Daily Archives: February 23, 2017

Getty Images/iStockphoto

AI is mainstream these days. The attention it gets and the feelings it provokes cover the whole gamut: from hands-on technical to business, from social science to pop culture, and from pragmatism to awe and bewilderment. Data and analytics are a prerequisite and an enabler for AI, and the boundaries between the two are getting increasingly blurred.

Many people and organizations from different backgrounds and with different goals are exploring these boundaries, and we've had the chance to converse with a couple of prominent figures in analytics and AI who share their insights.

IoT: The Security Challenge

The Internet of Things is creating serious new security risks. We examine the possibilities and the dangers.

Professor Mark Bishop is a lot of things: an academic with numerous publications on AI, the director of TCIDA (Tungsten Centre for Intelligent Data Analytics), and a thinker with his own view on why there are impenetrable barriers between deep minds and real minds.

Bishop recently presented on this topic in GOTO Berlin. His talk, intriguingly titled "Deep stupidity - what deep Neural Networks can and cannot do," was featured in the Future of IT track and attracted widespread interest.

In short, Bishop argues that AI cannot become sentient, because computers don't understand semantics, lack mathematical insight and cannot experience phenomenal sensation -- based on his own "Dancing with Pixies" reductium.

Bishop however is not some far-out academic with no connection to the real world. He does, when prompted, tend to refer to epistemology and ontology at a rate that far surpasses that of the average person. But he is also among the world's leading deep learning experts, having being deeply involved in neural networks before it was cool.

"I was practically mocked when I announced this was going to be my thesis topic, and going from that to seeing it in mainstream news is quite the distance," he notes.

His expertise has earned him more than recognition and a pet topic, however. It has also gotten him involved in a number of data-centric initiatives with some of the world's leading enterprises. Bishop, about to wrap up his current engagement with Tungsten as TCIDA director, notes that going from academic research and up in the sky discussions to real-world problems is quite the distance as well.

"My team and myself were hired to work with Tungsten to add more intelligence in their SaaS offering. The idea was that our expertise would help get the most out of data collected from Tungsten's invoicing solution. We would help them with transaction analysis, fraud detection, customer churn, and all sorts of advanced applications.

But we were dumbfounded to realize there was an array of real-world problems we had to address before embarking on such endeavors, like matching addresses. We never bothered with such things before -- it's mundane, somebody must have addressed the address issue already, right? Well, no. It's actually a thorny issue that was not solved, so we had to address it."

Injecting AI in enterprise software is a promising way to move forward, but beware of the mundane before tackling the advanced

Steven Hillion, on the other hand, comes at this from a different angle. With a PhD in mathematics from Berkeley, he does not lack relevant academic background. But Hillion made the turn to industry a long time ago, driven by the desire to apply his knowledge to solve real-world problems. Having previously served as VP of analytics for Greenplum, Hillion co-founded Alpine Data, and now serves as its CPO.

Hillion believes that we're currently in the "first generation" of enterprise AI: tools that, while absolutely helpful, are pretty mundane when it comes to the potential of AI. A few organizations have already moved to the second generation, which consists of a mix of tools and platforms that can operationalize data science -- e.g. custom solutions like Morgan Stanley's 3D Insights Platform or off the shelf solutions such as Salesforce's Einstein.

In many fields, employees (or their bosses) determine the set of tasks to focus on each day. They log into an app, go through a checklist, generate a BI report, etc. In contrast, AI could use existing operational data to automatically serve up the highest priority (or most relevant, or most profitable) tasks that a specific employee needs to focus on that day, and deliver those tasks directly within the relevant application.

"Success will be found in making AI pervasive across apps and operations and in its ability to affect people's work behavior to achieve larger business objectives. And, it's a future which is closer than many people realize. This is exactly what we have been doing with a number of our clients, gradually injecting AI-powered features into the everyday workflow of users and making them more productive.

Of course, this isn't easy. And in fact, the difficult aspect of getting value out of AI is as much in solving the more mundane issues, like security or data provisioning or address matching, as it is in working with complex algorithms."

Before handing over to AI overlords, it may help to actually understand how AI works

So, do androids dream of electric sheep, and does it matter for your organization? Although no definitive answers exist at this point, it is safe to say that both Bishop and Hillion seem to think this is not exactly the first thing we should be worried about. Data and algorithmic transparency on the other hand may be.

10 types of enterprise deployments

As businesses continue to experiment with the Internet of Things, interesting use cases are emerging. Here are some of the most common ways IoT is deployed in the enterprise.

Case in point -- Google's presentation on deep learning preceding Bishop's one in GOTO. The presentation, aptly titled "Tensorflow and deep learning, without a PhD", did deliver what it promised. It was a step-by-step, hands-on tutorial on how to use Tensorflow, Google's open source toolkit for deep learning, given by Robert Kubis, senior developer advocate for the Google Cloud Platform.

Expectedly, it was a full house. Unexpectedly, that changed dramatically as the talk progressed: by the end, the room was half empty, and a lukewarm applause greeted off Kubis. Bishop's talk, by contrast, started with what seemed like a full house, and ended proving there could actually be more people packed in the room, with a roaring applause and an entourage for Bishop.

There is an array of possible explanations for this. Perhaps Bishop's delivery style was more appealing than Kubis' -- videos of AI-generated art and Bladerunner references make for a lighter talk than a recipe-style "do A then B" tutorial.

Perhaps up in the sky discussions are more appealing than hands-on guides for yet another framework -- even if that happens to be Google's open source implementation of the technology that is supposed to change everything.

Or maybe the techies that attended GOTO just don't get Tensorflow -- with or without a PhD. In all likelihood, very few people in Kubis' audience could really connect with the recipe-like instructions delivered and understand why they were supposed to take the steps described, or how the algorithm actually works.

And they are not the only ones. Romeo Kienzler, chief data scientist at IBM Watson IoT, admitted in a recent AI Meetup discussion: "we know deep learning works, and it works well, but we don't exactly understand why or how." The million dollar question is -- does it matter?

After all, one could argue, not all developers (need to) know or care about the intrinsic details of QSort or Bubble Sort to use a sort function in their APIs -- they just need to know how to call it and trust it works. Of course, they can always dig into commonly used sort algorithms, dissect them, replay and reconstruct them, thus building trust in the process.

Deep learning and machine learning on the other hand are a somewhat different beast. Their complexity and their way of digressing from conventional procedural algorithmic wisdom make them hard to approach. Coupled with vast amounts of data, this makes for opaque systems, and adding poor data quality to the mix only aggravates the issue.

It's still early days for mainstream AI, but dealing with opaqueness may prove key to its adoption.

How the cloud enables the AI revolution:

Continue reading here:

Artificial intelligence in the real world: What can it actually do? - ZDNet

Forbes

This Cognitive Whiteboard Is Powered By Artificial Intelligence Forbes Imagine if the whiteboard in your next corporate meeting could take notes when you talked and add comments from your teammates in the meeting. The wait could be over soon. IBM and Ricoh Europe have announced an interactive whiteboard with artificial ... and more »

Original post:

This Cognitive Whiteboard Is Powered By Artificial Intelligence - Forbes

Engineering | Honors and awards | News releases | Research | Technology | UW Today blog

February 23, 2017

Carlos Guestrin in the Paul G. Allen Center for Computer Science & Engineering at the UW.Dennis Wise/ University of Washington

University of Washington Computer Science & Engineering announced today the establishment of the Guestrin Endowed Professorship in Artificial Intelligence and Machine Learning. This $1 million endowment will further enhance UW CSEs ability to recruit and retain the worlds most outstanding faculty members in these burgeoning areas.

The professorship is named for Carlos Guestrin, a leading expert in the machine learning field, who joined the UW CSE faculty in 2012 as the Amazon Professor of Machine Learning. Guestrin works on the machine learning team at Apple and joined Apple when it acquired the company he founded, Seattle-based Turi, Inc. Guestrin is widely recognized for creating the high-performance, highly-scalable machine learning technology first embodied in his open-source project GraphLab.

At Apple, Guestrin is helping establish a new Seattle hub for artificial intelligence and machine learning research and development, as well as strengthening ties between Apple and UW researchers.

Appleincorporates machine learning across our products and services, and education has been a part ofApples DNA from the very beginning. said Johny Srouji, senior vice president of Hardware Technologies at Apple.

Seattle and UW are near and dear to my heart, and it was incredibly important to me and our team that we continue supporting this world-class institution and the amazing talent coming out of the CSE program, said Guestrin. We look forward to strong collaboration betweenApple, CSE and the broader AI and machine learning community for many years to come.

For more information, contact Ed Lazowska, Bill & Melinda Gates Chair in Computer Science & Engineering at lazwoska@cs.washington.edu or Guestrin at guestrin@cs.washington.edu.

Read more here:

UW CSE announces the Guestrin Endowed Professorship in Artificial Intelligence and Machine Learning - UW Today

Artificial intelligence: What's real and what's not in 2017 The Business Journals I'm a big Star Wars fan, so when Rogue One: A Star Wars Story descended on theaters this month, I of course braved the crowds to see it twice in the first 18 hours. And just like all the other Star Wars movies, Rogue One stoked our geeky ... Rogue One: A Star Wars Story In-Home Trailer (Official)YouTube The Mission Comes Home: Rogue One: A Star Wars Story Arrives Soon on Digital HD and Blu-ray | StarWars.comStarWars.com all 79 news articles »

Here is the original post:

Artificial intelligence: What's real and what's not in 2017 - The Business Journals

Artificial intelligence is not going away. But we have a choice whether to embrace it or fear it. PHOTO: ambermb

The argument against artificial intelligence (AI) is driven by fear. Fear of the unknown fear of intelligence.

According to Stephen Hawkings we do have reason to beware of the consequences of artificial intelligence (AI) including the possibility of the end of the human race.

The rise of the machines wont be happening imminently. After all, AI is still in its infancy. The most realistic fear today is that AI will take peoples jobs.

Undoubtedly technology is taking peoples jobs in droves. Anytime you self-checkout in the grocery store you might be conveniencing yourself but youre also doing something that just 15 years ago someone would have been paid to do for you.

The trend is also happening in casual type restaurants such as Red Robin, where machines are on the table that do everything but bring you the food itself.

Airlines use self-serve kiosks to print luggage tags and boarding passes. Banks use intelligent automated voices to route calls and do practically everything unless you specifically ask for a representative.

It doesnt exactly take a forward thinker to envision a time when cars are self-driving. And with the technological advancement of drones, its not hard to imagine that commercial planes will one day be pilotless.

While Moores Law implies technology doubles every two years, the reality is humans are notoriously slow at adopting it.

Weve been trained to think of new technology as cost prohibitive and buggy. We let tech savvy pioneers test new things and we wait until the second or third iteration, when the technology is ready, before deciding to adopt it.

While AI seems like a futuristic concept, its actually something that many people use daily, although 63 percent of users dont realize theyre using it.

Google is a great example of machine learning that many people use every day and it truly does make life easier. Marketers use artificial intelligence for a variety of functions, not the least of which include personalization. The reason that Netflix or Amazon are able to give you personalized suggestions is because the technology that runs their software uses AI.

While the fear of job loss is understandable, there is another point to make: because of artificial intelligence many people are currently doing jobs that werent available even just a few years back.

Lets circle back to marketers for example. The technological know-how is now a full-time job, so alongside designers and copywriters is a new breed of marketer that is trained to purposefully promote content to a uniquely tailored audience.

Even so, when you Google which new jobs will AI produce, you only get a list of articles saying AI will eliminate jobs.

Of course, fear typically drives more clicks than positivity, so its not surprising that more articles focus on the negative aspects of AI than the good that many people proclaim will come from it.

Were currently in a situation where the new US Presidential administration that has made a mantra out of saving American jobs.

To date, the jobs the administration is focusing on are jobs that will be taken over by intelligent machines in the not-to-distant future.

Retaining jobs is important, but with a strategy around educating people on the coming technology, long-term retention of jobs would be a lot more realistic. Manufacturing is becoming less about screwing parts together and more about robotic maintenance and foresight.

No leader should want to stop this advancement, but a leader should recognize the future and see to a long-term solution rather than a short-term one.

The previous administration did study the impact of AI on our economy. The White House study, Artificial Intelligence, Automation, and the Economy doesnt sugarcoat the fact that AI will take peoples jobs as many as 47 percent in the next decade. It also goes on to emphasize that these jobs will be replaced with others, and that a focus on education and investments in the industry are vital.

AI informed intelligence software will always learn from current scenarios. It is only as good as the programmers, according to Kitty Parr, founder and CEO of Social Media Compliance (SMC), in ComputerWeekly.com. If thats the case, certainly programmers have a bright future.

Even software companies not at the scale of Google or Amazon are already using AI and creating jobs at the same time. Take my company, censhare, a Munich-based digital experience company. We've been running a semantic network, a fancy term for AI, since 2001. Besides the jobs at censhare that AI produces, its customer base needs people who can run the software as well.

You can extract from the above paragraph that there are many companies on the forefront of this new technology and they all need developers, marketers, sales, support, leadership and everyone else involved in running a company.

Intelligent machines arent going to start running companies, people will continue making the glue that holds corporations together.

Artificial intelligence is not going away.

We have a choice whether to embrace it or fear it.

People who embrace it from the start will inevitably end up ahead, while those who choose to fear or even ignore it will be left playing catch-up. The latter is who will end up losing jobs while the former will continue doing what they love, just maybe in a slightly different way.

Douglas Eldridge has worked in marketing/communications since 2003. As marketing manager for censhare US, he is tasked with strategizing and implementing digital marketing efforts in the US, utilizing both inbound and outbound methods.

Follow this link:

Why the Benefits of Artificial Intelligence Outweigh the Risks - CMSWire

Irish companies are seen to be particularly aware of the changing role for AI with 71 per cent of those surveyed saying they believe it will revolutionise the way they gain information from and interact with customers

As many as three quarters of Irish companies believe artificial intelligence (AI) will have a major impact on their industry in the coming years, with 25 per cent expecting it to completely transform their sector.

Thats according to Accentures annual Technology Vision 2017 report, which reports on the most disruptive tech trends for businesses.

The survey of more than 5,400 business and IT executives across 16 industries and 31 countries, including Ireland, indicates that AI is moving far beyond being a back-end tool to take on a more sophisticated role within companies.

Irish companies are seen to be particularly aware of the changing role for AI with 71 per cent of those surveyed saying they believe it will revolutionise the way they gain information from and interact with customers. Almost three quarters of those surveyed also expect AI interfaces to become their primary interface for interacting with the outside world.

However, the rise of AI is not without challenges with 41 per cent of Irish companies expecting compatibility issues to impact take-up within firms. Other potential problems cited included privacy issues, a lack of sufficient usable data and the newness of such technology.

When it comes to AI investment over the next three years, the most significant areas where Irish businesses plan to invest capabilities are in natural language processing, computer vision, machine learning, deep learning, and in embedded AI solutions such as IPsofts Amelia in call centre services, or IBMs Watson embedded in healthcare diagnostics.

The research indicates that many Irish organisations are racing to keep up with advances in technology, with one in five surveyed saying their industry is facing complete disruption and a further 48 per cent experiencing moderate disruption over the next three years.

Read more:

Irish companies preparing for artificial intelligence revolution - Irish Times