C3.ai, Microsoft and Adobe have announced the launch of C3 AI CRM powered by Microsoft Dynamics 365.

The first enterprise-class, AI-first customer relationship management solution is purpose-built for industries, integrates with Adobe Experience Cloud, and drives customer-facing operations with predictive business insights.

The partners have agreed to integrate Microsoft Dynamics 365, Adobe Experience Cloud (including Adobe Experience Platform), and C3.ai's industry-specific data models, connectors, and AI models, in a joint go-to-market offering designed to provide an integrated suite of industry-specific AI-enabled CRM solutions including marketing, sales, and customer service.

The partnership will sell the industry-specific AI CRM offering through dedicated sales teams to target enterprise accounts across multiple industries globally, as well as through agents and industry partners.

They will target industry vertical markets initially including financial services, oil and gas, utilities, manufacturing, telecommunications, public sector, healthcare, defense, intelligence, automotive, and aerospace.

Additionally, the partnership will market the jointly branded offering globally, supported by the companies' commitment to customer success.

"Microsoft, Adobe, and C3.ai are reinventing a market that Siebel Systems invented more than 25 years ago," says Thomas M. Siebel, CEO of C3.ai.

"The dynamics of the market and the mandates of digital transformation have dramatically changed CRM market requirements. A general-purpose CRM system of record is no longer sufficient," he says.

"Customers today demand industry-specific, fully AI-enabled solutions that provide AI-enabled revenue forecasting, product forecasting, customer churn, next-best product, next-best offer, and predisposition to buy."

Satya Nadella, CEO at Microsoft, says, "This year has made clear that businesses fortified by digital technology are more resilient and more capable of transforming when faced with sweeping changes like those we are experiencing.

"Together with C3.ai and Adobe, we are bringing to market a new class of industry-specific AI solutions, powered by Dynamics 365, to help organisations digitise their operations and unlock real-time insights across their business," he says.

Shantanu Narayen, president and CEO of Adobe, adds, "We are proud to partner with C3.ai and Microsoft to advance the imperative for digital customer engagement.

"The unique combination of Adobe Experience Cloud, the industry-leading solution for customer experiences, together with the C3 AI Suite and Microsoft Dynamics 365, will enable brands to deliver rich experiences that drive business growth," Narayen says.

Combining Microsoft Dynamics 365 CRM software with Adobe's suite of customer experience management solutions alongside C3.ai's enterprise AI capabilities, C3 AI CRM is the world's first AI-driven, industry-specific CRM built with a modern AI-first architecture.

C3 AI CRM integrates and unifies vast amounts of structured and unstructured data from enterprise and extraprise sources into a unified, federated image to drive real-time predictive insights across the entire revenue supply chain, from contact to cash. With embedded AI-driven, industry-specific workflows, C3 AI CRM helps teams:

C3 AI CRM enables brands to take advantage of their real-time customer profiles for cross-channel journey orchestration. The joint solution offers an integrated ecosystem that empowers customers to take advantage of leading CRM capabilities along with an integrated ecosystem with Azure, Microsoft 365, and the Microsoft Power Platform.

C3 AI CRM is pre-built and configured for industries financial services, healthcare, telecommunications, oil and gas, manufacturing, utilities, aerospace, automotive, public sector, defense, and intelligence enabling customers to deploy and operate C3 AI CRM and its industry-specific machine learning models quickly.

In addition, C3 AI CRM leverages the common data model of the Open Data Initiative (ODI), making it easier to bring together disparate customer data from across the enterprise.

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Microsoft, Adobe and C3.ai re-invent CRM with artificial intelligence - IT Brief Australia

Artificial intelligence enables computers and machines to mimic the perception, learning, problem-solving, and decision-making capabilities of the human mind.

In computer science, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mindlearning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problemsand combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.

After decades of being relegated to science fiction, today, AI is part of our everyday lives. The surge in AI development is made possible by the sudden availability of large amounts of data and the corresponding development and wide availability of computer systems that can process all that data faster and more accurately than humans can. AI is completing our words as we type them, providing driving directions when we ask, vacuuming our floors, and recommending what we should buy or binge-watch next. And its driving applicationssuch as medical image analysisthat help skilled professionals do important work faster and with greater success.

As common as artificial intelligence is today, understanding AI and AI terminology can be difficult because many of the terms are used interchangeably; and while they are actually interchangeable in some cases, they arent in other cases. Whats the difference between artificial intelligence and machine learning? Between machine learning and deep learning? Between speech recognition and natural language processing? Between weak AI and strong AI? This article will try to help you sort through these and other terms and understand the basics of how AI works.

The easiest way to understand the relationship between artificial intelligence (AI), machine learning, and deep learning is as follows:

Let's take a closer look at machine learning and deep learning, and how they differ.

Machine learning applications (also called machine learning models) are based on a neural network,which is a network of algorithmic calculations that attempts to mimic the perception and thought process of the human brain. At its most basic, a neural network consists of the following:

Machine learning models that arent deep learning models are based on artificial neural networks with just one hidden layer. These models are fed labeled datadata enhanced with tags that identify its features in a way that helps the model identify and understand the data. They are capable of supervised learning (i.e., learning that requires human supervision), such as periodic adjustment of the algorithms in the model.

Deep learning models are based on deep neural networksneural networks with multiple hidden layers, each of which further refines the conclusions of the previous layer. This movement of calculations through the hidden layers to the output layer is called forward propagation. Another process, called backpropagation, identifies errors in calculations, assigns them weights, and pushes them back to previous layers to refine or train the model.

While some deep learning models work with labeled data, many can work with unlabeled dataand lots of it. Deep learning models are also capable of unsupervised learningdetecting features and patterns in data with the barest minimum of human supervision.

A simple illustration of the difference between deep learning and other machine learning is the difference between Apples Siri or Amazons Alexa (which recognize your voice commands without training) and the voice-to-type applications of a decade ago, which required users to train the program (and label the data) by speaking scores of words to the system before use. But deep learning models power far more sophisticated applications, including image recognition systems that can identify everyday objects more quickly and accurately than humans.

For a deeper dive into the nuanced differences between thesetechnologies, read AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: Whats the Difference?

Weak AIalso called Narrow AI or Artificial Narrow Intelligence (ANI)is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. Narrow is a more accurate descriptor for this AI, because it is anything but weak; it enables some very impressive applications, including Apple's Siri and Amazon's Alexa, the IBM Watson computer that vanquished human competitors on Jeopardy, and self-driving cars.

Strong AI, also called Artificial General Intelligence (AGI), is AI that more fully replicates the autonomy of the human brainAI that can solve many types or classes of problems and even choose the problems it wants to solve without human intervention. Strong AI is still entirely theoretical, with no practical examples in use today. But that doesn't mean AI researchers aren't also exploring (warily) artificial super intelligence (ASI), which is artificial intelligence superior to human intelligence or ability. An example of ASI might be HAL, the superhuman (and eventually rogue) computer assistant in 2001: A Space Odyssey.

As noted earlier, artificial intelligence is everywhere today, but some of it has been around for longer than you think. Here are just a few of the most common examples:

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:

IBM Watson products and solutions give enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore IBM's portfolio of managed services and solutions.

Sign up for an IBMid and create your IBM Cloud account.

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What is Artificial Intelligence (AI)? | IBM

Artificial intelligence(AI) capabilities, frommachine learninganddeep learningtonatural languageprocessing (NLP) and computer vision, are rapidly advancing. Technology has never moved at such pace, meaning the role of the CIO is harder than ever to stay current and up to date with technology overall, so understanding the vast array of AI capabilities is a stretch for most CIOs right now, says Wayne Butterfield, director of cognitive automation and innovation technology research at advisory firmISG.

Naturally, IT leaders are increasingly exploring AI applications in the enterprise. However, AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches.

AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches.

It is imperative for CIOs to know AI in reasonable depth to understand its realistic and pragmatic adoption, explains Yugal Joshi, vice president of digital, cloud, and application services research forEverest Group. They need to understand what is doable as of today versus 3-5 years from now. Otherwise, there is a risk of them to either overestimate or underestimate AIs impact on business as well as IT.

[ Do you understandthe main types of AI?Read also:5 artificial intelligence (AI) types, defined.]

In addition, the business appetite for AI-driven transformation is at an all-time high, even asAI-washing by technology vendorscontinues to be a very real phenomenon. Its more important than ever that CIOs be able to differentiate between what is real versus what is vendor-driven AI marketing to make the best decisions for their business, Joshi says.

CIOs are increasingly hiring AI-savvy IT pros to further their digital transformation efforts. But those team members are depending on their IT leaders to understand enough about AI to best support and sustain their efforts. To that end, here are nine things CIOs should understand about AI.

In actual fact, its a group of technologies used to solve specific problems, says Butterfield. The catch-all term of Artificial Intelligence is so genericthat it is almost meaningless. In the most simplistic terms, AI is usually geared around providing a data-based answer or providing a data-fueled prediction. Then things begin to diverge.

NLP may be used to automate incoming emails, machine vision to gauge quality on the product line, or advanced analytics to predict a failure of your network. (For more on the various flavors of AI, read5 AI types, defined.) CIOs need to at least understand the strands of AI that are relevant to their business and ensure that they have a basic understanding of the problems that AI can solve for their business, and those it will not, Butterfield says.

"There is certainly a wide variety of people's expectations of AI, from realistic to off-the-wall."

There is certainly a wide variety of peoples expectations of AI, from realistic to off-the-wall, says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems in theCollege of Computing at Michigan Technological Universityand director of theInstitute of Computing and Cybersystems. CIOs should have at least a decent understanding of the limitations of AI such that they can predicate their expectations and properly evaluate AI solutions they are considering.

Machine learning, for example, can produce implicit models of very complex processes from representative data or experience. So an ML algorithm can learn to recognize cats by looking at millions of pictures of cats and not-cats, but it will not learn that cats meow or eat kibble.

The ROI on AI requires more patience than your average IT initiative. An Everest Group survey of more than 200 global IT leaders 84 percent cited long wait to return as a challenge. CIOs need to realize the reasons behind these long waits rather than getting flustered and disappointed with these, Joshi says.

In some cases, there may not be sufficient data governance in place.

CIOs need to understand the amount of data crunching needed to create an intelligent system, says Joshi. Therefore, CIOs need to decide whether the business has data and capability to build or use an AI system.

Havens advises CIOs to always ask where the training data will come from and how an algorithm is evaluated. That gets at whether this algorithm has been proven on real-world data that it hasnt seen before, Havens says.

In some cases, there may not be sufficient data governance in place. Although most organizations claim data is important, few invest as if that is the case. Their other enterprise functions such as HR and Finance have much larger teams than their data practice, says Joshi. CIOs need to understand what skills they need to invest given their spend appetite as some data skills may not be affordable for enterprises.

There is often a debate of where data science or AI Centers of Excellence belong, says Dan Simion, vice president of AI & Analytics with Capgemini North America. Some CIOs believe data scientists should sit within IT, while others may suggest data scientists be embedded within the business. CIOs must ensure that they are not downplaying the role of data scientists, says Simion, noting that when used properly they can do more than descriptive data visualizations but also solve business problems by leveraging AI and machine learning technologies.

CIOs who want to unlock the full potential of their AI programs should realize the knowledge and skills of their data scientists and give them opportunities to maximize the value they can drive, Simion says.

Thus, the operations team becomes extremely critical to the success or failure of intelligent capabilities. In fact, 61 percent of enterprises said their operations team are leaders in the charge of AI adoption in their organization, according to Everest Group research.

The operations team becomes extremely critical to the success or failure of intelligent capabilities.

Though [an increasing number of] enterprises are leveraging cloud-based AI offerings for cloud and SaaS vendors, the operations team is critical to scale such initiatives and create the needed guardrails, Joshi says.

One of the IT leaders most important roles is understanding the technology requirements necessary to support and sustain the companys AI transformations. In order for a company to be successful along its AI journey, Simion says, the CIO needs to make sure the AI technology stack is working and in sync with the overall enterprise technology.

Unlike many historical IT projects, AI initiatives require collaboration across data analytics, infrastructure, applications, data management, and the business. CIOs need to have the vision for creating such pod-based cross-functional teams that are jointly held accountable for the outcome and not for their individual pieces, Joshi says.

Although we throw around the term intelligent, AI is not inherently adaptive. AI algorithms are only good at what they are designed for, and will often fail miserably and in strange ways when applied to problems that may seem similar to humans, but are not similar from an AI-perspective, Havens says. An algorithm that is trained to drive a car in an urban environment may and probably will fail at rural driving, for example.

Is your organization looking to increase efficiency? Improve effectiveness? Transform the customer or user experience? Create entirely new business models? The CIO must understand what value the business wants to derive from AI adoption. Everest Group notes four common business imperatives: Efficiency, Effectiveness, Experience, and Evolution. CIOs may also need to manage inflated expectations of business around AI adoption and its impact on the organization.

[ How can automation free up staff time for innovation? Get the free eBook:Managing IT with Automation. ]

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Artificial Intelligence (AI): 9 things IT pros wish the CIO knew - The Enterprisers Project

Artificial Intelligence (AI) is transforming the world of work at a rapid pace, and its impact is poised to accelerate even more in the decades ahead. The rise of AI in the workplace has the potential to improve some aspects of work such as repetitive and dangerous tasks but much of the public discussion has focused on AIs potential to displace workers and the grave impacts this could pose to their livelihoods and quality of life.

Less discussed but equally important is how AI can also be part of the solution to train, upskill, and reskill employees, future-proofing and preparing them for the future of work.

The World Economic Forum's Future of Jobs Report 2020 found as automation increases, the amount of time spent on tasks at work by humans and machines will be equal in just five years. According to the report, 85 million jobs will be displaced across 26 countries and 15 industries by 2025, and more than 80% of employers expect to make wider use of remote work and to digitize work processes.

In other words, even with no further advances of AI, todays workers will face enormous disruptions as labour markets are transformed by automation tools already on the market.

The challenge ahead will be even greater for two reasons: First, COVID-19 has taken an enormous toll on the global economy, leading to an estimated loss of 14% of working hours or approximately 400 million full-time jobs in the second quarter of 2020.

Second, far from remaining static, automation technologies are being developed and commercialized at an accelerating pace during the pandemic, and their capacities are expanding as they harness growing quantities of data, computing power, and experience.

Despite these significant headwinds, AI holds great potential for workers and the future of labour. The Future of Jobs Report found that 97 new million jobs that are adapted to the new division of labour between humans and machines could be created by 2025.

Instead of worrying about job displacement, we should get to work creating the new, high-quality jobs of tomorrow while massively expanding our upskilling and reskilling efforts to transition displaced workers into these new opportunities. This is where AI can help solve some of the very problems it helped create.

Fortunately, a new cohort of purpose-driven AI innovators has begun mobilizing to ensure that workers around the world share not only in the risks that AI poses but also its powerful benefits.

Here are four ways that AI is helping create a just transition to a positive future of work:

1. Invest in reskilling and upskilling

Some AI start-ups are focused directly on reskilling and upskilling todays workers, using AI algorithms to create personalized training programmes that build on workers existing skillsets to prepare them for future opportunities that leverage technology. For example, California-based EdCast combines a detailed assessment of workers skills with data-driven analysis of future labour market needs, allowing users to identify potential future jobs and gain the skills and qualifications they need to secure them.

2. Embed learning into everyday activities

Other innovators are focused on embedding learning into everyday work activities so that workers are continuously growing their skillsets and expanding their capacity to fulfil future workforce needs. For example, Canadian start-up Axonify has pioneered the idea of microlearning in which employees spend three to five minutes per shift on learning and performance improvement activities that are matched to their individual needs by an AI algorithm.

3. Match workers to new opportunities

While AI-driven reskilling can help prepare workers for the disruptions underway, there is still an enormous need to match workers to new opportunities being created. AI can help here too, and start-ups like SkyHive and Kalido have created AI-driven platforms that connect workers to new opportunities within and outside their current organizations, based on their individual skillsets, career goals, and retraining needs.

4. Prepare the next generation of workers

AI is not only being used to future-proof todays workers and match them with tomorrows opportunities but also to prepare the next generation of workers for a future disrupted and transformed by technology. Recent years have seen an explosion in AI-powered education technology (EdTech) tools to improve learning outcomes at the K-12 level, primarily in the developed world but increasingly amongst less privileged populations.

For example, New Delhi-based start-up ConveGenius has targeted the 100 million students in India with the most limited access to quality teaching, using AI to assess each students learning needs and deliver the educational content they need to succeed as workers and citizens. Scores of other start-ups are racing to address this significant business challenge and seize the opportunity to upskill and reskill workers for the future.

No single technology will solve all the problems facing workers today. Achieving a just transition to a positive future of work will require smart government policies, ambitious corporate programs, and individual action by workers in addition to new technologies.

But as we work to improve our responses to the disruptions underway, we should encourage innovators and entrepreneurs to focus on new ways to use AI technologies to empower the workers of today and create exciting new opportunities for the workers of tomorrow.

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Here's how AI can train workers for the jobs of the future - World Economic Forum

By adding a few pixels (highlighted in red) to a legitimate check, fraudsters can trick artificial intelligence models into mistaking a $401 check for one worth $701. Undetected, the exploit could lead to large-scale financial fraud.

Yaron Singer climbed the tenure track ladder to a full professorship at Harvard in seven years, fueled by his work on adversarial machine learning, a way to fool artificial intelligence models using misleading data. Now, Singers startup, Robust Intelligence, which he formed with a former Ph.D. advisee and two former students, is emerging from stealth to take his research to market.

This year, artificial intelligence is set to account for $50 billion in corporate spending, though companies are still figuring out how to implement the technology into their business processes. Companies are still figuring out, too, how to protect their good AI from bad AI, like an algorithmically generated voice deepfake that can spoof voice authentication systems.

In the early days of the internet, it was designed like everybodys a good actor. Then people started to build firewalls because they discovered that not everybody was, says Bill Coughran, former senior vice president of engineering at Google. Were seeing signs of the same thing happening with these machine learning systems. Where theres money, bad actors tend to come in.

Enter Robust Intelligence, a new startup led by CEO Singer with a platform that the company says is trained to detect more than 100 types of adversarial attacks. Though its founders and most of the team hold a Cambridge pedigree, the startup has established headquarters in San Francisco and announced Wednesday that it had raised $14 million in a seed and Series A round led by Sequoia. Coughran, now a partner at the venture firm, is the lead investor on the fundraise, which also comes with participation from Engineering Capital and Harpoon Ventures.

Robust Intelligence CEO Yaron Singer is taking a leave from Harvard, where he is a professor of computer science and applied mathematics.

Singer followed his Ph.D. in computer science from the University of California at Berkeley, by joining Google as a postdoctoral researcher in 2011. He spent two years working on algorithms and machine-learning models to make the tech giants products run faster, and saw how easily AI could go off the rails with bad data.

Once you start seeing these vulnerabilities, it gets really, really scary, especially if we think about how much we want to use artificial intelligence to automate our decisions, he says.

Fraudsters and other bad actors can exploit the relative inflexibility of artificial intelligence models in processing unfamiliar data. For example, Singer says, a check for $401 can be manipulated by adding a few pixels that are imperceptible to the human eye yet cause the AI model to read the check erroneously as $701. If fraudsters get their hands on checks, they can hack into these apps and start doing this at scale, Singer says. Similar modifications to data inputs can lead to fraudulent financial transactions, as well as spoofed voice or facial recognition.

In 2013, upon taking an assistant professor position at Harvard, Singer decided to focus his research on devising mechanisms to secure AI models. Robust Intelligence comes from nearly a decade in the lab for Singer, during which time he worked with three Harvard pupils who would become his cofounders: Eric Balkanski, a Ph.D. student advised by Singer; Alexander Rilee, a graduate student; and undergraduate Kojin Oshiba, who coauthored academic papers with the professor. Across 25 papers, Singers team broke ground on designing algorithms to detect misleading or fraudulent data, and helped bring the issue to government attention, even receiving an early Darpa grant to conduct its research. Rilee and Oshiba remain involved with the day-to-day activities at Robust, the former on government and go-to-market, and the latter on security, technology and product development.

Robust Intelligence is launching with two products, an AI firewall and a red team offering, in which Robust functions like an adversarial attacker. The firewall works by wrapping around an organizations existing AI model to scan for contaminated data via Robusts algorithms. The other product, called Rime (or Robust Intelligence Machine Engine), performs a stress test on a customers AI model by inputting basic mistakes and deliberately launching adversarial attacks on the model to see how it holds up.

The startup is currently working with about ten customers, says Singer, including a major financial institution and a leading payment processor, though Robust will not name any names due to confidentiality. Launching out of stealth, Singer hopes to gain more customers as well as double the size of the team, which currently stands at 15 employees. Singer, who is on leave from Harvard, is sheepish about his future in academia, but says he is focused on his CEO role in San Francisco at the moment.

For me, Ive climbed the mountain of tenure at Harvard, but now I think weve found an even higher mountain, and that mountain is securing artificial intelligence, he says.

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This Harvard Professor And His Students Have Raised $14 Million To Make AI Too Smart To Be Fooled By Hackers - Forbes

Artificial intelligence is the new oil, and the governments or the countries that get the best datasets will unquestionably develop the best AI, the Joint Artificial Intelligence Center's chief technologyofficer said Oct. 15.

Speaking on a panel about AI superpowers at the Politico AI Summit, Nand Mulchandani said AI is a very large technology and industry. "It's not a single, monolithic technology," he said. "It's a collection of algorithms, technologies, etc., all cobbled together to call AI."

The United States has access to global datasets, and that's why global partnerships are so incredibly important, he said, noting the Defense Department launched the AI partnership for defense at the JAIC recently to have access to global datasets with partners, which gives DOD a natural advantage in building these systems at scale.

"Industry has to develop on its own, and that's where the global talent is; that's where the money is; that's where all of the innovation is going on," Mulchandani noted, adding that the U.S. government's job is to be able to work in the best way and absorb the best technology that it can. That includes working hand in glove with industry on a voluntary basis, he said. He said there are certain areas of AI that are highly scaled that you can trust and deploy at scale.

"But notice many or not many of those systems have been deployed on weapon systems. We actually don't have any of them deployed," he said.

Mulchandani said the reason is that explainability, testing, trust and ethics are all highly connected pieces and even AI security when it comes to model security, data security being able to penetrate and break models. This is all very early, which is why the DOD and the U.S. government widely have taken a very stringent approach to putting together the ethics principles and frameworks within which we're going to operate.

"[Earlier this year, one of the first international visits that we made were to NATO and our European partners, and [we] then pulled them into this AI partnership for defense that I just talked about," he said. "Thirteen different countries are getting together to actually build these principles because we actually do need to build a lot of confidence in this."

He said DOD continues to attract and have the best talent at JAIC. "The real tricky part is: How do we actually take that technology and get it deployed? That's the complexity of integrating AI into existing systems, because one isn't going to throw away the entire investment of legacy systems that one has, whether it be software or hardware or even military hardware," Mulchandani said. "[How] can we absorb the best of what's coming and get it integrated into the system as where the complexity is?"

DOD has had a long history of companies that know how to do that, and harnessing it is the actual work and the piece that we're worried about the most and really are focused on the most, he added.

A global workforce the DOD technology companies are global companies, he emphasized. "These are not linked to a particular geographic region. We hire. We bring the best talent in, wherever it may be, [and we have] research and development arms all over the world."

DOD has special security needs and requirements that must be taken care of when it comes to data, and the JAIC is putting in place very different development processes now to handle AI development, he said. "So, the dynamics of the way software gets built [and] the dynamics of who builds it are changing in a very significant way," Mulchandani said. "But the global war for talent is a real one, which is why we are not actually focused on trying to corner the market on talent."

He said they are trying to build leverage by building relationships with the leading AI companies to harness the innovation.

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Defense Official Calls Artificial Intelligence the New Oil - Department of Defense

An X-class solar flare from our sun in November 2013. Scientists trained a neural network to find such flares in data taken of distant planets around other stars. Credit: Scott Wiessinger, Solar Dynamics Observatory at NASA Goddard Space Flight Center

University of Chicago scientists teach a neural net to find baby star flares.

Like its human counterparts, a young star is cute but prone to temper flaresonly a stars are lethal. A flare from a star can incinerate everything around it, including the atmospheres of any nearby planets starting to form.

Finding out how often such young stars erupt can help scientists understand where to look for habitable planets. But until now, locating such flares involved poring over thousands of measurements of star brightness variations, called light curves, by eye.

Scientists with the University of Chicago and the University of New South Wales, however, thought this would be a task well suited for machine learning. They taught a type of artificial intelligence called a neural network to detect the telltale light patterns of a stellar flare, then asked it to check the light curves of thousands of young stars; it found more than 23,000 flares.

Published on October 23, 2020, in the Astronomical Journaland the Journal of Open Source Software the results offer a new benchmark in the use of AI in astronomy, as well as a better understanding of the evolution of young stars and their planets.

When we say young, we mean only a million to 800 million years old, said Adina Feinstein, a UChicago graduate student and first author on the paper. Any planets near a star are still forming at this point. This is a particularly fragile time, and a flare from a star can easily evaporate any water or atmosphere thats been collected.

NASAs TESS telescope, aboard a satellite that has been orbiting Earth since 2018, is specifically designed to search for exoplanets. Flares from faraway stars show up on TESSs images, but traditional algorithms have a hard time picking out the shape from the background noise of star activity.

NASAs Solar Dynamics Observatory captures flares from the sun. Credit: NASA

But neural networks are particularly good at looking for patternslike Googles AI picking cats out of internet imagesand astronomers have increasingly begun to look to them to classify astronomical data. Feinstein worked with a team of scientists from NASA, the Flatiron Institute, Fermi National Accelerator Laboratory, the Massachusetts Institute of Technology and the University of Texas at Austin to pull together a set of identified flares and not-flares to train the neural net.

It turned out to be really good at finding small flares, said study co-author and former UChicago postdoctoral fellow Benjamin Montet, now a Scientia Lecturer at the University of New South Wales in Sydney. Those are actually really hard to find with other methods.

Once the researchers were satisfied with the neural nets performance, they turned it loose on the full set of data of more than 3,200 stars.

They found that stars similar to our sun only have a few flares, and those flares seem to drop off after about 50 million years. This is good for fostering planetary atmospheresa calmer stellar environment means the atmospheres have a better chance of surviving, Feinstein said.

This can help scientists pinpoint the most likely places to look for habitable planets elsewhere in the universe.

In contrast, cooler stars called red dwarfs tended to flare much more frequently. Red dwarfs have been seen to host small rocky planets; If those planets are being bombarded when theyre young, this could prove detrimental for retaining any atmosphere, she said.

The results help scientists understand the odds of habitable planets surviving around different types of stars, and how atmospheres form. This can help them pinpoint the most likely places to look for habitable planets elsewhere in the universe.

They also investigated the connection between stellar flares and star spots, like the kind we see on our own suns surface. The spottiest our sun ever gets is maybe 0.3% of the surface, Montet said. For some of these stars were seeing, the surface is basically all spots. This reinforces the idea that spots and flares are connected, as magnetic events.

The scientists next want to adapt the neural net to look for planets lurking around young stars. Currently we only know of about a dozen younger than 50 million years, but theyre so valuable for learning how planetary atmospheres evolve, Feinstein said.

Reference: Flare Statistics for Young Stars from a Convolutional Neural Network Analysis of TESS Data by Adina D. Feinstein, Benjamin T. Montet, Megan Ansdell, Brian Nord, Jacob L. Bean, Maximilian N. Gnther, Michael A. Gully-Santiago, and Joshua E. Schlieder, 23 October 2020, The Astronomical Journal.DOI:10.3847/1538-3881/abac0a

Other UChicago-affiliated scientists on the study included visiting assistant research professor Brian Nord and Assoc. Prof. Jacob Bean.

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Artificial Intelligence Helps Understand the Evolution of Young Stars and Their Planets - SciTechDaily

Image: Yuichiro Chino/Getty Images

A future where were all replaced by artificial intelligence may be further off than experts currently predict, but the readiness with which we accept the notion of our own obsolescence says a lot about how much we value ourselves. The long-term danger is not that we will lose our jobs to robots. We can contend with joblessness if it happens. The real threat is that well lose our humanity to the value system we embed in our robots, and that they in turn impose on us.

Computer scientists once dreamed of enhancing the human mind through technology, a field of research known as intelligence augmentation. But this pursuit has been largely surrendered to the goal of creating artificial intelligence machines that can think for themselves. All were really training them to do is manipulate our behavior and engineer our compliance. Figure has again become ground.

We shape our technologies at the moment of conception, but from that point forward they shape us. We humans designed the telephone, but from then on the telephone influenced how we communicated, conducted business, and conceived of the world. We also invented the automobile, but then rebuilt our cities around automotive travel and our geopolitics around fossil fuels. While this axiom may be true for technologies from the pencil to the birth control pill, artificial intelligences add another twist: After we launch them, they not only shape us but they also begin to shape themselves. We give them an initial goal, then give them all the data they need to figure out how to accomplish it. From that point forward, we humans no longer fully understand how an A.I. may be processing information or modifying its tactics. The A.I. isnt conscious enough to tell us. Its just trying everything, and hanging on to what works.

Researchers have found, for example, that the algorithms running social media platforms tend to show people pictures of their ex-lovers having fun. No, users dont want to see such images. But, through trial and error, the algorithms have discovered that showing us pictures of our exes having fun increases our engagement. We are drawn to click on those pictures and see what our exes are up to, and were more likely to do it if were jealous that theyve found a new partner. The algorithms dont know why this works, and they dont care. Theyre only trying to maximize whichever metric weve instructed them to pursue. Thats why the original commands we give them are so important. Whatever values we embed efficiency, growth, security, compliance will be the values A.I.s achieve, by whatever means happen to work. A.I.s will be using techniques that no one not even they understand. And they will be honing them to generate better results, and then using those results to iterate further.

We already employ A.I. systems to evaluate teacher performance, mortgage applications, and criminal records, and they make decisions just as racist and prejudicial as the humans whose decisions they were fed. But the criteria and processes they use are deemed too commercially sensitive to be revealed, so we cannot open the black box and analyze how to solve the bias. Those judged unfavorably by an algorithm have no means to appeal the decision or learn the reasoning behind their rejection. Many companies couldnt ascertain their own A.I.s criteria anyway.

As A.I.s pursue their programmed goals, they will learn to leverage human values as exploits. As they have already discovered, the more they can trigger our social instincts and tug on our heartstrings, the more likely we are to engage with them as if they were human. Would you disobey an A.I. that feels like your parent, or disconnect one that seems like your child?

Eerily echoing the rationale behind corporate personhood, some computer scientists are already arguing that A.I.s should be granted the rights of living beings rather than being treated as mere instruments or slaves. Our science fiction movies depict races of robots taking revenge on their human overlords as if this problem is somehow more relevant than the unacknowledged legacy of slavery still driving racism in America, or the 21st-century slavery on which todays technological infrastructure depends.

We are moving into a world where we care less about how other people regard us than how A.I.s do.

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Artificial Intelligence Will Soon Shape Themselves, and Us - Medium

After spending $2.5 billion over five years, Uber is still far from delivering its self-driving vehicles.

As corporate spending on artificial intelligence systems is set to pass $50 billion this year, the vast majority of companies may not be seeing much return on that record investment.

In a survey of more than 3,000 company managers about their AI spend, only 10% reported significant financial benefits from their investment so far, the new report from MIT Sloan Management Review and Boston Consulting Group found.

Gains from the tech havent kept pace with increased adoption, says Shervin Khodabandeh, who led the study and is co-head of BCGs AI business in North America. We are seeing more activity, which also means more investment in technology and data science, Khodabandeh says. But that impact line hasnt really changed.

The results should prove concerning to corporations that continue to pour money into AI projects at a breakneck clip, looking to use the tools for everything from managing contracts to powering home assistants and self-driving cars. More than $50 billion is expected to be invested in AI systems globally this year, according to IDC, up from $37.5 billion in 2019. By 2024, investment is expected to reach $110 billion, IDC forecasts.

But despite the billions invested, failed AI projects have become an increasing factor. IBM has deprioritized its Watson technology after drawing scorn for ventures like one $62 million oncology project that made inaccurate suggestions on cancer treatments. Amazon canned an AI recruitment tool after it showed misogynistic biases. And smaller businesses have found that building the technology is harder than it looks, as supposedly AI-powered virtual assistants and meetings schedulers end up relying on actual humans behind the scenes.

Companies are struggling to deliver on AI projects, Khodabandeh says, because they overspend on technology and data scientists, without implementing changes in the business processes that could benefit from AI a conclusion that echoes a Harvard Business Review report published in June.

Take Uber. Last month, engineers at the ride-hailing company concluded that its self-driving cars couldnt drive more than half a mile before encountering a problem. The programs artificial intelligence still struggles with simple routines and simple maneuvers, per a report in The Information. Part of the reason for the failure, according to an internal memo: competing internal ideas on how to implement the tech.

But with AIs promise of large-scale business savings and improvements, companies arent likely to stop investing in the technology soon. The BCG and MIT researchers found that 57% of companies said they've deployed or piloted their own AI projects, up from 44% in 2018.

For those projects to pay off, Khodabandeh says more AI adopters will need to rethink how the tech is integrated within their businesses. There's clearly a lot of hype, he says. And some of that hype comes out in the data.

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Companies Will Spend $50 Billion On Artificial Intelligence This Year With Little To Show For It - Forbes

Summary

For breast cancers that have high levels of HER2, advanced MRI scans and artificial intelligence may help doctors make treatment decisions.

For people with breast cancer, biopsies have long been the gold standard for characterizing the molecular changes in a tumor, which can guide treatment decisions. Biopsies remove a small piece of tissue from the tumor so pathologists can study it under the microscope and make a diagnosis. Thanks to advances in imaging technologies and artificial intelligence (AI), however, experts are now able to use the characteristics of the whole tumor rather than the small sample removed during biopsy to assess tumor characteristics.

In a study published October 8, 2020, in EBioMedicine, a team led by experts from Memorial Sloan Kettering report that for breast cancers that have high levels of a protein called HER2 AI-enhanced imaging tools may also be useful for predicting how patients will respond to the targeted chemotherapy given before surgery to shrink the tumor (called neoadjuvant therapy). Ultimately, these tools could help to guide treatment and make it more personalized.

Were not aiming to replace biopsies, says MSK radiologist Katja Pinker, the studys corresponding author. But because breast tumors can be heterogeneous, meaning that not all parts of the tumor are the same, a biopsy cant always give us the full picture.

Because breast tumors can be heterogeneous, meaning that not all parts of the tumor are the same, a biopsy cant always give us the full picture, says breast radiologist Katja Pinker.

The study looked at data from 311 patients who had already been treated at MSK for early-stage breast cancer. All the patients had HER2-positive tumors meaning that the tumors had high levels of the protein HER2, which can be targeted with drugs like trastuzumab (Herceptin). The researchers wanted to see if AI-enhanced magnetic resonance imaging (MRI) could help them learn more about each specific tumors HER2 status.

One goal was to look at factors that could predict response to neoadjuvant therapy in people whose tumors were HER2-positive. Breast cancer experts have generally believed that people with heterogeneous HER2 disease dont do as well, but recently a study suggested they actually did better, says senior author Maxine Jochelson, Director of Radiology at MSKs Breast and Imaging Center. We wanted to find out if we could use imaging to take a closer look at heterogeneity and then use those findings to study patient outcomes.

The MSK team took advantage of AI and radiomics analysis, which uses computer algorithms to uncover disease characteristics. The computer helps revealfeatures on an MRI scan that cant be seen with the naked eye.

In this study, the researchers used machine learning to combine radiomics analysis of the entire tumor with clinical findings and biopsy results. They took a closer look at the HER2 status of the 311 patients, with the aim of predicting their response to neoadjuvant chemotherapy. By comparing the computer models to actual patient outcomes, they were able to verify that the models were effective.

We hope that this will get us to the next level of personalized treatment for breast cancer.

Our next step is to conduct a larger multicenter study that includes different patient populations treated at different hospitals and scanned with different machines, Dr. Pinker says. Im confident that our results will be the same, but these larger studies are very important to do before you can apply these findings to patient treatment.

Once weve confirmed our findings, our goal is to perform risk-adaptive treatment, Dr. Jochelson says. That means we could use it to monitor patients during treatment and consider changing their chemotherapy during treatment if their early response is not ideal.

Dr. Jochelson adds that conducting more frequent scans and using them to guide therapies has improved treatments for people with other cancers, including lymphoma. We hope that this will get us to the next level of personalized treatment for breast cancer, she concludes.

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Imaging and Artificial Intelligence Tools Help Predict Response to Breast Cancer Therapy - On Cancer - Memorial Sloan Kettering