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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

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

The U.S. Food and Drug Administration on Thursday convened a public meeting of its Patient Engagement Advisory Committee to discuss issues regarding artificial intelligence and machine learning in medical devices.

"Devices using AI and ML technology will transform healthcare delivery by increasing efficiency in key processes in the treatment of patients," said Dr. Paul Conway, PEAC chair and chair of policy and global affairs of the American Association of Kidney Patients.

As Conway and others noted during the panel, AI and ML systems may have algorithmic biases and lack transparency potentially leading, in turn, to an undermining of patient trust in devices.

Medical device innovation has already ramped up in response to the COVID-19 crisis, with Center for Devices and Radiological Health Director Dr. Jeff Shuren noting that 562 medical devices have already been granted emergency use authorization by the FDA.

It's imperative, said Shuren, that patients' needs be considered as part of the creation process.

"We continue to encourage all members of the healthcare ecosystem to strive to understand patients' perspective and proactively incorporate them into medical device development, modification and evaluation," said Shuren. "Patients are truly the inspiration for all the work we do."

"Despite the global challenges with the COVID-19 public health emergency ... the patient's voice won't be stopped," Shuren added. "And if anything, there is even more reason for it to be heard."

However, said Pat Baird, regulatory head of global software standards at Philips, facilitating patient trust also means acknowledging the importance of robust and accurate data sets.

"To help support ourpatients, we need to become more familiar with them, their medical conditions, their environment, and their needs and wantsto be able to better understand the potentially confounding factors that drive some of the trends in the collected data," said Baird.

"An algorithm trained on one subset of the population might not be relevant for a different subset," Baird explained.

For instance, if a hospital needed a device that would serve its population of seniors at a Florida retirement community, an algorithm trained on recognizing healthcare needs of teens in Maine would not be effective.Not every population will have the same needs.

"This bias in the data is not intentional, but can be hard to identify," he continued. He encouraged the development of a taxonomy of bias types that would be made publicly available.

Ultimately, he said, people won't use what they don't trust. "We need to use our collective intelligence to help produce better artificial intelligence populations," he said.

Captain Terri Cornelison, chief medical officer and director for thehealth of women at CDRH, noted that demographic identifiers can be medically significant due to genetics and social determinants of health, among other factors.

"Science is showing us that these are not just categorical identifiers but actually clinically relevant," Cornelison said.

She pointed out that a clinical study that does not identify patients' sex may mask different results for people with different chromosomes.

"In many instances, AI and ML devices may be learning a worldview that is narrow in focus, particularly in the available training data, if the available training data do not represent a diverse set of patients," she said.

"More simply, AI and ML algorithms may not represent you if the data do not include you," she said.

"Advances in artificial intelligence are transforming our health systems and daily lives," Cornelison continued. "Yet despite these significant achievements, most ignore the sex, gender, age, race [and] ethnicity dimensions and their contributions to health and disease differences among individuals."

The committee also examined how informed consent might play a role in algorithmic training.

"If I give my consent to be treated by an AI/ML device, I have the right to know whether there were patients like me ... in the data set," said Bennet Dunlap, a health communications consultant. "I think the FDA should not be accepting or approving a medical device that does not have patient engagement" of the kind outlined in committee meetings, he continued.

"You need to know what your data is going to be used for," he reiterated. "I have white privilege. I can just assume old white guys are in [the data sets]. That's where everybody starts. But that should not be the case."

Dr. Monica Parker, assistant professor in neurology and education core member of the Goizueta Alzheimers Disease Research Center at Emory University, pointed out that diversifying patient data requires turning to trusted entities within communities.

"If people are developing these devices, in the interest of being more broadly diverse, is there some question about where these things were tested?" She raised the issue of testing taking place in academic medical centers or technology centers on the East or West Coast, versus "real-world data collection from hospitals that may be using some variation of the device for disease process.

"Clinicians who are serving the population for which the device is needed" provide accountability and give the device developer a better sense of whothey're treating, Parker said. She also reminded fellow committee members that members of different demographic groups are not uniform.

Philip Rutherford, director of operation at Faces and Voices Recovery, pointed out that it's not just enough to prioritize diversity in data sets.The people in charge of training the algorithm must also not be homogenous.

"If we want diversity in our data, we have to seek diversity in the people that are collecting the data," said Rutherford.

The committee called on the FDA to take a strong role in addressing algorithmic bias in artificial intelligence and machine learning.

"At the end of the day, diversity validation and unconscious bias all these things can be addressed if there's strong leadership from the start," said Conway.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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FDA highlights the need to address bias in AI - Healthcare IT News

Newswise If you think that the Sahara is covered only by golden dunes and scorched rocks, you aren't alone. Perhaps it's time to shelve that notion. In an area of West Africa 30 times larger than Denmark, an international team, led by University of Copenhagen and NASA researchers, has counted over 1.8 billion trees and shrubs. The 1.3 million km2 area covers the western-most portion of the Sahara Desert, the Sahel and what are known as sub-humid zones of West Africa.

"We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed. We counted hundreds of millions of trees in the desert alone. Doing so wouldn't have been possible without this technology. Indeed, I think it marks the beginning of a new scientific era," asserts Assistant Professor Martin Brandt of the University of Copenhagen's Department of Geosciences and Natural Resource Management, lead author of the study'sscientific article, now published inNature.

The work was achieved through a combination of detailed satellite imagery provided by NASA, and deep learning -- an advanced artificial intelligence method. Normal satellite imagery is unable to identify individual trees, they remain literally invisible. Moreover, a limited interest in counting trees outside of forested areas led to the prevailing view that there were almost no trees in this particular region. This is the first time that trees across a large dryland region have been counted.

The role of trees in the global carbon budget

New knowledge about trees in dryland areas like this is important for several reasons, according to Martin Brandt. For example, they represent an unknown factor when it comes to the global carbon budget:

"Trees outside of forested areas are usually not included in climate models, and we know very little about their carbon stocks. They are basically a white spot on maps and an unknown component in the global carbon cycle," explains Martin Brandt.

Furthermore, the new study can contribute to better understanding the importance of trees for biodiversity and ecosystems and for the people living in these areas. In particular, enhanced knowledge about trees is also important for developing programmes that promote agroforestry, which plays a major environmental and socio-economic role in arid regions.

"Thus, we are also interested in using satellites to determine tree species, as tree types are significant in relation to their value to local populations who use wood resources as part of their livelihoods. Trees and their fruit are consumed by both livestock and humans, and when preserved in the fields, trees have a positive effect on crop yields because they improve the balance of water and nutrients," explains Professor Rasmus Fensholt of the Department of Geosciences and Natural Resource Management.

Technology with a high potential

The research was conducted in collaboration with the University of Copenhagen's Department of Computer Science, where researchers developed the deep learning algorithm that made the counting of trees over such a large area possible.

The researchers show the deep learning model what a tree looks like: They do so by feeding it thousands of images of various trees. Based upon the recognition of tree shapes, the model can then automatically identify and map trees over large areas and thousands of images. The model needs only hours what would take thousands of humans several years to achieve.

"This technology has enormous potential when it comes to documenting changes on a global scale and ultimately, in contributing towards global climate goals. We are motivated to develop this type of beneficial artificial intelligence," says professor and co-author Christian Igel of the Department of Computer Science.

The next step is to expand the count to a much larger area in Africa. And in the longer term, the aim is to create a global database of all trees growing outside forest areas.

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Artificial intelligence reveals hundreds of millions of trees in the Sahara - Newswise