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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

By Richard Shearer, CEO of Tintra PLC

In the modern banking world new technologies play a widely reported role in anti-money laundering (AML) protocols preventing financial crime however it is important that we do not overlook technologys potential for establishing financial innocence.

To businesses and institutions operating in and between developed markets, whose international transactions are fast and painless, this sentiment may seem counter intuitive. AML compliance is necessary for regulatory reasons, and catching out bad actors is, of course, a primary goal of any business but why should we view AML technology through the lens of establishing innocence?

This is a question which emerging market corporates will have no difficulty answering if they have ever attempted to interface with counterparts in developed markets.

Entities based in emerging markets are often tarred with the brush of AML risk due to their geography and unrelated to their specific business, and consequently such organisations find international transactions lengthy, arduous and expensive as they navigate an AML compliance process that operates from a base level that is an unfair assumption of their risk.

As such, in my view, embracing advances in technologies such as natural language processing (NLP) and machine learning (ML) is essential not only for financial services firms looking to enhance their ability to properly mitigate, but to progress the much bigger, and indeed more noble, goal of removing the biases against emerging markets, nationalities or cultures that currently colour the AML landscape.

How then, can NLP and ML technologies help, not only in addressing financial crime, but in creating an environment where those in emerging markets with upstanding credentials are treated and serviced free from these baked in prejudices?

Intelligent machines

Its worth taking a moment to define these terms.

Natural Language Processing pertains, in broad terms, to anything related to processing language. As such, NLP varies in terms of complexity it may be employed for tasks like term frequency, calculating how often a given word appears in a text, but NLP can equally be used for the purposes of translation; classifying the sentiment of a piece of text; or even detecting sarcasm, irony, and fake news in a social media context.

In order to perform the more complex tasks in this spectrum however, machine learning may also be required.

Machine Learning describes a variety of artificial intelligence (AI) with an emphasis on allowing machines to learn in a similar manner to humans, through a mix of data and algorithmic methods.

ML differs from traditional programming. Traditional programs see a solution to a problem defined through hand-crafted rules that are implemented in computer code. In ML, by contrast, the algorithm itself learns those rules and, by extension, how to solve the problem by analysing data.

This principle makes ML considerably more powerful than traditional programming, since it is capable of learning a complicated sets of rules that are impossible to define manually.

AML applications of these Technologies

In the context of AML practices, its not difficult to see the appeal of technology like this.

After all, manual investigations into potentially rogue activities are lengthy processes which involve employees investigating vast swathes of transaction histories and other information and often only happen after the event.

This process is made all the more difficult to manage for financial institutions when a large number of suspicious incident alerts are often false alarms. But each potential issue must be investigated with the same vigour to ensure a robust AML framework.

By contrast NLP/ML allows financial institutions to automate these processes the more sophisticated solutions, that my team and I are very focused on, are capable of interpreting the vast amounts of text-based data that a human would otherwise need to analyse.

These systems are able to recognise patterns and relevant information, consider appropriate context and cross reference faster and more accurately than a human, or indeed teams of humans, may overlook.

Crucially for me, NLP/ML performed by intelligent machines capable of learning can potentially undertake these tasks at the same time as neutralising human bias, which has promising implications for organisations and individuals in emerging markets who face these preconceptive biases frequently.

Less human, more humane

This application of NLP/ML has a range of benefits for all stakeholders, not least with reductions in the level of false positives representing savings in time and money for financial services companies.

There is, however, equal value to be found in NLP/ML tools which bring this power to bear on addressing the inequities that currently prevent frictionless transactions between these markets.

This piece began with reference to establishing cases of financial innocence as well as financial crime and, while NLP/ML makes this possible, it would be wrong to assume that such tools will magically resolve the issue of AML bias.

As such, establishing innocence isnt just a different perspective on the benefits of NLP/ML solutions its an ethos that I believe should be actively pursued by financial services businesses as our global economy becomes more and more integrated.

Removing human prejudice from the decision-making process is vital, but a truly fair approach can only be achieved when the creators of these solutions acknowledge that the prejudice exists in the first place.

After all, NLP/ML is entirely subject to bias or algorithmic unfairness,. A good example taken from research published in ACM Computing Surveys is a piece of software called COMPAS, used by US judges to assess offenders risks of reoffending, which was found to exhibit bias against African-American individuals illustrating clearly that human prejudice can inflect algorithmic decision-making. To make the technology better we need to be better, is may be one way of thinking about it.

This kind of example gives food for thought. If NLP/ML tools are trained without thought being given to how to eradicate bias in an AML context, then well be left with intelligent machines that simply replicate that bias meaning that prejudice will be automated rather than eliminated! A terrifying concept and one fraught with complex ethics.

The next step

The financial services sector is in the midst of digital transformation and as such the time is ripe to seize the wheel and ensure, as we embrace more sophisticated tech solutions, that the journey ends at a fair and equitable destination no matter where a given transaction takes place.

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Artificial Intelligences Role in Banking 3.0 - Global Banking And Finance Review