the electrodes, and generating a formula based on the results, the company will then test blind on another selection of patients to see if their formula is an effective predictor.

“‘Phase energy’ is purely a mathematical concept,” explains Ramchandani. “There is no current physiological description of this. We will be the first to demonstrate this.”

The test itself can be administered from a “phase energy signal recorder”, an iPad Mini adapted with proprietary technology to connect to the electrode input. The recorder transfers the data to the cloud. The front-end software and the data processing lives on IBM Cloud, and important code infrastructure is hosted on IBM’s Bluemix platform.

This setup makes it all portable. “You technically can take our test anywhere you have a 3G signal,” says Ramchandani. “You wouldn’t have to fly people in from remote areas to a place that has a special camera.”

According to Ramchandani, IBM infrastructure is ideal for handling healthcare data. “We have an almost off-the-shelf HIPAA compliant tool. When you’re collecting medical information, you have to either de-identify it in a way that it can’t connect it back to the patient, or it needs to be hosted on and transmitted on infrastructure that’s been validated for security purposes.”

A4L completed series A funding last August for CA$10 million, and is hoping to complete series B at the beginning of 2017 to help it fund commercialization activity and a pivotal clinical trial.

Tech Portfolio Fact

That pivotal clinical trial will support their next key step: the FDA approval process. “If you can’t get through regulatory affairs in an efficient manner and get the kind of reimbursement you need, it’s not going to be a business,” Ramchandani says. The company has made senior level hires in order to facilitate interaction with the FDA.

Another option for A4L is expanding in Europe. (They will not start with Canada initially, because the market is too small.)

Although a price for the test hasn’t been set, A4L says that the overheads for its new system, once approved and functioning, are going to be astronomically less than the status quo. “We have no regulated nuclei that needs to be injected, purchased, or handled,” says Ramchandani. “You don’t need a specialized technician, or a specialized camera.”

And no more running on a treadmill, either.

For a free trial of IBM Bluemix click here.

VIDEO: Cognitive Technology Needs the Cloud

Sunday, June 5th, 2016

Cognitive solutions have developed beyond traditional AI capabilities into machine thinking, and startups can now leverage cognitive technologies like IBM Watson through the cloud to offer better solutions to their customers.

IBM’s Watson can digest and reason through information, and come up with solutions. This allows startups to offer their customers APIs like natural language processing without the obstacles that prevent easy, robust and responsive user experiences.

Placing this advanced technology in the cloud provides the resources that cognitive needs to function smoothly, and for it to be accessible through platforms including IBM Bluemix.

For more on this, here’s part of our recent interview with IBM’s Nevil Knupp, Vice President of Cloud, IBM Canada.

https://www.youtube.com/watch?v=24fSC8L3i5Y

For a free trial of IBM Bluemix click here.

For a free trial of IBM Watson Analytics click here.

VIDEO: Expertise and Mentorship Help Startups Exit the ‘Valley of Death’

Wednesday, June 1st, 2016

Transitioning to a scale-up is proving difficult for startups within Canada’s tech ecosystem. There are many questions about how to better support startups in order to maintain competitiveness and longevity. The transition can be so difficult that Patrick Horgan, VP, Manufacturing, Development & Operations at IBM Canada refers to it as the ‘Valley of Death’.

Bridges are being built over the Valley, though. Initiatives like IBM’s Bluemix Garage – which provides technology support, shares growth experience, and offers mentorship – aims to foster scale up success in the long-run.

To learn more about the challenges startups face in scaling up, and about initiatives that offer support, watch this video:

https://www.youtube.com/watch?v=gGVlvcO3pn0

VIDEO: IBM’s Watson Offers Cognitive Technology Out of the Box

Saturday, May 28th, 2016

Not all startups can afford artificial intelligence and the associated infrastructure, but thanks to IBM’s Bluemix platform, the cognitive capabilities of IBM Watson can be a ‘call’ away. Apps developed on the platform will be able to make requests from Watson.

This will give startups access to functions that analyze data streams or translate text into language that either a machine or another person can understand.

For more on this, here’s part of our recent interview with IBM’s Nevil Knupp, Vice President of Cloud, IBM Canada.

https://www.youtube.com/watch?v=HmqmBcJM0AE

VIDEO: IBM’s Ontario Research Consortium Partnership

Tuesday, May 24th, 2016

IBM provided a $200+ million “sandbox” and access to the country’s biggest supercomputer, along with an abundance of analytical tools as the company’s contribution to Southern Ontario Smart Computing Innovation Platform (SOSCIP), a research and development consortium that now includes 14 universities and 2 colleges.

The result? The partnership has generated $2 billion in pipeline revenue by helping university researchers and startups get to market.

This innovation model, facilitating collaboration between IBM, academic institutions, Ontario Centre of Excellence, and SMEs aims to help establish Ontario as a leading global centre for driving innovation in information technology, health, and urban infrastructure (water, energy, transportation).

By partnering with private enterprise, academic institutions can leverage cutting-edge technology and experience. Learn more about SOSCIP and its economic impact:

https://www.youtube.com/watch?v=RmfLNK3YUP8

What IBM’s Watson Thinks of HBO’s Game of Thrones Characters

Friday, May 20th, 2016

When we found out we could access IBM’s Watson to analyze conversation tone, obviously our first idea was to apply it to HBO’s Game of Thrones.

So we coded a scraper and extracted the dialogue on Wikiquote of all the main characters. We then unleashed Watson’s Tone Analyzer — a tool for understanding the emotional impact of text — to assess five core emotions, as well as overall emotional stability, confidence, and agreeableness.

The Tone Analyzer is part of IBM’s Watson suite that includes several tools for textual analysis, such as concept search and linking, visual comprehension, and language translation. It’s accessible through an API on the IBM Bluemix cloud platform.

It’s no surprise no one in Westeros turned out to be especially emotionally stable (we’re looking at you Ramsay Bolton). In fact, the majority of characters we tested, from the Khaleesi herself to Jon Snow and even jolly old Tyrion Lannister, turned out to be mainly very angry and not at all joyful.

But the results we did find – some expected, some not – reveal a bit more about a few of the players.

Here’s our (spoiler free of season six, we promise) analysis.

CHARACTER: Joffrey Baratheon

RESULT: ANGER

techPortfolio_GOT_Joffrey1

Rage is the overriding emotion for Joffrey the False. Interestingly, though, no one particular line in our textual analysis stands out as being obviously furious or malicious. This, of course, is worse because it just shows the continual burning rage inside the boy king.

CHARACTER: Sansa Stark

RESULT: Even ANGRIER than Joffrey

techPortfolio_GOT_Sansa2

Sansa’s incandescent fury is clear at several points – and the Tone Analyzer has picked up on this. Her reputation among fans as a passive flower is completely undeserved. There’s not a great deal going on with conscientiousness, which could suggest machinations to come.

Combine this with her sky-high confidence and it seems we have identified that Sansa is a rising power player in the battle for the Iron Throne.

CHARACTER: Arya Stark

RESULT: Low confidence

techPortfolio_GOT_Arya1

A girl has no name…or confidence. Arya rates very low on the confidence scale for a Game of Thrones character. In fact, there are only four lines in her entire script that rate as confident. The rest don’t score anything at all. It looks like the first step of Faceless Man assassin training is breaking the spirit.

Still angrier than a burning nest of wasps, though. Just ask Meryn Trant.

CHARACTER: Varys, Master of Whispers

RESULT: Calm and in control

techPortfolio_GOT_Varys4

Varys once said: “The storms come and go, the waves crash overhead, the big fish eat the little fish, and I keep on paddling.” With lines like this, we’re not shocked to see the tone analysis of his language shows he rarely raises his voice. He’s unique in Westeros in being able to regulate his emotions and keep his cards close to his chest. Clearly, he’s perfectly suited to his role as spymaster regardless of who he serves.

Is there a character you want to analyze? Try the Tone Analyzer now.

If you want to incorporate IBM’s Watson into your own application hosted in the cloud, click here.

Agriculture. Healthcare. Real Estate: Three IBM Bluemix Use Cases

Thursday, May 19th, 2016

We spoke to a sample of Canadian startup entrepreneurs using IBM Bluemix to support their apps, and found a key theme was the amount of time and resources saved by leveraging Bluemix technology.

techPortfolio_Quote_May_19

Agriculture

The PlantID3 mobile app is used by agricultural professionals to monitor crop health. The service is in beta testing with 2,000 trial users and a soft launch is planned later this year in Australia.

Dylan Lidster, founder and CEO of PlantID3, says in agricultural tech, startup organizations clear a path for the larger corporations because they can pivot quickly and spend less money getting to market.

“The aging demographic of agriculture is quickly rolling over as a new tech sophisticated producer enters the market, open to change and moving swiftly,” he says.

In the app a farmer might take a picture of an apple tree with a disease. The app then searches through the database for pattern recognition and tags. The image is tagged and stored, and then feeds into recommendations for remedial practice, such as pruning techniques, fertilization or spray application. The database of image storage and tagging and the recommendation tree run off IBM’s BlueMix infrastructure.

Lidster is making sure that the customer base is involved at the early stages of development of the PlantID3 app, to ensure product fit with the interface. Being cheap and quick to scale is key.

“[BlueMix] saves many hours of labour and provides us with advanced services such as use of Watson that we could not achieve without the support of IBM,” says Lidster.

Healthcare

Speed and scalability are also paramount when you’re building an app that transmits health data. With SwiftPad, when patients take a photo of their prescription and send it to their pharmacy of choice, they can get real-time feedback on when their medication is ready and can opt to have it delivered.

Saif Abid, CTO of SwiftPad, says: “Currently, we have around 12 services running on IBM’s Bluemix network. To put this in a financial perspective, we’re spending around 75% less than we would with other competitors.” Among the services used are CloudFoundry, push notifications, and Watson.

“With Bluemix, we’re able to focus on engineering the best solution possible for our users without having to compromise the quality of tools and services we use,” says SwiftPad CEO and co-founder Amir Motahari.

Real Estate

Gregory Melchior, CEO of real estate software startup 4D Virtual Space, is using social media marketing as a key part of his growth strategy. His organization aims to replace floor plans for real estate developers. Instead of constructing a sales centre, realtors would use the app to show customers through a space, allowing prospective buyers to walk through and even visualize how their own furniture might look.

Feedback from users is vital, and quickly accessed. “With IBM Watson we’re able to get, per month, 500,000 documents analysed in social media immediately,” says Melchior. “We hit the ground running.”

Click here for a free IBM Bluemix trial.

 

 

 

Under Armour Leverages IBM’s Watson to Challenge Fitbit

Thursday, May 12th, 2016

One of the biggest brands in athletic wear is charging into wearable tech with a pack of products and a pair of tech industry partnerships. Under Armour is leveraging artificial intelligence to give its offering an edge over competing products from Nike and Fitbit. 

Retailing for $400, HealthBox is a trio consisting of a Fitbit-like band, a digital scale and a heart-rate monitor. And UA isn’t starting from zero in its effort to tap demand for wearables.

Over the last few years, the company has snagged three massive online fitness communities: Endomondo, MapMyFitness and MyFitnessPal. They now control the largest online wellness-focused ecosystem, at 165 million users. And it’s what HealthBox can do with all that data that makes this a compelling package.

Under Armour partnered with HTC to develop the hardware and a smartphone app called UA Record, which ties all the products together. The UA fitness tracker is cleanly designed, made of a rubber-like material with a LED display. The heart rate monitor is constructed of durable band and the monitor glows when it detects a heartbeat. The scale measures weight and BMI. All the data from the three devices talk to each other and transfer data via Bluetooth to UA Record.

Cognitive Coaching

Under Armour’s partnership with IBM and the “cognitive coaching” potential of Watson differentiates HealthBox from the competition. By feeding nutrition, training, and sleep information into Watson, it’s “able to understand data in large volumes, make recommendations, and continuously learn,” says Chris Glodé, Under Armour’s VP digital, connected fitness. “The more data UA Record inputs, the smarter Watson becomes.”

UA has experimented with Internet of Things in the past. They built a sensor-laden compression T-shirt back in 2011 for the NFL Combine, where college stars worked out for prospective pro teams. The shirt provided raw data on acceleration, speed and heart rate for scouts to pour over. HealthBox and the partnership with IBM means that the Record app will be able to send the data to Watson to disambiguate.

Watson’s Cognitive Coaching uses a comparative model, grouping users based on criteria like age, gender and activity level in order to provide training and recovery recommendations.

And the experience will get richer over time.

“As you record more data and as more data is recorded across the community, the smarter the insights will become,”  Glodé says.

How Much is Cognitive Technology Helping the Raptors?

Tuesday, May 10th, 2016

A year ago the Raptors were victims of an unexpected 4-0 sweep in the first round of the NBA playoffs at the hands of the lower-ranked Washington Wizards. They’re now tied with the Miami Heat 2-2 in a grueling best-of-seven series, which will see the eventual winners play LeBron James and the Cleveland Cavaliers in the Eastern Conference finals.

Maple Leaf Sports & Entertainment, which owns the Raptors, announced in February that it would use cognitive analysis provided by IBM’s Watson technology platform, noting that Watson would be used mostly for talent acquisition.

Is cognitive technology one of the factors behind the improved performance?

It’s difficult to answer that question because the Raptors consider the IBM agreement to be a competitive advantage, and are therefore mum on this subject. With a head coach as tight lipped as Dwane Casey, this is hardly surprising.

There’s only been one addition to the team since IBM and the Raptors announced the use of Watson, so the application of cognitive technology might not be a factor unless they’re using Watson’s analysis for more than recruitment.

Unstructured Data

Traditional analytics-based approaches require data to be tightly structured and presented in a predictable format. Watson is different in that it makes sense of large volumes of unstructured data — video footage, news articles, scientific journals, social media, as examples —  which a few years ago would have required a human to interpret.

And the machine learns. The more data it’s fed, the better its predictions become. Each suggestion is even accompanied by a confidence level rating.

In professional sports, Watson can be used to determine, based on any number of parameters, if a player will be a good fit for a team’s social dynamics. Artificial intelligence can quickly identify, for example, a player who will fill the needs for a particular position, work within salary restrictions, and play well with teammates.

With two crucial contracts — Bismack Biyombo and DeMar DeRozan — on the table at the end of this season, a salary cap to manage and four first-round draft picks over the next two years, the next incarnation of the Raptors lineup is far from clear.

While it’s tough to gauge Watson’s impact on the current season, the platform is likely learning much as it analyzes players on and off the court. Whatever the case, fans should expect the big data addition to play a bigger role in the Raptors’ strategy going forward.