Daily Archives: March 7, 2017

Are two clouds better than one?

How about two nerdily-named artificial intelligence platforms?

According to IBM and Salesforce , the answer to both of those questions is yes.

The two Fortune 500 companies on Monday afternoon revealed a sweeping global strategic partnership that aligns one iconic company's multiyear turnaround effort with another's staggering growth ambitions . According to the terms of the deal, IBM and Salesforce will integrate artificial intelligence platforms (Watson and Einstein, respectively) and some of their software and services (e.g. a Salesforce component to ingest The Weather Companys meteorological data). IBM will also deploy Salesforce Service Cloud internally in a sign of goodwill.

Why not go it alone? Fortune spoke on the phone with IBM CEO Ginni Rometty and Salesforce CEO Marc Benioff to get a better understanding of the motives behind the deal. What follows is a transcript of that conversation, edited and condensed.

Fortune : Hi, guys. So what's this all about?

Benioff : It's great to connect with you again. Artificial intelligence is really accelerating our customers' success and they're finding tremendous value in this new technology. The spring release of Salesforce Einstein has opened our eyes to what's possible. We now have thousands of customers who have deployed this next-generation artificial intelligence capability. I'll tell you, specifically with our Sales Cloud customers, it creates this incredible level of productivity. Sales executives are way more productive than ever beforethe ability to do everything from lead scoring to opportunity insights really opened my eyes that this is possible. So the more value in artificial intelligence we can provide our customers, the more successful they'll be, which is why we're doing this relationship with IBM.

We're able to give our customers the incredible capabilities of not only Einstein but Watson. When you look at the industries we cater toretail, financial services, healthcarethe data and insights that Watson can provide our customers is really incredible. And we're also thrilled that IBM has agreed to use Salesforce products internally as well. This is really taking our relationship to a whole new level.

Rometty : Andrew, thank you for taking the time. This announcement is both strategic and significant. I do think it's really going to take AI further into the enterprise. I think about 2017 as the year when we're going to see AI hit the world at scale. It's the beginning of an era that's going to run a decade or decades in front of us. Marc's got thousands of clients; by the end of this year we'll have a billion people touched by Watson. We both share that vision. An important part of it is the idea that every professional can be aided by this kind of technology. It takes all the noise and turns it into something on which they can take action. It isn't just a sales process; we're going to link other processes across a company. We're talking about being able to augment what everyone doesaugment human intelligence. Together, this will give us the most complete understanding of a customer anywhere.

For our joint customers, to me, this is a really big deal. Take an insurance companyMarc's got plenty of them as clients. You link to insights around weather, hook that into a particular region, tell people to move their cars inside because of hail. You might even change a policy. These two things together do really allow clients to be differentiated.

This is the beginning of a journey together.

I thought this was the brainiest deal I've ever heard of, with Watson and Einstein together.

Rometty : It's good comedy.

Like any two large tech companies, you compete in areas and collaborate in othersfrenemies. Why did you engage in this partnership? Any executive asks themselves: build, buy, or partner. Why partner this time?

Benioff : I'll give you my honest answer here, which is that I've always been a huge fan of IBMGinni knows that. When I look at pioneering values in businesscompanies that have done it right and really stuck to their principles over generationsI really look to IBM as a company that has deeply inspired me personally as I built Salesforce over the last 18 years. We're going to be 18 years old on March 8th. When I look at what we've gone through in the last two decades, I really think that it's our values that have guided us and how those values have been inspired by many of the things at IBM.

Number two is, Ginni made a strategic decision to acquire Bluewolf, which is a company that we had worked very hard to nurture and incubate over a very long period of time. It really demonstrated to me that the opportunity to form a strategic relationship with IBM was possible. We both have this incredible vision for artificial intelligence but we're coming at it from very different areas. [Salesforce is] coming at it from a declarative standpoint, expressed through our platform, for our customer relationship management system. IBM's approach, which is pioneering, especially when it comes to key verticals like retail or finance or healthcarethese are complements. These are the best of both worlds for artificial intelligence. These are the two best players coming together. We have almost no overlap in our businesses. We have really a desire to make our customers more successful.

Rometty : Beautifully said. And I'll only add a couple of points. Not only sharing values as companies but in terms of how we look at our customers. We share over 5,000 joint clients. But more importantly, think about this era of AI. There are different approaches you can take. What Marc's done with Einsteinthink of it as CRM as a process. What we've done with Watsonthink of it as an industry in depth. We do have very little overlap. Why we talk about Watson as a platform is to be integrated with things like what Marc's doing.

Let me ask you about AI. It's been in development for decades, but the current wave is nascent. How do you each see AI as part of the success of your companies? It's a capabilityno one goes to the store to buy AI. Hopefully it solves their problems. But AI can be anything.

Rometty : I view AI as fundamental to IBM. Watson on the IBM cloudthat's a fundamental and enduring platform. We've built platforms for ourselves before: mainframe, middleware, managed services. This is now the era of AI. It will be a silver thread through all of what IBM does.

Is it fair to say that you guys aren't trying to compete on AI? I don't mean between youI mean within the greater industry.

Rometty : We're absolutely complementary. Clients will make some architectural decisions here. Everyone's gonna pick some platforms to use. They will pick them around AI. By the way, there are stages: the most basic is machine learning, then AI, then cognitive [computing]. What we're doing with Marc goes all the way into cognitive. Just to be clear.

Benioff : I could not agree more. We brought our customers into the cloud, then into the social world, then into the mobile world. Now we're bringing them into the AI world.

This is really beyond my wildest dreams in terms of what's possible today. And by the waythat we're able to replace Microsoft's products [at IBM] is a bonus for us. (laughs)

Read more here:

IBM, Salesforce Strike Global Partnership on Cloud, AI - Fortune

GETTY

Microsoft and Cambridge University have teamed up to create AI software which has the ability to write code for itself.

The sophisticated machine known as DeepCoder has the ability to solve its programming problems by stealing codes from other programs.

The research paper from the two establishments says that the development is a huge step towards powerful AI and will also allow people to develop programs much easier.

The paper reads: We have found several problems in real online programming challenges that can be solved with a program in our language.

GETTY

Jason Dorfman, MIT CSAIL

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"A dream of artificial intelligence is to build systems that can write computer programs.

Coding has been described as one of the most important skills of the future, and a recent survey from job markets firm Burning Glass found that as many as seven million job openings in 2015 required some form of coding skills.

GETTY

But with AI now having the ability to code itself, it could put many budding coders out of work.

A recent report from the United Nations (UN) revealed AI is set to displace millions of workers across the globe as scientists storm towards making machines with human-level intelligence.

While many firms will welcome the news of free labour that will be more efficient than humans, it will leave many people worried about their economic future.

The report from the UN warn that people in the developing world are at the most risk of losing their jobs to disruptive technologies and the study states that the process is already in full swing.

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RISE OF THE MACHINES: AI computers learn to code THEMSELVES in major development - Express.co.uk

Imagining AIs Doomsday Artificial intelligence (AI) is going to transform the world, but whether it will be a force of good or evil is still subject to debate. To that end, a team of experts gathered for Arizona State Universitys (ASU) Envisioning and Addressing Adverse AI Outcomes to talk about the worst-case scenarios that we could face if AI veers towards becoming a serious threat to humanity.

There is huge potential for AI to transform so many aspects of our society in so many ways. At the same time, there are rough edges and potential downsides, like any technology, says AI scientist Eric Horvitz.

As an optimistic supporter of everything AI has to offer, Horvitz has a very positive outlook about the future of AI. But hes also pragmatic enough to recognize that for the technology to consistently advance and move forward, it has to earn the trust of the public. For that to happen, all possible concerns surrounding the technology have to be discussed.

That conversation specificallywas what the workshop hoped to tackle.40 scientists, cyber-security experts, and policy-makers were divided into two teams to hash out the numerous ways AI can cause trouble for the world. The red team were tasked with imagining all the cataclysmic scenarios AI could incite, and the blue team was asked to devisesolutions to defend against such attacks.

These situations had to be realistic rather than purely hypothetical, anchored in whats possible given our current technology, and what we expect to come from AI over the next few decades.

Among the scenarios described were automated cyber attacks (wherein a cyber weapon is intelligent enough to hide itself after an attack and prevent all efforts to destroy it), stock markets being manipulated by machines, self-driving technology failing to recognize critical road signs, and AI being used to rig or sway elections.

Not all scenarios were given sufficient solutions either, illustrating just how unprepared we are at present to face the worse possible situations that AI could bring. For example, in the case of intelligent, automated cyber attacks, it would apparently be quiteeasy for attackers to use unsuspecting internet gamers to cover their tracks, using something like an online game toobscure the attacks themselves.

As entertaining as it may be to think up all of these wild doomsday scenarios, its actually a deliberate first step towards real conversations and awareness about the threat that AI could pose. John Launchbury, from the US Defenses Advanced Research Projects Agency hopes it will lead to concrete agreements on rules of engagement for cyber war, automated weapons, and robot troops.

The purpose of the workshop after all, isnt to incite fear, but to realistically anticipate the various possibilities of how technology can be misused and hopefully, get a head start on defending ourselves againstit.

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What Would an AI Doomsday Actually Look Like? - Futurism

More investors are setting their sights on the financial technology (Fintech) arena. According to consulting firm Accenture, investment in Fintech firms rose by 10 percent worldwide to the tune of $23.2 billion in 2016.

China is leading the charge after securing $10 billion in investments in 55 deals which account for 90 percent of investments in Asia-Pacific. The US came second taking in $6.2 billion in funding. Europe, also saw an 11 percent increase in deals despite Britain seeing a decrease in funding due to the uncertainty from the Brexit vote.

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The excitement stems from the disruption of traditional financial institutions (FIs) such as banks, insurance, and credit companies by technology. The next unicorn might be among the hundreds of tech startups that are giving Fintech a go.

What exactly is going to be the next big thing has yet to be determined, but other developments in computing like artificial intelligence (AI) may play a huge part.

The growing reality is that, while opportunities are abound, competition is also heating up.

Take, for example, the number of Fintech startups that aim to digitize routine financial tasks like payments. In the US, the digital wallet and payments segment is fiercely competitive. Pioneers like PayPal see themselves being taken on by other tech giants like Google and Apple, by niche-oriented ventures like Venmo, and even by traditional FIs.

Some ventures are seeing bluer oceans by focusing on local and regional markets where conditions are somewhat favorable.

The growth of Chinas Fintech was largely made possible by the relative age of its current banking system. It was easier for people to use mobile and web-based financial services such as Alibabas Ant Financial and Tencent since phones were more pervasive and more convenient to access than traditional financial instruments.

In Europe, the new Payment Services Directive (PSD2) set to take effect in 2018 has busted the game wide open. Banks are obligated to open up their application program interfaces (APIs) enabling Fintech apps and services to tap into users bank accounts. The line between banks and fintech companies are set to blur so just about everyone in finance is set to compete with old and new players alike.

Convenience has become a fundamental selling point to many users that a number of Fintech ventures have zeroed in on delivering better user experiences for an assortment of financial tasks such as payments, budgeting, banking, and even loan applications.

There is a mad scramble among companies to leverage cutting-edge technologies for competitive advantage. Even established tech companies like e-commerce giant Amazon had to give due attention to mobile as users shift their computing habits towards phones and tablets. Enterprises are also working on transitioning to cloud computing for infrastructure.

But where do more advanced technologies such as AI come in?

The drive to eliminate human fallibility has also made artificial intelligence (AI) driven to the forefront of research and development. Its applications range from sorting what gets shown on your social media newsfeed to self-driving cars. Its also expected to have a major impact in Fintech due to potential of game changing insights that can be derived from the sheer volume of data that humanity is generating. Enterprising ventures are banking on it to expose the gap in the market that has become increasingly small due to competition.

AI and finance are no strangers to each other. Traditional banking and finance have relied heavily on algorithms for automation and analysis. However, these were exclusive only to large and established institutions. Fintech is being aimed at empowering smaller organizations and consumers, and AI is expected to make its benefits accessible to a wider audience.

AI has a wide variety of consumer-level applications for smarter and more error-free user experiences. Personal finance applications are now using AI to balance peoples budgets based specifically to a users behavior. AI now also serves as robo-advisors to casual traders to guide them in managing their stock portfolios.

For enterprises, AI is expected to continue serving functions such as business intelligence and predictive analytics. Merchant services such as payments and fraud detection are also relying on AI to seek out patterns in customer behavior in order to weed out bad transactions.

People may soon have very little excuse of not having a handle of their money because of these services

Despite the exciting potential AI brings, there are still caveats. A big challenge for Fintech is to develop AI to be as smart as it can. There will be no shortage of people who will try to game and outwit such systems.

While AI seeks to eliminate human error, the flipside losing the human touch is a common criticism of AI. Smart money decisions are best made through numbers and logic. However, people do have an emotional connection with their money so it will be a challenge for Fintech apps to create experiences that do not alienate its users. Take the sad stories of insurance claims being denied due to strict algorithms that disregard the nuances of the human condition. AI still has a way to go factoring in what is just and moral in its decision making.

As for finance as a field and industry, there is also the issue of financial analysts, advisors, bankers, and traders being threatened to obsolescence by AI. A running joke with AI alludes to the Terminator movie franchise where AI seeks to eliminate humanity from existence. Unemployment, however, is rarely a laughing matter.

With the stiff competition in Fintech, ventures have to deliver a truly valuable products and services in order to stand out. The venture that provides the best user experience often wins but finding this X factor has become increasingly challenging.

The developments in AI may provide that something extra especially if it could promise to eliminate the guess work and human error out of finance. Its for these reasons that AI might just hold the key to what further Fintech innovations can be made.

This post is part of our contributor series. It is written and published independently of TNW.

Read next: TNW's 5 rules for writing the perfect cold email

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Why AI will determine the future of fintech - TNW

ZDNet

AI: the promise of big data ZDNet You'll be aware of the recent explosion of interest in AI, and most likely have seen it in action too, in the form of smart assistants on phones, desktops (think Cortana) and elsewhere. Pretty soon, the ability of machines to achieve a greater ...

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AI: the promise of big data - ZDNet

A GeoVisual search for baseball stadiums in the lower 48.

Want to know where all the wind and solar power supplies in the US are for some brilliant renewable-energy project? Or plot a round-the-world trip hitting every major soccer stadium along the way? It should be possible with a new tool that lets anyone scan the globe through AI "eyes" to instantly find satellite images of matching objects.

Descartes Labs, a New Mexico startup that provides AI-driven analysis of satellite images to governments, academics and industry, on Tuesday released a public demo of its GeoVisual Search, a new type of search engine that combines satellite images of Earth with machine learning on a massive scale.

The idea behind GeoVisual is pretty simple. Pick an object anywhere on Earth that can be seen from space, and the system returns a list of similar-looking objects and their locations on the planet. It's cool to play with, which you can do at the Descartes site here. A short search for wind turbines had me dreaming of a family road trip where every pit stop was sure to include kite-flying for the kids.

Perhaps this sounds just like Google Earth to you, but keep in mind that tool just allows you to find countless geotagged locations around the world. GeoVisual Search actually compares all the pixels making up huge photos of the world to find matching objects as best it can, an ability that hasn't been available to the public before on a global scale.

Mark Johnson, Descartes Lab CEO

Fun as it is, the tool also gives the public a taste of Descartes' broader work, which so far has focused largely on agricultural datasets that can do things like analyze crop yields.

"The goal of this launch is to show people what's possible with machine learning. Our aim is to use this data to model complex planetary systems, and this is just the first step," CEO and co-founder Mark Johnson said via email. "We want businesses to think about how new kinds of data will help to improve their work. And I'd like everyone to think about how we can improve our life on this planet if we better understood it."

The tool's not perfect. I tried searching for objects that look similar to a large coal mine and power plant here in northern New Mexico and ended up with a list of mostly similar-looking lakes and bridges. Searching for locations similar to the launch pads at Cape Canaveral returned an odd assortment of landscapes that seemed to have nothing in common besides a passing resemblance to concrete surfaces.

The algorithm can easily mistake a whole lot of coal for a whole lots of water.

"Though this is a demo, GeoVisual Search operates on top of an intelligent machine-learning platform that can be trained and will improve over time," Johnson said. "We've never taught the computer what a wind turbine is, it just determines what's unique about that image (i.e., the fact there is a wind turbine there) and automatically recognizes visually similar scenes."

Right now the demo relies on three different imagery sources that include more than 4 petabytes of data altogether. You can search in the most detail using the National Agriculture Imagery Program (NAIP) data for the lower 48 United States because it has the highest resolution of one meter per pixel, making it possible to spot orchards, solar farms and turbines, among other objects.

Four-meter imagery is available for China that makes it possible to recognize slightly larger things like stadiums. For the rest of the world, Descartes uses 15-meter resolution images from Landsat 8 that are more coarse but still allow for identification of larger-scale objects like pivot irrigation and suburbs.

"As a next step, we certainly want to start to understand specific objects and count them accurately through time," Johnson said. "At that point, we'll have turned satellite imagery into a searchable database, which opens up a whole new interface for dealing with planetary data."

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Earth's recent changes, from space (pictures)

Descartes was spun out of Los Alamos National Lab (LANL) and co-founded by Steven Brumby, who spent over a decade working in information sciences for the lab. Near the start of his time at LANL, a massive wildfire nearly destroyed the lab and Brumby's home. More importantly, it sparked Brumby's interest in developing machine-learning tools to map the world's fires.

"At that time when we did the analysis (of satellite images of the fire's aftermath) it was pretty clear the fire had been catastrophic, but there was a lot of fuel left," Brumby told me when I visited Descartes' offices in Los Alamos last year.

When some of that remaining fuel burned in another big Los Alamos wildfire in 2011, Brumby says he was able to help out. During his time at LANL he was often called on for imagery analysis when disaster struck, from 9/11 to Hurricane Katrina and the breakup of the Space Shuttle Columbia. All those years of insight led to another Descartes project to analyze satellite imagery to better understand and perhaps even predict wildfires around the globe.

"You can use satellite imagery to warn you of stuff that's coming down the road and if you listen to it, you can be prepared for it," Brumby said.

Before and after the 2000 Cerro Grande fire, with the burn scar shown in bright red.

Brumby and Johnson spent the better part of an afternoon laying out the short- and long-term vision for Descartes Labs when I visited. In the short term, the company has been working in agriculture to better monitor crops, feed lots and other data sources.

"One of the things we're building with our current system is a continuously updating living map of the world, which is the platform I wish we had when we had to deal with some of these disasters back in the day," Brumby said.

Being able to check in on any part of the world in real time is one thing, but Descartes hopes to go further by applying artificial intelligence to see things in all those images that might not be immediately obvious to our eyes: the patterns that tie together all the activities captured in those countless pixels.

If a picture really is worth a thousand words, tools like the ones Descartes is developing could help write volumes about what our satellites are really seeing.

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Search Earth with AI eyes via a powerful new satellite image tool - CNET