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The Defense Departments Chief Digital and Artificial Intelligence Office, a new hub to align disparate AI-centered pursuits across the vast enterprise, officially reached initial operating capacity this weekbut much must still be puzzled out before its totally realized this summer.

John Sherman, DODs recently Senate-confirmed chief information officer, will play a major role in seeing it through. Hes taking the offices lead as acting chief digital and AI officer until the department completes its search for the right person to fill this first-of-a-kind position.

In addition to getting the OCDAO up and running for [full operational capacity], rest assured we'll remain laser-focused on our CIO dutiescybersecurity, digital modernization and other areas the department relies on us for, Sherman told reporters during a press call on Wednesday. We're not taking our eye off the ball.

He and two other senior defense officials shared fresh details about the new units establishment and what its ultimately meant to accomplish. Two memorandums signed by Deputy Defense Secretary Kathleen Hicks on Tuesday and released on Wednesday also provide further clarity on the OCDAOs functions.

The Pentagon in December announced plans to stand up this central office to underpin the integration and synchronization of all data- and AI-associated work, which is primarily led by DODs Joint AI Center, office of the chief data officer and Defense Digital Service. Officials intend for the tedious reorganization to eventually provide DOD with end-to-end cohesion from the time data is captured, to when it's used for advanced analytics.

The deadline for initial operating capacity set for Feb.1, was met this week. Now, officials are working to reach full capacity by June 1.

Let me just note that the goal here is for data, and data analytics and AI to enable faster and better decision-making, and therefore military advantage from campaigning to conflict, a senior defense official explained. As many of you know, China, in particular, is investing aggressively and using these capabilities to offset traditional U.S. advantages, and this is a key part of our efforts to match that pacing threat.

A memo to Defense leadership announcing that the office reached IOC on time also highlights some of what it will be responsible for during this phase. It notes that officials will need to oversee DODs strategy development and policy formulation for data, analytics and AI; break down barriers to technology adoption; produce an enabling infrastructure for digital solutions; and selectively scale existing assets for enterprise and joint use cases for Pentagon components.

On the call, officials said theyre also working at this point to help create a greater sense of team among the components involved.

Multiple shifts additionally need to be hashed out before the OCDAO can come to full fruition.

For instance, the IOC memo asserts that the DODs chief data officer will continue to report to the CIO for now, in compliance with the 2020 National Defense Authorization Actbut the supporting data office will transfer to the OCDAO. The CDAO is directed in the memo to draft a legislative proposal to permit the CDO to report up to the new office down the line.

The document also confirms that the CDAO has assumed the existing authorities of its component organizations, and the OCDAO is now considered their successor.

Further, it requires DODs CDAO to steer a comprehensive review of all authorities and governance structures connected to DOD data, analytics and AIand provide recommendations for updates to Pentagon leadership by May 1.

Officials on the call noted that, so far, Congress members have been supportive of this ongoing initiative, and are looking forward to DOD moving faster in this technological realm.

I think we have very much appreciated the bipartisan collaborative nature of these engagements with Congress. This is an area where I think in a very polarized time there's just a lot of sort of joint problem solving and figuring out how we can make more rapid progress, a senior defense official said. Theres a lot of good ideas coming from experts and staffers on the Hill on where we can start looking for opportunitiesand we look forward to continued engagement with them.

In a separate memo also published Wednesday, the department also sheds light on how this new CDAO role differs from but will work with other DOD positionssuch as the CIO and undersecretaries for research, engineering and policy, among others. The hub will clarify and codify its final role within its chartering directive due this summer.

During the call, a senior defense official confirmed the CDAOs office will have an initial budget of about $500 million and include about 200-to-300 employees from across the massive enterprise.

It really is a collective ecosystem, Sherman said. We had the parts of it, but were putting it together in a way we havent before to deliver that decision advantage that our leaders need.

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DOD Debuts Office to Help It 'Move Faster' on Artificial Intelligence - Nextgov

If I were to ask someone why they chose a career in information technology, I doubt they would respond withI love data entry!,I could debug code all day long!, orHandling tickets is so much fun, Id do it even if I didnt get paid for it.

Fortunately, AI can help. Here are the top three ways AI can help automate manual IT tasks, thereby freeing up precious resources and benefiting your teams, businesses, and customers.

Grace Murray Hopper was a Navy rear admiral and computer programming pioneer who worked on the Mark II computer at Harvard in the 1940s. On September 9, 1947, Hopper traced an error with the Mark II to of all things a dead moth in the relay. The insects remains were taped in the teams logbook with the caption, First actual case of a bug being found.

While Hopper and her team werent the first to use the term bug to describe a system glitch, they certainly helped popularize it. Of course, software bugs are decidedly unpopular. IT departments and software engineers have all felt the pain of toiling over lines of code trying to reproduce and locate problems.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders:Cheat sheet: AI glossary. ]

To be as good as human engineers, an AI tool would need to possess levels of reasoning and creativity it simply hasnt yet reached. But AI can still be tremendously effective in exception and anomaly detection. You train it on normal usage and it detects when something is off.

Another advantage AI has over humans is its pattern detection. Lets say a system is crashing at the same time every week or after memory usage hits a certain level. An AI tool could easily connect the dots. AI can learn which behaviors of your developers and which code patterns that are checked into your repo are correlated to bugs. This can be used to notify developers that they have done something that is likely to break and ask them to check again.

If you had a moth infestation in your home, you could certainly go around swatting them one by one. But wouldnt it be a lot easier to discover where they hide and put out traps?

The adage an ounce of prevention is worth a pound of cure is as true in IT as it is in medicine. Monitoring operations and taking proactive action instead of just reacting to problems as they arise can prevent unexpected downtime and expensive failures.

CIOs and IT professionals are familiar with the value of preventative maintenance to some degree, whether its installing software updates or creating backups. That kind of maintenance is done after a certain amount of time has elapsed or usage has been logged. Its like eating vegetables or getting exercise theyre sound practices for a company.

[ Read also:4 Robotic Process Automation (RPA) trends to watch in 2022.]

Predictive maintenance, on the other hand, is individualized and custom-tailored. It monitors the equipment and its environment, performs tests, and receives equipment feedback to generate individualized predictions. Its like having a blood test show that youre pre-diabetic and in response, you design a low-sugar diet.

People may be uncomfortable with the idea of machines watching them all day. But with AI-enabled predictive maintenance, you watch the machines with other machines.

Dealing with IT tickets can feel like playing a perpetual game of Whack-A-Mole, but with all of the exhaustion and none of the fun carnival music and prizes.

Dealing with IT tickets can feel like playing a perpetual game of Whack-A-Mole, but with all of the exhaustion and none of the fun carnival music and prizes.

As we all know, some incidents are worth your attention and others arent at all. And without a proper way to triage incidents, IT departments become overwhelmed. Enter intelligent filters. Theyve been around for years in search engines and email inboxes, distinguishing between good and bad, important and unimportant. For IT departments, they can distinguish between real incidents and noise.

More on artificial intelligence

Using AI techniques like case-based reasoning can help decide which solution to explore first or what additional information to request from a customer to make a diagnosis quickly and accurately. Case-based reasoning systems learn from success and failure, apply sophisticated probabilistic reasoning to identify promising solutions, and create a valuable knowledge base.

With intelligent filters and case-based reasoning, IT managers can better allocate resources for incidents that require human intervention.

While there are numerous existing AI applications that help IT departments and many more yet to be discovered debugging, predictive maintenance, and intelligent filtering are three applications of AI that are essential for any great IT department today.

As AI becomes increasingly integrated into our work, any organization that is not actively exploring automating its more manual IT tasks is wasting valuable financial and human capital and may eventually fall behind.

[ How does AI connect tohybrid cloud strategy? Get the free eBooks,Hybrid Cloud Strategy for DummiesandMulti-Cloud Portability for Dummies. ]

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Artificial intelligence (AI): 3 everyday IT tasks where automation fits - The Enterprisers Project

We often think of artificial intelligence (AI) technologies as a gateway to a future written in chrome, operating on a virtual cloud. This techno-optimism underpinned FM Nirmala Sitharamans 2022 budget speech, where AI was described as a sunrise technology that would assist sustainable development at scale and modernise the country. While there is an allure to national dreams of economic prosperity and global competitiveness, underwritten by AI, there is an environmental cost and like any issue at the nexus of technology, development, growth and security a cost that comes with being locked into rules about said environmental impact set by powerful actors.

The race for dominance in AI is far from fair: Not only do a few developed economies possess certain material advantages right from the start, they also set the rules. They have an advantage in research and development, and possess a skilled workforce as well as wealth to invest in AI. North America and East Asia alone account for three-fourths of global private investment in AI, patents and publications.

We can also look at the state of inequity in AI in terms of governance: How tech fluent are policymakers in developing and underdeveloped countries? What barriers do they face in crafting regulations and industrial policy? Are they sufficiently represented and empowered at the international bodies that set rules and standards on AI? At the same time, there is an emerging challenge at the nexus of AI and climate change that could deepen this inequity.

The climate impact of AI comes in a few forms: The energy use of training and operating large AI models is one. In 2020, digital technologies accounted for between 1.8 per cent and 6.3 per cent of global emissions. At the same time, AI development and adoption across sectors has skyrocketed, as has the demand for processing power associated with larger and larger AI models. Paired with the fact that governments of developing countries see AI as a silver bullet for solving complex socio-economic problems, we could see a growing share of AI in technology-linked emissions in the coming decades.

The idea of sustainability is rapidly entering mainstream debates on AI ethics and sustainable development. In November 2021, UNESCO adopted the Recommendation on the Ethics of Artificial Intelligence, calling on actors to reduce the environmental impact of AI systems, including but not limited to its carbon footprint. Similarly, technology giants like Amazon, Microsoft, Alphabet and Facebook have announced net zero policies and initiatives. These initiatives are a good sign, but they only scratch the surface. Both global AI governance and climate change policy (historically) are contentious, being rooted in inequitable access to resources.

Developing and underdeveloped countries face a challenge on two fronts: First, AIs social and economic benefits are accruing to a few countries, and second, most of the current efforts and narratives on the relationship between AI and climate impact are being driven by the developed West.

What then is the way ahead? Like most nexus issues, the relationship between climate change and AI is still a whisper in the wind. It is understudied, not least because the largest companies working in this space are neither transparent nor meaningfully committed to studying, let alone acting, to substantively limit the climate impact of their operations.

Governments of developing countries, India included, should also assess their technology-led growth priorities in the context of AIs climate costs. It is argued that as developing nations are not plagued by legacy infrastructure it would be easier for them to build up better. These countries dont have to follow the same AI-led growth paradigm as their Western counterparts. It may be worth thinking through what solutions would truly work for the unique social and economic contexts of the communities in our global village.

This column first appeared in the print edition on February 3, 2022 under the title The climate costs of AI. The writer is an associate fellow at the Observer Research Foundation

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Artificial intelligence technologies have a climate cost - The Indian Express

DUBLIN--(BUSINESS WIRE)--The "Global Artificial Intelligence in Medical Imaging Market: Size, Trends & Forecast with Impact of COVID-19 (2022-2026)" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence (AI) is a branch of computer science that aims to emulate human intelligence through intelligent systems such as image analysis and speech recognition. Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics.

The global artificial intelligence in medical imaging market can be segmented based on image acquisition technology (X-Ray, CT, MRI, Ultrasound Imaging, and Molecular Imaging); AI technology (Deep Learning and Other AI & Computer Vision); clinical application (Cardiology, Neurology, Breast, Pulmonology, Liver, and Rest of the Body); and end-user (Medical Institutions and Consumer Healthcare Environment).

COVID-19 has a positive effect on market growth. Attempts have also been made to identify various imaging features of chest CT, resulting in increased popularity for AI in the medical imaging market amid the pandemic. However, with COVID-19 cases on the rise across the world, emerging AI technologies are developed to support hospitals in scaling treatment in the second wave. It also highlights the significance of expanding the use of AI and machine learning in imaging, with the dual goals of improving diagnoses and improving clinician well-being and job security.

The global AI in medical imaging market has increased during the years 2019-2021. The projections are made that the market would rise in the next four years i.e. 2022-2026 tremendously. The global AI in medical imaging market is expected to increase due to the increasing burden of chronic diseases, increasing health spending, increasing funding in AI, increasing government expenditure and policy support, etc. Yet the market faces some challenges such as development hurdles, the black-box nature of AI, etc. Moreover, the market growth would succeed by various market trends like increasing diversity in training datasets, detecting multiple diseases from a single image, high image resolution to maximize algorithm performance, etc.

The global AI in the medical imaging market is fragmented. The key players of the global AI in the medical imaging market are IBM (IBM Watson Health), Butterfly Network, Inc., Gauss Surgical, Inc., and Arterys are also profiled with their financial information and respective business strategies.

Market Dynamics

Drivers

Challenges

Trends

Company Coverage:

For more information about this report visit https://www.researchandmarkets.com/r/eq3eya

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Global Artificial Intelligence in Medical Imaging Market (2022 to 2026) - Size, Trends & Forecast with Impact of COVID-19 - ResearchAndMarkets.com...

The Ministry of Agriculture and Forestry plans to implement artificial intelligence (AI) technology to tackle forest fires, which destroyed large swaths of land last year.

AI will be used in the Remote Smoke Detection-Early Fire Warning System developed by the ministry. It will enable a faster response to fires. Forestry Minister Bekir Pakdemirli said the technology will be used in cameras set atop watchtowers in the forests. In an interview published by Yeni afak newspaper on Wednesday, he stated that cameras can detect smoke from a distance up to 20 kilometers (12.4 miles) through smoke perception, and the new system would reduce the detection time to two minutes.

The system is currently installed in Antalya and Mula, two Mediterranean provinces that lost hundreds of acres of forests to devastating wildfires in the summer of 2021, one of the worst and deadliest outbreaks in the region. AI enables us to keep track of the smoke and deploy our teams as soon as possible, Pakdemirli said.

The ministry has 76 smart watchtowers, entirely operated without staff and 103 towers installed with cameras. Cameras, through AI and machine learning, are able to send alarm signals to authorities, via text or multimedia message, upon detection of smoke. Every tower can scan an area of up to 50,000 hectares in two minutes and can send exact coordinates of the fire.

Forest fires, worsened by the ongoing climate crisis, are a major concern for Turkey, which has expanded its forest cover in the past two decades. President Recep Tayyip Erdoan said on Monday after a Cabinet meeting that they were working to boost infrastructure to fight forest fires. We will increase the number of domestically manufactured unmanned aerial vehicles (UAVs) to eight, the number of firefighting planes to 20 and helicopters to 55, Erdoan said.

Turkey suffered from at least 2,105 forest fires last year, though the worst was in Antalya and Mula. Strong winds and extreme temperatures hampered efforts to douse the fires. The country witnessed an unprecedented surge in forest fires starting from the last week of July, a period with the highest number of almost simultaneous forest fires. It took around two weeks for authorities to put out all 240 wildfires that had raged across the country forcing the evacuation of hundreds of people.

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Turkey taps artificial intelligence in its fight against wildfires | Daily Sabah - Daily Sabah