Problems? See this thread at archive.org.-
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HPC AND SUPERCOMPUTING
The HPC and Supercomputing Track features sessions from industry experts on topics including simulation, visualization, and deep learning. GTC recordings focus on how computational and data science are used to solve traditional HPC problems in healthcare, weather, astronomy, and other domains.
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Yeah, that's the point. You can usually tell by the comments how people feel about the subject / person of the video, and whether they are for or against them by checking the top upvoted comments.
Nvidia isn't at all highly rated given those responses. And, that's supposed to be a professional HPC event.
The popular sentiment runs contrary to the claim that Nvidia has been "crowned the smartest company in the world right now", that's simply the proclamation of paid PR.
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And what is AMD's anwer to this?
Only $3K
NVIDIA Announces “NVIDIA Titan V" Video Card: GV100 for $3000, On Sale Now
Edit. See also
Wanna Go SLI With Titan V? NVLINK Bridge costs 599 USDLast edited: Dec 8, 2017Dr. AMK likes this. -
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Leaked NVIDIA TITAN V Benchmarks Show Volta GPU Demolishing All Competitors
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"Unigine's Superposition benchmark also yielded some impressive numbers. At stock speeds, the TITAN V scored 5,222 in the 8K preset, and 9,431 in the 1080p Extreme preset. The latter is particularly interesting—famed overclocker Kingpin had previously taken a GeForce GTX 1080 Ti, stripped off the heatsink and bathed the card in liquid nitrogen (LN2), and overclocked it to 2,581MHz, which resulted in a score 8,642 in the 1080p Extreme preset. The TITAN V scored nearly 800 points higher."
"Of course, NVIDIA is not in a rush to bring Volta to the consumer market, as AMD has not fully caught up with Pascal (Vega comes close). The silver lining to that is it gives NVIDIA time to tweak things and flesh out better drivers for when Volta does infiltrate the mainstream gaming sector. Based on what we've seen here, we can hardly wait."
And from the RED camp...
AMD quietly made some Radeon RX 560 graphics cards worseLast edited: Dec 9, 2017Dr. AMK likes this. -
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Where Is AI Headed in 2018?
13 predictions by researchers and experts from around the world.
Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 “The Year of AI.” And with good reason.
AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE’s Avitas Systems invested in deep learning supercomputers.
These are a few of many milestones in 2017. So what’s next?
We’ve gathered predictions from the world’s leading researchers and industry thought leaders.
AI Will Become Real for Medicine
“2018 will be the year AI becomes real for medicine. We’re going to move from algorithms to products and think more about integration and validation, so that these solutions can move from concepts to real, tangible solutions for our doctors. By the end of next year, I think around half of leading healthcare systems will have adopted some form of AI within their diagnostic groups. And while a lot of this adoption will happen first in the diagnostic medical specialties, we’re seeing solutions for population health, hospital operations and a broad set of clinical specialties quickly follow behind. In 2018, we’ll begin the adoption of a technology that may truly transform the way providers work, and the way patients experience healthcare, on a global scale.” – Mark Michalski, executive director, Massachusetts General Hospital and Brigham and Women’s Center for Clinical Data Science
Deep Learning Will Revolutionize Engineering Simulation and Design
“2018 will be the year deep learning starts a revolution in engineering simulation and design. Over the next three to five years, deep learning will accelerate product development from years to months and weeks to days to create a new paradigm of rapid innovation in product features, performance and cost.” – Marc Edgar, senior information scientist, GE Research
AI Will Be Considered Part of a “Regular” Clinical System
“AI in 2018 and in the coming years will be so embedded into our clinical systems that it will no longer be called AI but rather just a regular system. And people will ask themselves: ‘How were we able to live without these systems in the past?’” – Luciano Prevedello, M.D., M.P.H., Radiology & Neuroradiology, Ohio State University Wexler Medical Center
AI Will Be Considered a Mainstream Content Creator
“Given the rapid pace of research, I expect AI to be able to create new personalized media, such as music according to your taste. Imagine a future music service that doesn’t just play existing songs you might like, but continually generates new songs just for you.” – Jan Kautz, senior director of Visual Computing and Machine Learning Research, NVIDIA
Technology Will Continue to Adapt to AI
“AI is going to affect 25 percent of technology spend going forward. The key topic is how organizations and the human workforce will cope with the changes that AI technologies will bring.” – Nicola Morini Bianzino, managing director of Artificial Intelligence and growth & strategy lead of Technology, Accenture
Biometrics Will Replace Credit Cards and Driver’s Licenses
“Thanks to AI, the face will be the new credit card, the new driver’s license and the new barcode. Facial recognition is already completely transforming security with biometric capabilities being adopted, and seeing how tech and retail are merging, like Amazon is with Whole Foods, I can see a near future where people will no longer need to stand in line at the store.” – Georges Nahon, CEO, Orange Silicon Valley; president, Orange Institute, a global research co-laboratory
New Deep Learning Techniques Will Provide Transparency into How Data Is Processed
“Deep learning will significantly increase the quantitative content of radiology reports. There will be much fewer concerns about deep learning being a ‘black box,’ as new techniques will help us understand what DL is ‘seeing.’” – Bradley J. Erickson, M.D., Ph.D., consultant for Department of Radiology; consultant for Division of Biomedical Statistics and Informatics, Department of Health Sciences Research; associate chair of Research, Department of Radiology, Mayo Clinic
AI and Deep Neural Networks Will Be Accessible on Smartphones
“Vast applications on smartphones will run deep neural networks to enable AI. Friendly robots will start to emerge as more affordable and rise as the new platform at home. They will start to bridge vision, language and speech in such a way that the users will not be conscious about the difference between these communication modalities.” – Robinson Piramuthu, chief scientist for computer vision, eBay
AI Will More Fully Integrate into Daily Life
“Robots are going to get better at complex tasks that humans still take for granted, like walking around a room and over objects. They’ll get better at mastering boring, normal things. I’m looking forward to seeing progress in NLP tasks as well, since right now we’ve got a ways to go. We’re going to see more and more products that contain some form of AI enter our lives. Waymo’s level 4 autonomous vehicles are deployed on the road now. So all this stuff that’s been tested in the lab will become more common and available. It will touch more lives.” – Chris Nicholson, CEO and co-founder, Skymind.io
AI Development Will Be More Diverse
“We will start seeing more and more people from all kinds of backgrounds participating in building, developing and productizing AI. Tooling and infrastructure will continue to improve and make it easier for more people to translate their data and algorithms into real-world usage. Products and apps will allow more interactive querying of the inner workings of the underlying models, with the result being increased trust and confidence in these systems, particularly in mission-critical applications. In medicine, we will see more aggregation of disparate sources of information spanning across many disciplines, rather than focusing on single-application cases, though the scope of these targeted applications will continue to expand at a feverish pace.” – George Shih, founder, MD.ai; associate professor and vice chair, Informatics, Department of Radiology, Weill Cornell Medicine
AI Will Open a New Field of Research in Contemporary Astrophysics
“AI will enable the detection of an unexpected astrophysical event that emits gravitational waves, opening a new field of research in contemporary astrophysics.” – Eliu Huerta, astrophysicist and head of the gravity group, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
AI Will Translate from the Research Lab to the Patient Bedside
“AI in imaging is reaching the peak of the ‘hype curve,’ and we will begin to see AI-enabled tools translate from the research lab to the radiologist workstation and ultimately the patient bedside. The not so glamorous use cases (for example, workflow tools, quality/safety, patient triage, etc.) for AI evaluation and implementation will start grabbing the attention of developers, insurance companies, healthcare organizations and institutions. One of the biggest challenges the medical and imaging AI industry will face is the ability of regulators to keep up with the innovation that is occurring. The FDA will need to find efficient and streamlined methodologies to vet and approve algorithms that will be used to screen, detect and diagnose disease.” – Safwan Halabi, medical director of Radiology Informatics, Stanford Children’s Health, Lucile Packard Children’s Hospital
AI Personal Assistants Will Continue to Get Smarter
“Personal assistant AIs will keep getting smarter. As our personal assistants learn more about our daily routines, I can imagine the day I need not to worry about preparing dinner. My AI knows what I like, what I have in my pantry, which days of the week I like to cook at home, and makes sure that when I get back from work all my groceries are waiting at my doorstep, ready for me to prepare that delicious meal I had been craving.” – Alejandro Troccoli, senior research scientist, NVIDIALast edited: Mar 11, 2018 -
Where is AI headed in 2018? Your phone will know you better than your friends do, researcher predicts
January 5, 2018, University of Toronto
https://phys.org/news/2018-01-ai-friends.html
"From self-driving cars to finding disease cures, artificial intelligence, or AI, has rapidly emerged as a potentially revolutionary technology – and the pace of innovation is only set to speed up.
To get a sense of where the field is headed in 2018, U of T News sat down with the University of Toronto's Richard Zemel, a professor of computer science and the research director at the Vector Institute for artificial intelligence research.
He was just back from the annual Neural Information and Processing Systems (NIPS) conference in Long Beach, Calif. – a once staid academic gathering that's more than tripled in size over the past five years, drawing dozens of giant corporations.
Zemel's take? Get ready for a world where businesses enjoy unprecedented insight into their products and services, Toronto continues its ascent as a major AI research hub, and digital assistants like Apple's Siri and Amazon's Alexa become ultra-personalized.
"It will be like being friends with someone for many years," predicts Zemel, who spoke on the sidelines of an event at the Creative Destruction Lab (CDL), one of U of T's numerous entrepreneurship hubs. "The computer or phone may know more about you, potentially, than anyone else."
What can we expect to happen in AI over the next 12 months?
One of the things that will affect people in their daily lives will be personalization. People know about personalized assistants like Alexa and Siri, but those are just the first generation. They're going to get a lot better in the next six to 12 months. They will be able to really understand what you're asking and be able to formulate answers and get to know you better – not just look things up in your calendar or on the web.
Is that a function of improved speech recognition capabilities?
Speech will be part of it. But there's all this other information about you that's available. It's your daily habits, what you do and where you go. So, if people allow it – if you give it access to your emails and photos, what you look at online, watch on TV and the books you read – it's going to be a much bigger package. It will be like being friends with someone for many years. The computer or phone may know more about you, potentially, than anyone else. So it's a question of combining all that information and getting a real profile of your tastes.
Beyond personal assistants, there's a lot of other industries that are potentially going to be impacted by this technology, if they aren't already. What other sectors do you think we may be hearing about?
Education is one example. There will be more systems that learn how you learn best. These could be online learning tools that are custom tailored to you. There will also be lots of manufacturing applications. Here at CDL, there's a lot of companies who are using sensing technologies to find out what's happening in the environment – ranging from smart cities down to a company I was just chatting with that's putting sensors in cows' milk to determine how healthy it is. All of these things that were typically very expensive to assess can now be done with a few sensors and a lot of training data.
Medicine is another area where you have a ton of data, although there's a lot of privacy and data-sharing issues in that. The big companies like Microsoft and Google have talked for years about getting into it, but they've always stopped because of privacy issues. But I think there's enough momentum now that there will be progress in health, ranging from health records to medical imaging diagnoses and robotics in surgery.
On the research side, which areas do you find exciting right now?
One of the most exciting areas – and it's reflected in the research I'm doing – is called transfer learning. That's the idea of performing a new task without a lot of training data. This has a lot of applications in business. Let's say a robot has to climb hills and take out the garbage, and it has a lot of data it's trained on to do that. But now you give it a new task – moving a bin from one place to another, and it's never done that before. So now it has to transfer its knowledge to this new task. The novel thing here is you're training it with a huge amount of data, but you're testing it on something else.
I'm guessing that's more difficult to accomplish than it sounds.
Exactly. That's the interesting piece in all of this. The things that seem easy to us, because people do them naturally, are typically the biggest challenge for these systems. That's true for perception and speech. I mean, we speak pretty easily, but it's taken computers a long time to figure out speech and produce speech. All these things we take for granted are big challenges.
Any other areas of research interest?
Another one goes back to what we were saying about personalization. If it takes off, you will need to allow the [AI] system to see all your personal details. So it becomes a question of whether you're going to be hesitant to release your personal details because of privacy and fairness issues. So there's a lot of research now – a really growing field – called fairness in machine learning. I do lot of research in this. In past years, there were just two or three papers at NIPS. This year there were 20.
Is this a technical issue or an ethical one?
It's both. It's an ethical and societal issue to define what it means to be fair, but the technical issue is how do you build a machine learning system that embodies those principles? It's a very interesting area. The challenge is defining it in a good way, and then you take this definition and formalize it into a mathematical statement we can use to train up the machine learning.
There's also a field called FAT ML, which is fairness, accountability and transparency in machine learning. That's going beyond fairness and privacy to ask whether you can get explanations from the system, so it can be useful for doctors and lawyers. In those high-risk situations, you need systems that are more interpretable. That's an increasingly important direction, too.
[U of T University Professor Emeritus] Geoffrey Hinton made a statement last year about how the current paradigm for deep learning needs to be thrown out the window so we can start over. What do you make of his comments and what should the rest of us make of them?
What he's talking about actually follows a trend in machine learning. And that's to build more structure into the system. Now, a lot of people don't like that point of view. They think of it as a plain vanilla system and you should allow it to learn everything. But he's saying you actually need to build in some structure – capsule networks – where you learn about parts of objects and some parameters associated with them and how they're related. There's a lot of work in that area. There's always been this debate between a sort of tabula rasa view of learning to something where you start with some structure and learn on top of it. So throwing out current deep learning might mean, in my view, that you want to incorporate some sort of structure and the key question is: What's the right structure?
AI has been a big story for U of T and Toronto, particularly with the creation of the Vector Institute last year. Can you give me an update on what will be happening at Vector in 2018?
It's exciting. We finally have the space to move into with desks, chairs and everything. People were worried we wouldn't have enough critical mass to get going. But, actually, when we put it all together, there will be 90 people moving in, including students, post-docs and faculty. And that's just the full-time people. There will be a lot affiliates coming in part-time from other parts of Toronto and the province, including the Universities of Guelph, Waterloo and McMaster. It's going to be a real hub of AI activity. We'll do some growing as well, hiring some additional research scientists and bringing in a new batch of grad students in September. We're hiring some post-docs, as well as software and research engineers.
How will the relationship between Vector and U of T work?
The idea is people will be working at U of T in their faculties, but are also cross-appointed to Vector, so they will move back and forth. Many will also have students whose main desks will be at Vector, but they will also be teaching and holding talks on campus. The way I think about it is as an additional facility with a lot of good researchers who will facilitate collaboration.
In a broader sense, how do you see the concept of an AI hub developing in Toronto?
There's a lot of AI around. A lot of the hospitals are doing AI and health is an application that we're very interested in at Vector. We're going to try and co-ordinate things to get the hospitals working and talking with each other and sharing data. We could also play an important role working with businesses when it comes to finding talent. One of our main aims is graduating master's and Ph.D. students. We're not going to co-ordinate all AI, but we can be an important resource and hub for research.
What do you think are the misconceptions rattling around out there about AI?
One thing people don't realize is that machine learning systems, the way they are right now, require a lot of data and a lot of labelled data – thousands of examples with annotations. If you don't have that then you're in the research field, not the applications field. People need to know that. You need a lot of data.
Is it difficult to get access to sufficient data in a less populous country like Canada?
Not really. There's data everywhere. It's just a question of harnessing it and figuring out how to get labels. We're big players in this. People are trying to emulate what's going on here. I had meetings with top people all over Europe, asking "How did you do Vector? We want to copy you." I think we're sitting on a model for the rest of the world.
Explore further: New AI method keeps data private
Provided by: University of Toronto![[IMG]](images/storyImages/img-dot.gif)
51 Artificial Intelligence (AI) Predictions For 2018
https://www.forbes.com/sites/gilpress/2017/11/28/51-artificial-intelligence-ai-predictions-for-2018/
"It is somewhat safe to predict that AI will continue to be at the top of the hype cycle in 2018. But the following 51 predictions also envision it becoming more practical and useful, automating some jobs and augmenting many others, combining machine learning and big data for fresh insights, with chatbots proliferating in the enterprise.
As the automotive industry undergoes massive disruption, incumbent OEMs and Tier 1s are becoming increasingly aware that they need to adopt AI immediately to address not only the external vehicle environment but to understand the in-cabin experience as well. Semi-autonomous and fully autonomous vehicles will require an AI-based computer vision solution to ensure safe driving, seamless handoffs to a human driver, and an enriched travel experience based on the emotional, cognitive and wellness of the occupants—Dr. Rana el Kaliouby, CEO and co-founder, Affectiva
In 2018, I expect we'll see a number of firsts, including AI systems which can explain themselves directly ('first person') instead of being externally assessed ('third person'); the erosion of net neutrality due to increasingly personalized and optimized AI-driven content delivery; and the burst of the deep learning bubble. AI startups who have simply applied AI in a particular domain will no longer receive over-inflated valuations. Those that survive will be offering a fundamental and demonstrable step forward in AI capability. We'll also see at least one more fatal accident involving autonomous vehicles on the roads, and a realization that human-level autonomous driving will require much longer to test and mature than current optimistic predictions—Monty Barlow, director of machine learning, Cambridge Consultants
AI will begin answering the question “Why?” Two things we’ve learned watching early adopters interact with AI systems over the last couple of years are: 1) Humans are not good at not knowing what an AI is doing, and 2) AI is not good at telling humans what it’s doing. This leaves users frustrated wondering “Why?” in the face of AI’s only current explanation: “Because I said so.” In 2018, it will no longer be enough for AI creators to shrug off users’ desire for more transparency by blaming the lack of communication on the fact that the machine is processing thousands of variables per second. In order to gain users’ trust that an AI system is working in pursuit of a shared goal, AI developers will begin prioritizing advanced forms of accountability, reporting and system queries that allow users to ask, “Why?”, in response to very specific actions—Or Shani, CEO, Albert
Personalized Dynamic Pricing. We predict a move of major ecommerce sites (mostly fashion, electronics, food and drugstore) to real time pricing personalization. Online and brick and mortar pricing will be based on behavior, supply and demand and competitive pricing. Unlike today’s dynamic pricing which changes according to variables specifically not customer-related, personalized pricing will reflect a unique offer received per shopper. Prices will change frequently to reflect a personal offer. The online experience will be emulated offline with all store supply tagged with electronic pricing. There will be a price on the page or on the shelf, and then there will be “your” price—the unique offer you receive—Dan Baruchi, CEO, Personali
In the near future, we’ll be looking at an AI vs. AI combat zone for the first time. Warfare, in general, will move from state actors, hackers and humans engaging in the process, to AI. AI will be directed to attack foreign states and corporations at a veracity that humans cannot defend, so now's the time to discuss purpose-driven AI for good and regulations that should be put in place—Chad Steelberg, CEO and Chairman, Veritone
If you are a software company and are not thinking about adding some type of intelligent AI layer on your product or service, then you will be lagging behind others who will. AI is like water or the air around us -- it's not a category, but it's everywhere and will be embedded in most software we use whether we know it or not—Ed Sim, founder, Boldstart Ventures
2018 is the year that AI becomes packaged and provided to the rest of us in ways that do not require a computer science degree. The goal isn't to create The Singularity – it's to make sound judgement scalable. Observe patterns. Learn from patterns. Apply and test guesses. Draw inferences. It's brute force-based, but it happens so fast you and I don't care – and it happens across a larger set of data than you and I could otherwise touch. But none of that matters if it isn't provided to other software – and users – in a format that can be used and can yield results. We're finally seeing APIs and client apps emerge that show we've hit that milestone—Mike Fitzmaurice, VP of Workflow Technology, Nintex
Silicon Valley won’t be the only place innovation in this area happens. Several countries around the world are placing big bets on AI. It will truly be the technological battleground of the future. If a company is going to commit to AI being a part of their future business plans, they better commit to a long-term development plan that might include several periods of rebuilding and disruption. AI is going to go through several periods of both slow and rapid change—Todd Thibodeaux, CEO and President, Computing Technology Industry Association ( CompTIA)
AI is not about to leave the hype-cycle anytime soon. Current advanced analytics solutions will continue to transform into AI solutions using machine learning and deep learning. Looking to 2018, expect companies to invest in self-driving car research and implement features for assisted driving in new car models. An example of this is computer vision that enables a car to take control if the driver shows signs of fatigue. We predict that companies traditionally using statistical models for advanced analytics solutions – for example, to improve forecasting – will invest in machine learning based adaptive solutions, extracting data from internal and external sources to improve their models—Naresh Koka, VP, SPR
It’s now possible to blend AI with real-time transactional data flowing through a single platform. That opens a world of new possibilities. As an example, AI can help companies capitalize on perishable opportunities when data flows on a single platform, such as optimizing the cost per unit when sourcing a wide variety of commodities such as energy. On a platform with cost-per-unit information for energy from wind, solar and grid sources, AI can enable a company to adjust actions as costs fluctuate in real time, taking advantage of price changes to minimize energy expenses. That’s just one use case — AI and real-time transactional data can enable organizations to take advantage of other short-term opportunities as they arise—Bob Renner, CEO, Liaison Technologies
In 2018, AI technologies that are implemented in the enterprise will be human-centric and result in measurable business outcomes. These technologies will augment human intelligence to make us better versions of ourselves. AI that augments humans will be more widely accepted as it enhances skills and has a positive impact on society, as opposed to perpetuating fears of the human vs. machine—Joshua Feast, CEO and Co-Founder, Cogito Corp
We expect to see continued investments in AI by VCs and from technology and non-technology sectors. It’s the next step in our evolution to unleash and utilize full potential of data – whether sitting within an organization or connecting to external industry sources and macro-economic trends or data coming from sensors and devices. We expect insights from such data will be automated 70-80% of the time through training and learning. But it will require the right human skills and feedback loop aligned with technology advancements. Human expertise will continue to be required in this journey and we will see more focus shifted to strategic decision making—Subrata Chakrabarti, VP of Product Marketing and Strategy, Anaplan
In 2018, the implementation of “smart automation” will deliver the most immediate results to organizations. So many businesses still rely on decades-old, legacy-driven manual processes which create bottlenecks in the digital world of commerce. Automation technology has advanced to the point where these manual tasks—predominately in the back office and shared services centers—can be effectively taken out of human hands. Importantly, we're now at a stage where business users themselves can manage this process, rather than needing full-time IT attention. This means we're going to see CIOs have a say in more and more aspects of the business, as they build out enterprise-wide automation strategies. These strategies offer immediate value to companies, and will lay the foundation for long-term AI success—Dennis Walsh, President, Americas and APAC, Redwood Software
In 2018, we will see Artificial Intelligence (AI) technologies allow Business Intelligence (BI) to advance by many orders of magnitude—bringing about not just linear-paced sustainable innovation, but true exponentially disruptive innovation that we only see once every few decades. In today’s BI and Analytics space, it may take unnecessarily long—and millions of dollars—to query truly large complex data sets, measured in the many terabytes and petabytes of volume. With advancements in AI for BI, businesses in 2018 will be able to query very big data in milliseconds, enabling them to learn much more at much faster speeds, ushering in not only a transition toward greater Business Intelligence, but toward true business cognition—meaning that AI will finally be able to ‘understand’ business data in lieu of simply reporting on it—Guy Levy-Yurista, Head of Product, Sisense
When it comes to artificial intelligence in 2018, companies will begin to hire individuals who can properly analyze algorithms. We will call these people ‘algorithm whisperers.’ Next year, chatbots will be assisting everyone – from being incorporated into mobile phones, to the brick and mortar shopping experience. In the future, all products, services, and business processes will be self-improving—Timo Elliott, Innovation Evangelist, SAP
Advancements in analytics and AI will play a major role in healthcare this coming year. Not just within patient population tools, but also in optimizing workflows both within in-patient and out-patient scenarios. The age of EHR deployments are now pushing organizations to revise, enhance, and develop new processes within the care continuum. Essentially changing the way they work, treat patients, and receive care within the healthcare environment—Christian Boucher, Healthcare Evangelist , Citrix
AI will accelerate the extinction of simple order-taking sales. It will enhance consultative sellers’ ability to win more customers by effectively articulating business value. AI-powered sales learning tools will suggest actions, micro-training, and just-in-time content for reps—based on assessment of the customer's needs, the rep's skills and experience, and the competitive dynamic during sales, like the way Netflix recommends movies—Yuchun Lee, CEO and Co-founder, Allego
General purpose AI is still decades away. However, narrow AI applications will make a big splash in enterprise support functions in 2018, as call center, finance and IT executives begin to move conversational AI, image recognition and autonomic applications from pilot mode into production. These applications will complement existing robotic process automation implementations, turbo-charging employee productivity and operational speed to levels far beyond traditional industry benchmarks—Stanton Jones, Director and Principal Analyst, ISG
2018 will be the year that exposes where AI works and where it fails in healthcare. Applied to huge de-identified data sets, AI is already generating insights useful in population-based work such as accountable care and drug discovery. But AI fails badly when "resolving" to the individual care plan, mostly because the full set of data needed to treat a single human being is still too vast, complex, and mysterious for today's computers and algorithms to "automate"—Frank Ingari, CEO, Growth Ally
I expect 2018 to be an even more exciting year for businesses on their journey towards the intelligent enterprise. More and more companies will grow out of proof of concepts and will effectively start to apply AI throughout the business. Thanks to mature machine learning algorithms, disruptive business models will emerge. They will force whole industries to realize that digital transformation is not just trend, but essential to remain competitive. Meanwhile, deep learning is established as the standard machine learning commodity, but will now strive for more efficiency and scalability within the systems. Finally, we can await further breakthroughs in reinforcement learning and will see academia further adjust to industrial research to ensure their competitiveness—Markus Noga, Head of Machine Learning, SAP
AI will drive up demand for data quality. Organizations are increasingly taking humans out-of-the-loop and empowering AIs to actually make decisions—about how to price flights, stock shelves, or even triage ER patients. At the same time, researchers are finding that “black-box” deep learning algorithms—which once trained can’t be tweaked or even really understood by humans—are the most effective for many problems. Since these algorithms are “garbage in, garbage out” and since the results of garbage-output are becoming ever more consequential, high quality training data will become a coveted resource, like oil for the information age. The sharpest human minds in tech may even shift their attention from creating algorithms to feeding those algorithms the best data diet—Aaron Kalb, co-founder and head of product, Alation
Advances in AI lead to specialized tools in the cloud. As companies look to innovate and improve with machine learning and artificial intelligence, more specialized tooling and infrastructure will be adopted in the cloud to support specific use cases, like solutions for merging multi-modal sensory inputs for human interaction with robots (think sound, touch, and vision) or solutions for merging satellite imagery with financial data to catapult algorithmic trading capabilities. We expect to see an explosion in cloud-based solutions that accelerate the current pace of data collection and further demonstrate the need for frictionless, on-demand compute and storage from managed cloud providers—Horia Margarit, principal data scientist, Qubole
This year, companies tapped AI and machine learning to transform the customer experience, making stories from sci-fi a reality by having robots in stores and using VR to let shoppers test-drive cars, design houses, and more. 2018 will be a time for these organizations to apply the lessons learned from working with AI in customer-facing capabilities to back-end processes by using the technologies to streamline and automate. They can also explore the power of coupling AI and machine learning with other tools, such as IoT, AR, and others, to further enhance front- and back-end functions—Moritz Zimmerman, CTO, SAP Hybris
In the same way that decades ago made it possible for any business to provide key information about the business 24 hours a day, we'll see bots start making it possible for businesses to provide answers to the most common questions their customers have. Natural language processing and machine learning will be increasingly accessible to even small and medium organizations—Dharmesh Shah, co-founder and CTO, HubSpot
2018 will be the year of blended AI—even though interest in AI and chatbots is increasing, human interaction will never go away. While some brands have goals of decreasing their call volume by 50%, real-time interaction will increase (as evident by the Forrester prediction that more brands will phase out email in favor of real-time customer engagement communications). Human interaction will be at the epicenter driving AI in customer service, and is imperative for it to be successful and not see satisfaction levels drop. We still need cognitive thinking that can tweak the algorithms and step in to help customers when needed – brands shouldn’t leave everything to a bot—Dan Kiely, CEO, Voxpro
Data is the foundation of digital transformation initiatives and we are sure to see more major brands across both business and consumer industries leveraging AI-driven metadata in the coming year. This data about data, unified across the enterprise, will enable organizations to start realizing AI’s full potential. By applying machine learning and AI to metadata across the enterprise, businesses will be able to more quickly and accurately capture unprecedented insights and make intelligent predictions based on data – including things they never thought to consider. This will fuel innovation, create better customer experiences, enhance security of sensitive information, and improve overall business outcomes—Anil Chakravarthy, CEO, Informatica
In 2018, AI technologies that are implemented in the enterprise will be human-centric and result in measurable business outcomes. These technologies will augment human intelligence to make us better versions of ourselves. AI that augments humans will be more widely accepted as it enhances skills and has a positive impact on society, as opposed to perpetuating fears of the human vs. machine—Joshua Feast, CEO and Co-Founder, Cogito Corp
We expect that the AI market, specifically as it relates to chatbots, will continue to grow as advertisers gain a better understanding of how the technology can fit into their customer engagement plans and enhance the shopping experience. Chatbots empower brands to have direct, automated conversations with customers 24/7/365. The key for brands in 2018 will be to deliver relevant messages and engagement through this new medium (chatbots, or voice recognition devices) as part of a holistic consumer-centric approach that includes audience understanding, targeting, delivery and measurement—Pehr Luedtke, SVP of Business Development, Valassis Digital
Mobile ad tech is a data-driven industry and the perfect platform for AI and machine learning to thrive and make an impact. However, AI is yet to be adopted across the industry despite being able to solve pressing problems such as optimizing campaigns to reduce infrastructure costs, and dynamic creative optimization, amongst others. 2018 will bring synergies between AI and ad tech which will evolve with a wider adoption—Abhay Singal, co-founder and Chief Revenue Officer, InMobi
Making smart marketing decisions across all customer touchpoints, using all available data, to drive complex business outcomes is a herculean task—and artificial intelligence is an absolute requirement for making it all work. In 2018, we’ll finally start to see AI deliver on the omnichannel promise to make marketing that consumers—and others in the value chain—love. The technology is there—from players like IBM Watson and others—and now is the time to rally the right processes and people to put it in action—Dan Rosenberg, Chief Strategy Officer, MediaMath
AI will play an enhanced, proactive role in enabling exceptional customer experience in 2018, where fast and accurate resolution is key. Today, AI in the self-service space (e.g. chatbots) helps provide fast service for transactional or traditionally self-service resolved issues. 2018 will bring AI’s proactive, context-inclusive handoff of chat sessions to live agents, enabling quicker and more complete resolution of more complex customer needs—Chris Bauserman, VP Segment & Product Marketing, NICE inContact
AI will have a pivotal role in communication and collaboration. With new modes of communication attempting to stake a claim in the workforce, expect to see a significant change in the traditional work dynamic. Artificial intelligence, for example, is going to transform and customize the way people communicate with pattern and location recognition to streamline meetings and calls for each individual employee. Further, employees will shift from communicating through their devices to having their devices communicate for them—Mark Sher, vice president of product marketing for cloud voice, Intermedia
AI and data will take the gossip out of the real estate industry. Just as technology changed financial markets, we will see AI revolutionize the largest data asset class in the world, real estate. No more subjective opinions or guesswork; decisions will be based on AI working with massive data sets to create a truly competitive industry. Regardless of whether we are talking about home price trends for a single block or an entire nation, AI will remove the constant gossip that attempts to inform the buying and selling decisions consumers, investors and mortgage lenders make—Jeremy Sicklick, CEO and Co-Founder, HouseCanary
Rather than being seen as a cure for everything, AI will get more practical in 2018. In fact, it will actually become less visible in many cases, as people focus more on the new ways that AI, when tightly integrated into everyday applications, solves specific business problems. Instead of just a concept, they will find that AI enables them to be more productive and able to discover new insights that they could not before. In addition, technology industry leaders and even AI customers will increasingly find it valuable to integrate disparate AI technologies—and discover that when integrated, these different AI technologies deliver more than the sum of their parts—Peter Wallqvist, VP of Strategy, iManage
In 2018, I expect AI techniques to be applied to solve more of the complex engineering problems organizations face in design, testing, and certification of engineering products. By utilizing knowledge management platforms to amplify and augment human decision making, AI can take historical data to make sense of problems that otherwise may not have been solved with traditional engineering—Mohit Joshi, president and head of banking, financial services and insurance, and health care and life sciences, Infosys
AI will become more accessible to non-experts. Early forms of business AI are demonstrating how they can help organizations scale and maximize efficiency, but before they reach mainstream adoption, they’re going to have to achieve mainstream usability. In 2018, we'll begin seeing two trends: AI interfaces will become so accessible that non-technical users across organizations and roles will be able to operate them. Additionally, more and more developers will begin learning how to program AI systems, making AI less obscure and rarefied and more a part of the standard developer toolbox—Tomer Naveh, CTO, Albert
Data analysts begin to reap the benefits of AI. While “data analyst” seems like a job ripe for automation (isn’t that what computers do well?), the advancements in AI will lead to efficient assistants rather than replacements. We’re getting closer to a place where data analysts leverage AI for pattern matching and conducting closed environment analysis. Soon, the job of analysts will be to point the AI to the right questions to be analyzed and to decide how to interpret the results in the real world—David Crawford, director of software engineering at Alation
AI will help companies step out of the middleman role between offline and online. In 2018, online-first companies will be forced to make their way into the physical realm to connect with consumers. Technologies that enhance brands’ ability to connect once they’re offline—and integrate online audiences into the experience—will have significant impact. What currently stands in the way of many in-person experiences’ ability to extend online is companies’ need to control the associated social media content for brand “appropriateness.” AI will step in to take over by moderating and retargeting content in real-time, based on image analysis of image-based content, as well as its fit with predefined brand guidelines—Matthew Haber, Managing Director & Co-Founder, BeSide Digital
Enterprises will move from AI science experiments to truly operationalizing it. As enterprises move forward with operationalizing AI, they will look for products and tools to automate, manage and streamline the entire machine learning and deep learning life cycle. Data scientists need to focus on the code and algorithms and not automating and operationalizing the process. In 2018, investments in AI life cycle management will increase and technologies that house the data and supervise the process will mature—Nima Negahban, CTO and cofounder, Kinetica
The AI debate shifts from ‘is it good or evil’ to ‘is it ever going to be good enough’. If 2017 was the year where the warnings from Elon Musk and Stephen Hawking about the potential evil from AI clashed with predictions from Mark Zuckerberg and Bill Gates on its potential good, 2018 will be the year when the debate shifts to its practical utility. Much like other technologies that were lauded for their world-changing potential and then fizzled as the fog of the hype cleared, early adopters will find themselves disappointed by AI’s obvious limits. The broader public—familiar with Alexa, Siri and Google Home—will be similarly disillusioned as the experts acknowledge that there is only so much that AI will be able to do, and for really complex problems, a new paradigm will be needed—Michel Morvan, co-founder and CEO, Cosmo Tech USA
Developers will confront the question of open sourcing their AI/ML data sets. It is no secret that companies like Facebook, Google and Amazon currently have a monopoly on our data. In 2018, developers will need to make a decision: band together and open-source their AI/ML data sets in the hope of standing up to these monopolies, or give in and resign to a future where Mark Zuckerberg and Sundar Pichai remain the keeper of the keys to AI innovation. One technology that will make these developer-led, open-source initiatives possible is homomorphic encryption. Through homomorphic encryption, AI/ML models can be developed and verified on a blockchain before being shared, in turn liberating them from today's limited and highly-centralized data sets. This approach paves the way to a more democratic and collaborative AI future while at the same time skirting any concerns with privacy and proprietary data—Matt Creager, Vice President of Growth and Developer Relations, Manifold
The rising AI tide lifts all technology boats, and, in cybersecurity, it means we can now make better predictions than ever before about never before seen threats. That, combined with great enhancements to automation, means that defenders may gain an upper hand on attackers. However, all technology, including AI, is dual-use, meaning it has the potential to be used for both good and bad purposes, depending on the intentions and actions of those who wield it. While the risk of the great robot uprising is somewhere between science-fiction and very far off, there are more immediate risks to AI, from the subversion of defensive AI by attackers, to AI-driven cyberweapons or campaigns. As defenders, our excitement about the benefits of AI are balanced with our preparations for such harmful exploitations—Joe Levy, CTO, Sophos
As AI-based cybersecurity technologies, including user behavior analytics, become mature enough for enterprise deployment, we predict that we'll hear more success stories about how AI prevented a complex cybersecurity attack, with more companies allocating a direct budget for similar technologies in 2018 and beyond. We also think that vendors will extend their AI-related cyber security portfolio to support incident management. On the R&D side, we expect promising AI-based security announcements for securing IoT and smart cities—Csaba Krasznay, security evangelist, Balabit
In efforts to keep up with consumers’ desire for innovation, companies are rebuilding their legacy apps on the cloud. But, these rapid changes have given rise to complex IT ecosystems, which make it difficult to monitor digital performance and manage the user-experience effectively. That’s why, in 2018, organizations will look to AI to automate all the heavy lifting and proactively identify problems so that they can pinpoint the underlying root cause of any issues before their customers are impacted—Alois Reitbauer, chief technology strategist, Dynatrace
As enterprises ramp up their AI operations and build their own centers of excellence, we’re going to see a war for talent across a range of disciplines. Data scientists and cognitive programmers, linguists, psychologists, script writers and UX experts, are going to be in more demand than ever before—Chetan Dube, CEO, IPSoft
In 2018, AI will help companies scale and will take on a higher percentage of work. In 2018, business leaders will push to make the business run more efficiently and will turn to AI and machine learning for help. Companies will also turn to AI to help scale and do jobs instead of adding headcount. We will see AI developments and research move from the scientific/abstract concept phase to a more practical phase—Derek Choy, CIO, Rainforest QA
Artificial intelligence will dramatically change the accounting profession and recast the skillset required to succeed. We will see the rise of accountants as strategic advisors to small businesses as AI increasingly powers prescriptive financial decision making by sifting through vast amounts of financial data and empowering individuals to make recommendations about the best course of action. This will fundamentally alter the role of today's accountant as focus shifts from tedious data entry, to using data-driven insights uncovered by machine learning to help small business clients make better business decisions—Herman Man, VP of product and partnerships, Xero
Alexa will start to use her eyes. With the release of the Echo Show and other forthcoming devices, Amazon’s extensive investment in vision-based product recognition efforts through its Lab 126 is coming to fruition. It follows that Alexa will start to use the camera to disambiguate between possible purchases, particularly for grocery. This will be important in on-boarding new shoppers where the machine learning algorithms don’t have the shoppers’ history modeled or when ordering a new item. In addition, devices like Show will allow Amazon to better leverage its strength in analytics to cross-sell the customer in the same fashion as the website, by showing the ubiquitous: “Customers who bought this item also bought…” options, providing Amazon more control on margins and power in the market—Tony Rodriguez, CTO, Digimarc Corporation
As we saw briefly this year with WannaCry, AI and machine learning technologies are diversifying hackers’ arsenals so that they can create more sophisticated attacks – and this will only become a more common trend in 2018 as adversaries look to bypass basic protection methods like two-factor authentication (2FA). Next year, it’s time for organizations to start thinking beyond the basic layer of 2FA and start considering what’s next for safeguarding our systems and social platforms. Enterprises must begin adopting automated security tools on a broader scale to analyze their digital presence for threats and suspicious behavior, which will in-turn spur an interesting AI vs. AI dogfight—Phil Tully, principal data scientist, and Zack Allen, manager of threat operations, ZeroFOX
In today’s noisy digital environment, personalization in marketing will continue to be a priority and major theme. I expect that we’ll see AI and machine learning technologies play a larger role in making marketing messages and online shopping experiences more customized for each individual consumer. With the amount of customer data available, targeting the right people with a relevant message, offer or promotion in a timely manner is quite difficult, but machine learning and AI techniques are helping marketers correlate and synthesize signals from different sources, identify behavioral patterns and infer the strength of interest or purchase intent more efficiently than before. This will help companies get closer to the desired 1-to-1 marketing that is so hard to achieve and scale—Bryan Chagoly, VP of Technology, Bazaarvoice
AI won’t replace jobs in 2018 – it will augment them. A common misconception is that the rocket-like adoption of AI applications will replace human jobs in 2018. In cyber security, however, we can expect the opposite. AI will act as a force multiplier, helping humans allocate resources effectively and spend their time on the most important priorities. With over one million cyber security positions unmanned in 2016, there is an important gap to be filled. 2017 showed us that security teams simply can’t keep – in 2018, organizations are taking back the time advantage by using AI to autonomously respond and mitigate threats in real time, before they do damage—Justin Fier, Director for Cyber Intelligence and Analysis, Darktrace
[Bonus Update] In 2018, we will see greater specialization of AI-powered technology in response to demand for more personalized decision-making and admin support that can augment individual work flows. It’s the little decisions that support our best work – organizing calendars, tracking to-do lists, scheduling meetings – that we will derive true value from the intersection of AI and enterprise communications. AI-powered tools will learn preferences and behaviors, and subsequently bend toward individual work flows, creating more specialized experiences and improving productivity for all--Keith Johnson, CTO, Fuze
[Bonus Update] The electricity industry remains one of the most automated and least digitalized industries. Operations and maintenance budgets are not only high, but dominate cost profiles and asset value for the grid and power plants, even wind farms and large-scale solar. In 2018, we predict AI will rapidly automate maintenance of industrial processes, from real-time fuel optimization (which significantly impacts greenhouse gas emissions from coal and gas power generation) to wind power forecasting (whereby AI optimizes turbine blade positions to increase power yield). At the same time, we foresee a blended human/AI approach to digitalizing operations that frees up human capital to focus on high priority issues. Electricity has a mature workforce with 25% of industry workers reaching retirement age within five years, so power producers and utilities will need to invest substantially in creating a culture where operators are comfortable working in AI-enabled environments--Peter Kirk, Executive, Business Operations, GE Power Digital
Follow me on Twitter @GilPress or Facebook or Google+Last edited: Jan 6, 2018 -
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Nvidia Thread
Discussion in 'Hardware Components and Aftermarket Upgrades' started by Dr. AMK, Jul 4, 2017.
