Its happening already. Following Dropboxs move to lay off 500 employees as it shifts its focus to AI, IBM now plans to replace 7,800 jobs with AI technology and pause hiring for roles that could be automated. Company CEO Arvind Krishna stated that most back-office positions, such as HR and accounting, will be replaced.

Layoffs due to AI were inevitable, but amid lingering job losses, new jobs are also being created. A report by the World Economic Forum states that demand for AI and machine learning specialists will grow at the fastest rate in the next five years. The organisation has also listed a number of AI jobs that are expected to see massive growth in the coming years. Lets take a look at them.

AI and machine learning specialists: These are professionals who design, develop, and implement AI and ML systems and applications. They use various tools and techniques to analyse data, build models, and optimise algorithms. The demand for AI and machine learning specialists will grow at the fastest rate in the next five years, the WEF report says.

Big data specialists: They specialise in managing, analysing and interpreting large and complex data sets. They use cutting-edge technologies to organise, store, and retrieve vast amounts of information, turning it into valuable insights that can drive business decisions. They work with a variety of industries such as healthcare, finance, and technology, to help them understand and leverage the power of data.

Data engineers: They are responsible for the design, construction and maintenance of the data infrastructure that supports an organisations data management and analytics needs. They develop and manage data pipelines, work with large datasets, and ensure that data is available and accessible to those who need it. They also work with other data professionals to design and implement data architectures that meet the needs of the organisation.

Data analysts and scientists: These are experts who collect, process, and interpret large and complex datasets to generate insights and solutions for various problems and domains. They use statistical methods, programming languages, and visualisation tools to manipulate and communicate data. Data analysts and scientists are expected to see a 32% growth in demand by 2023.

Apart from the aforementioned jobs listed by the World Economic Forum, heres a list of other jobs AI is expected to create in the near future.

AI trainers: They are responsible for teaching machines to learn from data effectively. They also help to ensure that the AI models accurately interpret the data, providing businesses with valuable insights that can drive informed decisions.

AI ethicists: They use their expertise to ensure that AI systems are developed and deployed responsibly. They also identify potential ethical concerns related to privacy, fairness, and transparency, and work to address them through policy and guidelines.

AI user experience designers: They create interfaces and experiences that are intuitive and user-friendly for AI-driven products and services. They also work to ensure that users can easily interact with AI systems, making their experiences more enjoyable and productive.

AI security analysts: They focus on ensuring the safety and integrity of AI-driven solutions. They also identify potential threats, vulnerabilities, and attacks that could compromise AI systems and develop strategies to mitigate them.

Robotics engineers: They design, build, and program autonomous machines that can perform a wide range of tasks, from assembly line work to surgical procedures. By incorporating AI capabilities such as computer vision and natural language processing, they create intelligent machines that can work alongside humans in new and exciting ways.

Of course, these are just a few examples of the new jobs that AI is expected to create. As AI continues to evolve and become more integrated into various industries, its likely that even more new job opportunities will emerge.

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First published on: 03-05-2023 at 19:39 IST

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From machine learning to robotics: WEF report predicts the most lucrative AI jobs - The Indian Express

Services like Google Translate can help millions of people communicate in over 100 languages. Users can type or speak words to be translated, or even translate text in photos and videos using augmented reality.

Now, computer science professor Andrea Salgian and Ben Guerrieri 26 are working to add one more language to the list: American Sign Language.

Using computer vision and machine learning, the researchers are setting out to create a program to serve as a Google Translate tool for ASL speakers to sign to the camera and receive a direct translation.

Right now, were looking at recognizing letters and words that have static gestures, Salgian said, referring to letters in the ASL alphabet with no hand movement. The program will act more like a dictionary at first. The pair will then develop the automated translation, she explained.

Salgians research utilizes a free machine-learning framework called Mediapipe, which is developed by Google and uses a camera to detect joint locations in real time. The program tracks the users movements, provides the coordinates of every single joint in the hand, and uses the coordinates to extract gestures that are matched to ASL signs.

Computer science major Ben Guerrieri 26 discovered Salgians project shortly after arriving at TCNJ and is now working alongside her in this AI research.

Its such a hands-on thing for me to do, he said of his contribution to the project, which consists of researching and developing the translator algorithms. We get to incrementally develop algorithms that have super fascinating real-time results.

This project is part of Salgians on-going interest and research into visual gesture recognition that also includes applications to musical conducting and exercising.

ASL is a fascinating application, especially looking at the accessibility aspect of it, Salgian said. To make communication possible for those who dont speak ASL but would love to understand would mean so much, Salgian said.

Kaitlyn Bonomo 23

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We want to have the results within a week, so that we can really accelerate decision-making for climate scientists, said Yu, who is an assistant professor in the Department of Computer Science and Engineering at the Jacobs School of Engineering and the Halcolu Data Science Institute.

Ambitious? Yes. But thats where artificial intelligence comes in. Thanks to a $3.6 million grant awarded in 2021 by the Department of Energy, Yu and two UC San Diego colleagues, Yian Ma and Lawrence Saul, have teamed up with researchers at Columbia University and UC Irvine to develop new machine learning methods that can speed up these climate models, better predict the future, and improve our understanding of climate extremes.

This work comes at a crucial time, as it becomes increasingly important that we develop an accurate understanding of how climate change is impacting our Earth, our communities and our daily livesand how to use that newfound knowledge to inform climate action. To date, the team has published more than 20 papers in both machine learning and climate science-related journals as they continue to push the boundaries of science and engineering on this highly consequential front.

To increase the accuracy of predictionsand quantify their inherent uncertaintythe team is working on customizing algorithms to embed physical laws and first principles into deep learning models, a form of machine learning that essentially imitates the function of the human brain. Its no small task, but its given them the opportunity to collaborate closely with climate scientists who are putting these machine learning methods into practical algorithms in climate modeling.

Because of this grant, we have established new connections and new collaborations to expand the impact of AI methods to climate science, said Yu. We started working on algorithms and models with the application of climate in mind, and now we can really work closely with climate scientists to validate our models.

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The AI Revolution is Upon UsAnd UC San Diego Researchers Are ... - University of California San Diego

Researchers at the University of Glasgow have published a paper that highlights their so-called ThermoSecure implementation for discovering passwords and PINs. The name ThermoSecure provides a clue to the underlying methodology, as the researchers are using a mix of thermal imaging technology and AI to reveal passwords from input devices like keyboards, touchpads, and even touch screens.

Before looking at the underlying techniques and technology, it's worth highlighting how impressive ThermoSecure is for uncovering password inputs. During tests, the research paper states: "ThermoSecure successfully attacks 6-symbol, 8-symbol, 12-symbol, and 16-symbol passwords with an average accuracy of 92%, 80%, 71%, and 55% respectively." Moreover, these results were from relatively cold evidence, and the paper adds that "even higher accuracy [is achieved] when thermal images are taken within 30 seconds."

How does ThermoSecure work? The system needs a thermal camera, which is becoming a much more affordable item in recent years. A usable device may only cost $150, according to the research paper. On the AI software side of things, the system uses an object detection technique based on Mask RCNN that basically maps the (thermal) image to keys. Across three phases, variables like keyboard localization are considered, then key entry and multi-press detection is undertaken, then the order of the key presses is determined by algorithms. Overall it appears to work pretty well, as the results suggest.

With the above thermal attack looking quite viable option for hackers to spy passwords, PINs, and so on, what can be done to mitigate the ThermoSecure threat? We've gathered the main factors that can impact the success of a thermal attack.

Input factors: Users can be more secure by using longer passwords and typing faster. "Users who are hunt-and-peck typists are particularly vulnerable to thermal attacks," note the researchers.

Interface factors: The thermodynamic properties of the input device material is important. If a hacker can image the input device in under 30 seconds, it helps a lot. Keyboard enthusiasts will also probably be interested to know that ABS keycaps retained touch heat signatures much longer than PBT keycaps.

Erase activity: The heat emitted from backlit keyboards helps disguise the heat traces from the human interaction with the keyboard. A cautious person could sometimes touch keys without actuating them and not leave the input area for at least a minute after they input the username / password.

Go passwordless: Even the best passwords are embarrassingly insecure compared to alternative authentication methods such as biometrics.

In summary, the accuracy of these thermal attacks is surprisingly high, even some time after the user has moved away from the keyboard / keypad. It is worrying but no more so than the other surveillance / skimming techniques that are already widespread. The best solution to these kinds of password and PIN guessing methods appears to be the move to biometrics, and / or two or more factor authentication. Preventing unauthorized access to your device in the first place (i.e. not leaving your laptop or phone unattended), especially not right after typing in your PIN/password, will also help thwart attackers.

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Thermal Cameras and Machine Learning Combine to Snoop Out Passwords - Tom's Hardware

Q: What are the challenges to undertaking data analytical research? And where have these modes of analysis been successful?

The challenges are many, especially when you want to make a meaningful impact in one of the most complex sectorsthe health care sector. The health care sector involves a variety of stakeholders, especially in the United States, where health care is extremely decentralized yet highly regulated, for example in the areas of data collections and data use. Analytics-based solutions that can help one part of this sector might harm other parts, making finding globally optimal solutions in this sector extremely difficult. Therefore, finding data-driven approaches that can have public impact is not a walk in the park.

Then there are various challenges in implementation. In my lab, we can design advanced machine learning and AI algorithms that have outstanding performance. But if they are not implemented in practice, or if the recommendations they provide are not followed, they wont have any tangible impact.

In some of our recent experiments, for example, we found that the algorithms we had designed outperformed expert physicians in one of the leading U.S. hospitals. Interestingly, when we provided physicians with our algorithmic-based recommendations, they did not put much weight on the advice they got from the algorithms, and ignored it when treating patients, although they knew the algorithm most likely outperforms them.

We then studied ways of removing this obstacle. We found that combining human expertise with the recommendations provided by algorithms not only made it more likely for the physicians to put more weight on the algorithms advice, but also synthesized recommendations that are superior to both the best algorithms and the human experts.

We have also observed similar challenges at the policy level. For example, we have developed advanced algorithms trained on large-scale data that could help the Centers for Disease Control and Prevention improve its opioid-related policies. The opioid epidemic caused more than 556,000 deaths in the United States between 2000 and 2020, and yet the authorities still do not have a complete understanding of what can be done to effectively control this deadly epidemic. Our algorithms have produced recommendations we believe are superior to the CDCs. But, again, a significant challenge is to make sure CDC and other authorities listen to these superior recommendations.

I do not want to imply that policymakers or other authorities are always against these algorithm-driven solutionssome are more eager than othersbut I believe the helpfulness of algorithms is consistently underrated and often ignored in the practice.

Q: How do you think about the role of oversight and regulation in this field of new technologies and data analytical models?

Imposing appropriate regulations is important. There is, however, a fine line: while new tools and advancements should be guarded against misuses, the regulations should not block these tools from reaching their full potential.

As an example, in a paper that we published in the National Academy of Medicine in 2021, we discussed that the use of mobile health (mHealth) interventions (mainly enabled through advanced algorithms and smart devices) have been rapidly increasing worldwide as health care providers, industry, and governments seek more efficient ways of delivering health care. Despite the technological advances, increasingly widespread adoption, and endorsements from leading voices from the medical, government, financial, and technology sectors, these technologies have not reached their full potential.

Part of the reason is that there are scientific challenges that need to be addressed. For example, as we discuss in our paper, mHealth technologies need to make use of more advanced algorithms and statistical experimental designs in deciding how best to adapt the content and delivery timing of a treatment to the users current context.

However, various regulatory challenges remainsuch as how best to protect user data. The Food and Drug Administration in a 2019 statement encouraged the development of mobile medical apps (MMAs) that improve health care but also emphasized its public health responsibility to oversee the safety and effectiveness of medical devicesincluding mobile medical apps. Balancing between encouraging new developments and ensuring that such developments abide by the well-known principle of do no harm is not an easy regulatory task.

At the end, what is needed are two-fold: (a) advancements in the underlying science, and (b) appropriately balanced regulations. If these are met, the possibilities for using advanced analytics science methods in solving our lingering societal problems are endless.

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Big data and machine learning can usher in a new era of policymaking - Harvard Kennedy School

Health innovation in Canada has always been strong, but the sector is now experiencing growth at a pace we havent seen before.

While COVID-19 helped accelerate change, new technologies like OpenAIs ChatGPT are also having an impact. Plus, Canadian companies are leveraging machine learning to develop new therapies, diagnostics and patient platforms.

Theres a lot of really interesting drivers out there for innovation, says Jacki Jenuth, partner and chief operating officer at Lumira Ventures. Were starting to better define some of the underlying mechanisms and therapeutics approaches for diseases that up until now had no options, such as neurodegenerative diseases. And researchers are starting to define biomarkers to select patients more likely to respond in clinical settings thats really good news.

Next week, the annual MaRS Impact Health conference will bring together health care professionals, entrepreneurs, investors, policymakers and other stakeholders. Heres a sneak preview of some of the emerging trends in the health care and life sciences space theyll be exploring.

There's huge revenue opportunities in women's health, says Annie Thriault, managing partner at Cross-Border Impact Ventures. (Fryer, Tim)

Womens health funding isnt where it should be, says Annie Thriault, managing partner at Cross-Border Impact Ventures. Bayer recently announced its stopping R&D for womens health to focus on other areas. Other pharmaceutical companies such as Merck have made similar decisions in recent years. Its hard to imagine why groups are moving in that direction, because were seeing huge revenue opportunities in these markets, says Thriault. A lot of exciting things are happening.

One area that Thriault has been watching closely has been personalized medicine that uses artificial intelligence, machine learning or sophisticated algorithms to tailor treatment for women and children. For instance, there are tools that provide targeted cancer treatments that use gender as a key input. In the past, that maybe wouldnt have been thought of as an important variable, she says.

In prenatal care, there are new tools related to diagnosing anomalies in pregnancies through data. What we see in maternal health is a lot of inequalities, Thriault says. But if the exam is performed with the same level of care, accuracy, and specificity, then analyzed through AI to spot problems, you can make positive health outcomes and hopefully a less unequal health system.

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With the right protections and security measures, AI could help create efficiencies in health care, says Frank Rudzicz, a ??faculty member at the Vector Institute for Artificial Intelligence. (Fryer, Tim)

New technologies like ChatGPT have shown the potential of not just getting AI and machine learning to take large data sets and make sense of them, but also to create efficiencies when it comes to doing paperwork with that information.

I always thought wed get to this point, but I just didnt think wed get to here so soon where we are talking about AI really changing the nature of jobs, says Frank Rudzicz, a faculty member at the Vector Institute for Artificial Intelligence. And its just getting started.

There are a lot of inefficiencies in health care that AI can help with. Doctors, for instance, spend up to half their time working on medical records and filling out forms. (A recent study from the Canadian Federation of Independent Business found that collectively they are spending some 18.5 million hours on unnecessary paperwork and administrative work each year the equivalent of more than 55 million patient visits.) Thats not what they signed up for, he says. They signed up to help people.

While people are becoming more comfortable with using technology to track and monitor their health whether that be through smartwatches, smartphone apps or genetic testing there arent as many connection points for them to use that data with their family doctor. There is an opportunity, Rudzicz says, to use data and technologies such as machine learning, with proper guardrails and patient consent, to sync the data with your doctors records to help with diagnosis and prescribing.

Ultimately, doctors are trained professionals and they need to be the ones who make the diagnosis and come up with treatment plans with the patients, he says. But once you get all the pieces together, the results could be more accurate and safer than they have been.

Plus, there are a lot of possible futures for technologies like ChatGPT in health care, such as automating repetitive tasks like filling out forms or writing requisitions and referral letters for doctors to review before submitting. The barrier to entry for anything that will speed up your workflow is going to be very low and easily integrated, Rudzicz says.

While there's been a slowdown in venture capital investments, there's still funding to be found, says Jacki Jenuth, partner and chief operating officer at Lumira Ventures. (Fryer, Tim)

While theres been a slowdown in venture capital funding, with fewer dollars available as markets become more rational after the record highs of the last few years, theres still funding to be found, says Lumiras Jenuth. Management teams in the life sciences space just have to be more resourceful and explore all possible avenues of funding, including corporations, non-dilutive sources, foundations and disease specific funders, she adds.

It helps to build deep relationships with investors who want to make an impact in the health sectors, she says. The pitch needs to be targeted for each one of these groups. Youll hear a lot of nos, so you need to be tenacious. Its not easy.

Discover more of the technologies and ideas that will transform health care at the MaRS Impact Health conference on May 3 and 4.

Disclaimer This content was produced as part of a partnership and therefore it may not meet the standards of impartial or independent journalism.

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How ChatGPT might help your family doctor and other emerging health trends - Toronto Star

Watch out for the man with the silicon chipHold on to your job with a good firm gripCause if you dont youll have had your chipsThe same as my old man

Scottish revival singer-songwriter Ewan MacColls 1986 track My Old Man was an ode to his father, an iron-moulder who faced an existential threat to his job because of the advent of technology. The lyrics could finds some resonance nearly four decades on, as industry leaders and tech stalwarts predict the advancement in large language models such as OpenAIs GPT-4 and their ability to write essays, code, and do maths with greater accuracy and consistency, heralding a fundamental tech shift; almost as significant as the creation of the integrated circuit, the personal computer, the web browser or the smartphone. But there still are question marks over how advanced chatbots could impact the job market. And if the blue collar work was the focus of MacColls ballad, artificial intelligence (AI) models of the generative pretrained transformer type signify a greater threat for white collar workers, as more powerful word-predicting neural networks that manage to carry out a series of operations on arrays of inputs end up producing output that is significantly humanlike. So, will this latest wave impact the current level of employment?

According to Goldman Sachs economists Joseph Briggs and Devesh Kodnani, the answer is a resounding yes, and they predict that as many as 300 million full-time jobs around the world are set to get automated, with workers replaced by machines or AI systems. What lends credence to this stark prediction is the new wave of AI, especially large language models that include neural networks such as Microsoft-backed OPenAIs ChatGPT.

The Goldman Sachs economists predict that such technology could bring significant disruption to the labour market, with lawyers, economists, writers, and administrative staff among those projected to be at greatest risk of becoming redundant. In a new report, The Potentially Large Effects of Artificial Intelligence on Economic Growth, they calculate that approximately two-thirds of jobs in the US and Europe are set to be exposed to AI automation, to various degrees.

In general white-collar workers, and workers in advanced economies in general, are projected to be at a greater risk than blue collar workers in developing countries. The combination of significant labour cost savings, new job creation, and a productivity boost for non-displaced workers raises the possibility of a labour productivity boom like those that followed the emergence of earlier general-purpose technologies like the electric motor and personal computer, the report said.

And OpenAI itself predicts that a vast majority of workers will have at least part of their jobs automated by GPT models. In a study published on the arXiv preprint server, researchers from OpenAI and the University of Pennsylvania said that 80 percent of the US workforce could have at least 10 percent of their tasks affected by the introduction of GPTs.

Central to these predictions is the way models such as ChatGPT get better with more usage GPT stands for Generative Pre-trained Transformer and is a marker for how the platform works; being pre-trained by human developers initially and then primed to learn for itself as more and more queries are posed by users to it. The OpenAI study also said that around 19 per cent of US workers will see at least 50 per cent of their tasks impacted, with the qualifier that GPT exposure is likely greater for higher-income jobs, but spans across almost all industries. These models, the OpenAI study said, will end up as general-purpose technologies like the steam engine or the printing press.

A January 2023 paper, by Anuj Kapoor of the Indian Institute of Management Ahmedabad and his co-authors, explored the question of whether AI tools or humans were more effective at helping people lose weight. The authors conducted the first causal evaluation of the effectiveness of human vs. AI tools in helping consumers achieve their health outcomes in a real-world setting by comparing the weight loss outcomes achieved by users of a mobile app, some of whom used only an AI coach while others used a human coach as well.

Interestingly, while human coaches scored higher broadly, users with a higher BMI did not fare as well with a human coach as those who weighed less.

The results of our analysis can extend beyond the narrow domain of weight loss apps to that of healthcare domains more generally. We document that human coaches do better than AI coaches in helping consumers achieve their weight loss goals. Importantly, there are significant differences in this effect across different consumer groups. This suggests that a one-size-fits-all approach might not be most effective Kapoor told The Indian Express.

The findings: Human coaches help consumers achieve their goals better than AI coaches for consumers below the median BMI relative to consumers who have above-median BMI. Human coaches help consumers achieve their goals better than AI coaches for consumers below the median age relative to consumers who have above-median age.

Human coaches help consumers achieve their goals better than AI coaches for consumers below the median time in a spell relative to consumers who spent above-median time in a spell. Further, human coaches help consumers achieve their goals better than AI coaches for female consumers relative to male consumers.

While Kapoor said the paper did not go deeper into the why of the effectiveness of AI+Human plans for low BMI individuals over high BMI individuals, he speculated on what could be the reasons for that trend: Humans can feel emotions like shame and guilt while dealing with other humans. This is not always true, but in general and theres ample evidence to suggest this research has shown that individuals feel shameful while purchasing contraceptives and also while consuming high-calorie indulgent food items. Therefore, high BMI individuals might find it difficult to interact with other human coaches. This doesnt mean that health tech platforms shouldnt suggest human plans for high BMI individuals. Instead, they can focus on (1) Training their coaches well to make the high BMI individuals feel comfortable and heard and (2) deciding the optimal mix of the AI and Human components of the guidance for weight loss, he added.

Similarly, the female consumers responding well to the human coaches can be attributed to the recent advancements in the literature on Human AI interaction, which suggests that the adoption of AI is different for females/males and also theres differential adoption across ages, Kapoor said, adding that this can be a potential reason for the differential impact of human coaches for females over males.

An earlier OECD paper on AI and employment titled New Evidence from Occupations most exposed to AI asserted that the impact of these tools would be skewed in favour of high-skilled, white-collar ones, including: business professionals; managers; science and engineering professionals; and legal, social and cultural professionals.

This contrasts with the impact of previous automating technologies, which have tended to take over primarily routine tasks performed by lower-skilled workers. The 2021 study noted that higher exposure to AI may be a good thing for workers, as long as they have the skills to use these technologies effectively. The research found that over the period 2012-19, greater exposure to AI was associated with higher employment in occupations where computer use is high, suggesting that workers who have strong digital skills may have a greater ability to adapt to and use AI at work and, hence, to reap the benefits that these technologies bring. By contrast, there is some indication that higher exposure to AI is associated with lower growth in average hours worked in occupations where computer use is low. On the whole, the study findings suggested that the adoption of AI may increase labour market disparities between workers who have the skills to use AI effectively and those who do not. Making sure that workers have the right skills to work with new technologies is therefore a key policy challenge, which policymakers will increasingly have to grapple with.

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Machine learning: As AI tools gain heft, the jobs that could be at stake - The Indian Express

Chicago - David Higginson has some advice for hospitals and health systems looking to use machine learning.

"Get started," he says.

Higginson, the chief innovation officer of Phoenix Children's Hospital, offered a presentation on machine learning at the HIMSS Global Health Conference & Exhibition. He described how machine learning models helped identify children with malnutrition and people who would be willing to donate to the hospital's foundation.

After the session, he spoke with Chief Healthcare Executive and offered some guidance for health systems looking to do more with machine learning.

"I would say get started by thinking about how you going to use it first," Higginson says. "Don't get tricked into actually building the model."

"Think about the problem, frame it up as a prediction problem," he says, while adding that not all problems can be framed that way.

"But if you find one that is a really nice prediction problem, ask the operators, the people that will use it everyday: 'Tell me how you'd use this,'" Higginson says. "And work with them on their workflow and how it's going to change the way they do their job.

"And when they can see it and say, 'OK, I'm excited about that, I can see how it's going to make a difference,' then go and build it," he says. "You'll have more motivation to do it, you'll understand what the goal is. But when you finally do get it, you'll know it's going to be used."

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David Higginson of Phoenix Children's Hospital on using machine ... - Chief Healthcare Executive

Available today, customers can automatically generate tailored COIs in under a minute, furthering NEXT's commitment to provide a simple and efficient insurance experience

PALO ALTO, Calif., April 26, 2023 /PRNewswire/ --NEXT Insurance, a leading digital insurtech company transforming small business insurance, today announced the launch and availability of the Certificate of Insurance (COI) Analyzer an innovative, new offering for small business owners to generate free, instant, custom-made COIsto show valid insurance coverage to potential employers in under a minute. This new offering is the latest iteration of NEXT's commitment to advancing innovation in the small business insurance space, fulfilling its promise to provide a simple and streamlined insurance experience.

A COI is often required and may make the difference between being hired or not for a job. NEXT's COI Analyzer enables customers to upload a sample certificate and receive an automatically generated COI within seconds,via the 24/7 self-service portal on desktop or mobile app. Advanced machine learning models read the sample document using Optical Character Recognition (OCR) and an Object Detector Network, to accurately extract and understand the certificate holder details, as well as any special requirements that may be included in the sample certificate.

"Insurance shouldn't stall a small business owner from thriving, it should empower them to build, launch, grow and expand. This new innovation will only speed up the owners' mission to meet the next job opportunity, challenge and goal, and we're excited to be part of that success story," said Effi Fuks-Leichtag CPO at NEXT. "Leveraging the latest machine learning models, we're able to remove the guesswork, likelihood of human error and ensure that the COI is right the first time so that the individual can get back to their passion of running a business."

For businesses including those in construction, retail, cleaning professionals, sports and fitness and more, a new and personalized COI is often required for each and every job. In fact, NEXT has confirmed that some construction business owners may need to share a COI nearly 200 times a year.In 2022, NEXT's customers on average created 16,314 COIs per month, with 9,215 coming from construction businesses 1,204 from retail and 984 from cleaning professionals. With that much documentation from differing customers and businesses, comes countless potential inputs and needs for completing a COI. This also benefits insurance agents who regularly receive COI examples from customers reviewing new job opportunities. They are required to both verify that their clients have the correct coverage, and also create their COI for them. This new feature can now save agents time on both fronts. Now,in less than a minute from start to finish, the COI Analyzer speeds up the process, eliminates errors and ensures a modern experience.

"As a fitness, nutrition and wellness coach, COIs are critical for me to quickly secure jobs and maintain my work with clients," said Laura Jean, founder and CEO of Fit by LJ, Inc. "Every six months I may need to create up to four different COIs, so efficiency and accuracy for each request are crucial. NEXT's COI Analyzer eliminated several tedious steps from the process, saving me an average of 10 minutes. Just recently, I used the COI Analyzer to complete the process and after NEXT automatically sent the proof to my potential employer, I was hired within 20 minutes."

Visit us to learn more about the advantage of NEXT's free digital Certificates of Insurance and how to generate free, instant, custom-made Certificates of Insurance with the COI Analyzer.

About NEXT InsuranceNEXT Insurance is transforming small business insurance with simple, digital and affordable coverage tailored to the self-employed. Trusted by over 450,000 business owners, NEXT offers policies that are easy to buy and provides 24/7 access to Live Certificates of Insurance, Additional Insured, and more, with no extra fees. Revolutionizing a historically complicated insurance industry, NEXT utilizes AI and machine learning to simplify the purchasing process and provide more affordable coverage. Founded in 2016, the company is headquartered in Palo Alto, has received a total of $881 million in venture capital funding, is rated "A- Excellent" by AM Best and has been recognized by CNBC Disruptor 50, Forbes Fintech 50,Inc.'s Best-Led Companies, and Forbes Best Startup Employers. For more information visit NEXTInsurance.com. To learn more about partnering with NEXT and the value of embedded insurance please visit NEXT's partner page. Stay up to date on the latest with NEXT on Twitter, LinkedIn, Facebook and our blog.

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The security-focused RSA Conference 2023 prominently featured AI, the topic of many sessions and announcements and the specialty of multiple award winners.

The latter includes HiddenLayer, a company that provides security for machine learning, which was named the "Most Innovative Startup" and won the annual RSAC Innovation Sandbox. That contest, in its 18th year, involved 10 finalists who each had three minutes to woo a panel of judges on their respective offerings. As noted, the winner's offering is centered around AI -- specifically machine learning -- like most things these days.

"HiddenLayer was selected by a panel of esteemed judges for helping enterprises safeguard the machine learning models behind their critical products with a comprehensive security platform," RSA Conference said in an April 24 news release. "An AI application security company based out of Austin, Texas, its patent-pending solutions monitor machine learning algorithms for adversarial ML attack techniques."

The company's web site says, "HiddenLayer's patent-pending solution provides a noninvasive, software-based platform that monitors the inputs and outputs of your machine learning algorithms for anomalous activity consistent with adversarial ML attack techniques. Response actions are immediate with a flexible response framework to protect your ML."

Another AI specialist, Concentric AI, a vendor of intelligent AI-based solutions for autonomous data security posture management, won the Publisher's Choice Award for Data Security Posture Management for its Semantic Intelligence solution, bestowed by Cyber Defense Magazine. Concentric AI's site says, "Concentric Semantic Intelligence solution uses sophisticated machine learning technologies to autonomously scan and categorize data. Our solution discovers and categorizes all your data, from financial data to PII/PHI/PCI to intellectual property to business confidential information."

Cyber Defense Magazine has named winners in various categories at the event for years. Several of those categories relate to AI or machine learning, with pertinent winners, their specific award and their category, being:

As far as announcements, there were plenty of AI-themed ones made during the conference from vendors both small and big. For the latter, for example, Google Cloud announced Security AI Workbench, described as an industry-first platform that enables security partners to extend generative AI to their products. You can read more about that in the Virtualization & Cloud Review article, "Google Matches Microsoft with AI-Powered Security Offering."

IBM, which famously switched its focus to hybrid cloud and AI a few years ago, launched the new QRadar Security Suite to speed threat detection and response.

The new offering includes EDR/XDR, SIEM, SOAR and a new cloud-native log management capability, all of which are built around a common user interface, shared insights and connected workflows, said IBM, which listed the following core design elements:

There were also many AI-related sessions, such as "Quick Look: ChatGPT: A New Generation of Dynamic Machine Based Attacks?"

"In the last year there has been much furor around ChatGPT, a chatbot that is evolving both through trained and reinforced learning techniques," RSA said about that session. "As with every scientific advancement that has noble intent, there will inevitably be a scope for misuse. This session will explore the art of the possible and consider whether ML can outsmart the human in the cyber attack domain."

There were plenty more sessions, announcements and awards related to AI at RSA Security 2023, which can be investigated further at the conference site. The big increase in AI-related content compared to a year ago (the pre-ChatGPT era) shows just how much AI is changing the security space, along with most other things these days. The conference is ending today (April 27).

About the Author

David Ramel is an editor and writer for Converge360.

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RSA Conference's 'Most Innovative Startup' Does ... You Guessed It: AI - Virtualization Review

Artificial intelligence has transitioned from being justinterestingto deliveringimpact for businesses and consumers

This is an exclusive article series conducted by the Editor Team of CIO News with Abhishek Dwivedi,Vice President of Technology atVista

Introduction

Machine learning and artificial intelligence are rapidly growing fields that have had a significant impact on various industries. Predictions show that the AI market will reach $500 billion by 2023 and an estimated $1,597.1 billion by 2030, highlighting the continued demand for machine-learning technologies in the coming years.

In 2023, we can expect to see increased adoption of ML in several technical segments, including creative AI, autonomous systems, enterprise management, and cybersecurity. ML will continue to play a crucial role in improving efficiency and enhancing work security across a broader range of business fields.

Generative AI

Generative AI allows enterprises to generate a range of content, such as images, videos, and written material, thereby reducing turnaround time. These artificial intelligence networks utilise transfer-style learning or general adversarial networks to create content from various sources. Not only does this technology have obvious applications in marketing, but it could also have a major impact on the media industry. The filmmaking process could be transformed with the ability to restore old films in high definition and enhance special effects. Additionally, building avatars in the metaverse is just one of many limitless possibilities.

Large language models, such as GPT-3, will also play a key role in creating compelling content across various genres, including fiction, non-fiction, and academic articles. However, its important to be aware of potentially malicious applications, including the creation of deep fakes and the spread of fake news and propaganda. To address these emerging threats, GPTZero is already being developed to distinguish between AI-generated content and text written by humans.

Adaptive AI

Artificial intelligence holds the potential for organisations to make rapid progress by continually learning and generating new data insights. Adaptive AI, which can modify its own code to accommodate unforeseen changes, enables design adaptability and resilience. This allows the artificial intelligence system to continuously learn and react to changes in real time, bypassing the traditional learning phase. The operationalization of AI is crucial, as it facilitates the rapid development, deployment, adaptation, and maintenance of artificial intelligence across various enterprise environments. Self-adaptive artificial intelligence models are capable of faster and more accurate development, leading to improved user experiences that adapt to changing real-world situations. The future will belong to a continuous learning approach, adapting to incoming signals and making personalised experiences ubiquitous in any shopping format.

Edge AI

The rise of mobile computing and IoT has led to a massive increase in the number of connected devices, generating a large amount of data at the network edge. This has caused high latency and network bandwidth usage when collecting data in cloud data centres. To address this issue, edge artificial intelligence (Edge AI) has emerged as a solution that balances the use of centralised data centres (the cloud) and devices closer to humans and physical objects (the edge). With advancements in technology such as 5G, low-power, high-performance hardware, and faster networks, edge AI has become more accessible.

Lower computing costs due to reduced data requirements are creating a market for smart and responsive devices, especially in industries such as healthcare and finance, where data management is regulated. With edge AI, models are tailored to the specific edge environment, and critical data is kept within the edge network. Edge AI will see widespread adoption in industries such as smart warehouses, manufacturing, and utilities as organisations aim to reduce the carbon footprint of artificial intelligence and meet sustainability goals.

Explainable AI

Explainable Artificial Intelligence (XAI) is a crucial aspect of artificial intelligence development that enables human users to understand and trust the results generated by machine learning algorithms. XAI helps to describe the workings of an artificial intelligence model, its expected impact, and any potential biases that may be present. This helps to increase the transparency, fairness, and accuracy of artificial intelligence-powered decision-making, building trust and confidence among stakeholders.

There are various techniques that can be used to increase the interpretability of AI models, such as LIME and SHAP. LIME perturbs the inputs and assesses the impact on the output, while SHAP uses a game theory-based approach to analyse the combined effects of various features on the resulting delta. This creates explainability scores that highlight which aspects of the input had the greatest impact on the output. For example, in image-based predictions, the dominant area or pixels contributing to the output can be identified.

As the impact of artificial intelligence continues to increase in business and society, it is crucial to consider the potential ethical issues that may arise from these complex use cases. This includes implementing proper data governance frameworks, tools to detect bias, and factors for transparency to ensure compliance with legal and social structures. Models will need to be thoroughly tested for drifts, humility, and bias, and proper model validation and audit mechanisms with built-in explainability and reproducibility checks will become standard practise to prevent ethical lapses.

Conclusion

In 2023, machine learning will continue to be a promising and rapidly growing field that will present many interesting innovations. Artificial intelligence has transitioned from being justinterestingto deliveringimpact for businesses and consumers. Many core AI technologies like large language models, multimodal machine learning, transformers, and TinyML will gain considerable importance in the near and mid-term future, leading to standardised software and devices that organisations use daily that will become smarter with the infusion of AI.

Also read:AI and ML, two rapidly growing fields in the realm of computer science, Aravind Raghunathan

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CIO News, a proprietary of Mercadeo, produces award-winning content and resources for IT leaders across any industry through print articles and recorded video interviews on topics in the technology sector such as Digital Transformation, Artificial Intelligence (AI), Machine Learning (ML), Cloud, Robotics, Cyber-security, Data, Analytics, SOC, SASE, among other technology topics

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Trends of Artificial Intelligence and Machine Learning in 2023 - CIO News

AI paraphrasing has become so popular nowadays that writers use them every day for improving their own write-ups.

It is no secret that content writing is immensely popular and that content writers have tight deadlines due to huge workloads.

They use content optimization tools to improve their own productivity and spend less time on editing and post-processing. One of the most used types of content optimization tools is paraphrasing tools.

Today, we are going to explore a little bit of what happens behind the scenes in an AI paraphrasing tool.

More specifically, we are going to see what machine learning technologies are applied and how they drive these paraphrasing tools.

We are going to check out what is the process that happens when an AI paraphraser receives some input.

We are also going to look at which kind of ML technology is used during each step of the process. So, lets start.

This is the first step in the paraphrasing process. The software/online tool has to detect the text provided to it and analyze it.

During this analysis, the individual words are recognized, and the meaning of phrases and sentences is extracted.

Depending on the tool being used, tone detection also occurs during this phase.

So, how does all of this happen? Well, in this phase a subfield of machine learning called Natural Language Processing (NLP) is used.

NLP basically combines linguistics, computer science, and artificial intelligence.

With NLP, computer systems are able to understand and interact with natural language in a way similar to humans.

Understanding text with NLP involves the following steps:

This is where it ends if the purpose is just understanding. There are more steps involved if the task requires paraphrasing. So, lets move on.

Now, paraphrasing text can be done in a fair few ways. But lets see what are the two basic ways in which paraphrasing is done with AI tools. There are two steps involved in that.

After understanding the text is over, an AI paraphraser will use machine learning to find out whether the important words and phrases have synonyms or not.

For that purpose, it will run those words/phrases through its own catalog of known words and pick out the ones that have the same meaning.

This is done via machine learning and more specifically it is a machine learning classification task. The tool classifies words according to their meaning. In machine learning, the system learns to find patterns among the given data.

Once it has learned to find these patterns, it can identify them in new and unknown datasets as well.

This is basically what happens during paraphrase identificationpatterns where words having similar meanings are identified.

Then these synonyms are used for paraphrasing the input sentences and changing them syntactically, but not semantically.

Example of a Paraphrasing Tool Using This Technique

You can find a lot of paraphrase tools online that utilize this technique. We will show you an example in which we utilize an AI paraphrasing tool. You can see it below.

In this example, we can see that the different words have been replaced with synonyms that have the same meaning.

Another thing that we can see is that the new words are bold. Clicking on the bold words opens a drop-down list that contains even more synonyms.

This is possible because this paraphrase tool utilizes a machine-learning classification technique.

In paraphrase generation, the classification approach is ditched in favor of the generation approach.

Basically, instead of finding words and phrases that have the same meaning and using them, it generates new sentences and phrases themselves.

There are multiple ways in which this can be done. A popular technique is to use a large language model (LLM) like GPT-4.

This is a pre-trained transformer that can create human-like text from prompts.

Naturally, it is very good at paraphrasing texts too. It is available as an API and many AI paraphrasers use it.

Other approaches that work are using syntactic trees, reinforcement learning, deep learning, and even the combination of several of these techniques.

These approaches are generally more time-consuming compared to using LLMs and pre-trained models.

Example of a Paraphrasing Tool Using This Technique

Nowadays you dont have to find and use GPT-4 raw, instead, you can simply utilize some tools that have GTP-4.

Fortunately, the tool we discussed in our previous example also utilizes GPT-4 in some of its modes. To see an example of this, check out the image below.

You can see that entire phrases are completely changed. Thats possible because of the generation of semantically identical text with the help of large language models.

So, these are some of the machine learning technologies and techniques that are used in AI paraphrasing. Since there are different technologies and not all tools use the same technologies, differences in paraphrasing arise.

Hopefully, this article helped you to understand a little bit more about machine learning technologies used in AI paraphrasing. If you want to learn more about AI, then you can head to our blog and find more information.

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Artificial Intelligence In Space: The Amazing Ways Machine Learning Is Helping To Unravel The Mysteries Of The Universe  Forbes

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Artificial Intelligence In Space: The Amazing Ways Machine Learning Is Helping To Unravel The Mysteries Of The Universe - Forbes

CFA Institute Launches Data Science for Investment Professionals Certificate

Certificate will allow participants to learn about the use of AI and machine learning in the investment process and develop in-demand skills for jobs at the intersection of data science and investment management

CFA Institute, the global association of investment professionals, has launched its Data Science for Investment Professionals Certificate designed to provide current or aspiring investment professionals with practical knowledge of the fundamentals of artificial intelligence and machine learning techniques and how they are used in the investment process.

The Data Science for Investment Professionals Certificate is suitable for individuals from a variety of backgrounds and requires no prior data science knowledge. Among those most likely to benefit are current or aspiring investment professionals in roles including, but not limited to, investment analyst, portfolio manager, relationship manager, and trader.

What does studying for the Certificate involve?

The Certificate comprises five interactive courses totalling approximately 100 hours, which participants can study in their own time, followed by a final 90-minute assessment. The content is hands-on application-oriented and includes instructional videos, coding labs, and case studies from industry practitioners.

Participants will learn how to:

The five courses are:

Richard Fernand, Head of Certificate Management at CFA Institute comments:

Data science is sweeping the investment industry, but currently only about one in four investment professionals interested in acquiring the necessary knowledge is actively doing so. As asset managers continue to adapt to the fast-changing dynamics of the AI, big data, and machine learning environment, everyone in an investment role will need to understand how they can utilize data science techniques.

The Data Science for Investment Professionals Certificate seeks to address this skills gap by providing a strong foundational learning and practical content for anyone working in any investment-related job. It equips learners with the knowledge to understand the application of data science in the investment process, as well as the language to be able to explain and translate machine learning concepts and their application to real-world investment problems. These skills will be key for professionals wishing to position themselves for the growing number of jobs found at the intersection of data science and investment management.

According to a CFA Institute report The Future of Work in Investment Management: The Future of Skills and Learning, almost two thirds (64 percent) of surveyed investment professionals report an interest in learning more about AI and machine learning. In the same survey, just three percent of investment professionals say they are already proficient in AI and machine learning concepts.

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Are you an avid online gamer? Do you find yourself craving a more immersive experience every time you jump into playing your favorite slot games or any game at that? If so, you may be interested to learn about how advances in AI and machine learning are transforming the gaming experience.

In this blog post, we will explore the ways that artificial intelligence and machine learning technologies are making online gaming smoother and more thrilling than ever before. Well look at how these technologies have been used to enhance graphics, user interfaces, and in-game dynamics all of which can drastically improve your gameplay.

Whether your favorite pastime is first-person shooters or real-time strategy games, lets delve into everything AI has to offer gamers!

As the online gaming industry continues to grow and evolve, AI and machine learning have become increasingly important tools for developers. These technologies can change the way we experience our favorite games, from providing more realistic and unpredictable opponents to personalized gameplay.

Through the use of AI and machine learning, game developers can analyze vast amounts of data, allowing them to create better-balanced and more engaging gaming experiences.

Additionally, these tools can help identify and prevent cheating, making online gaming fairer and more enjoyable for all. As the gaming industry moves forward, its clear that AI and machine learning will play an important role in shaping the future of the industry.

The world of online gaming is constantly evolving and with the introduction of AI and machine learning, it just keeps getting better. These technologies have revolutionized the gaming industry and brought about countless benefits for both players and developers.

AI algorithms help create more realistic gameplay and sophisticated opponents, while machine learning helps predict player behavior and preferences, leading to a more personalized gaming experience.

Additionally, AI can help game developers optimize their games for performance and eliminate bugs faster than ever before. In short, the benefits of using AI and machine learning in online gaming are diverse and far-reaching, making it an exciting area to watch for future developments.

Developing AI and machine learning technologies can be incredibly challenging for software developers. One of the biggest obstacles faced by developers is finding the right data to train their algorithms effectively.

In addition to this, there is also a lot of complexity involved in designing AI systems that can learn from data with minimal human intervention. Moreover, creating machine learning models that can accurately predict and analyze data in real time requires a sophisticated understanding of various statistical techniques and programming languages.

With these challenges in mind, its no wonder that many developers in this field feel overwhelmed. However, with the right tools and resources, developers can overcome these obstacles and continue advancing the exciting field of AI and machine learning.

The world of gaming has evolved significantly in recent years, and one major factor in this transformation is the integration of AI and machine learning into popular online games. From first-person shooters to strategy and adventure games, players have been enjoying a more immersive experience thanks to the inclusion of smarter, more complex non-player characters (NPCs) and advanced game optimization.

For example, in the game AI Dungeon, players can enter any storyline, and the AI generates a unique adventure based on their input. Similarly, the popular game League of Legends uses machine learning to optimize matchmaking, ensuring players are pitted against opponents of similar skill levels.

With AI and machine learning continually improving, the future of online gaming promises to be even more exciting and engrossing.

Artificial intelligence and machine learning have drastically transformed the gaming industry in recent years. These technologies can analyze vast amounts of data, predict outcomes, and make recommendations for players to improve their overall gameplay experience. AI can also assist developers in creating more immersive worlds, where virtual characters have reactive behaviors that mimic real-life behaviors.

Machine learning algorithms, on the other hand, can help determine a players skill level and preferences, adapting gameplay accordingly. Many gamers have already seen the benefits of these technologies, with smarter NPCs, more adaptive environments, and improved matchmaking systems.

As AI and machine learning continue to evolve, the gaming experience will only become more enhanced and personalized, creating an even more immersive world for players to explore.

AI and machine learning-based games have become increasingly popular in recent years, offering players a unique and immersive gaming experience. But how can you make the most of these cutting-edge titles?

Firstly, take the time to understand the game mechanics and the AIs decision-making process. This can help you anticipate actions and develop strategies to stay ahead of the curve. Additionally, be sure to give feedback to the developers, as this can help them improve the games machine-learning algorithms and provide a better experience for everyone.

Lastly, dont be afraid to experiment and try out different approaches to see what works best. With these tips, youll be well on your way to dominating the world of AI and machine learning-based gaming.

Online gaming experiences have been revolutionized by AI and Machine Learning technology. The ability to offer players intelligent, personalized gaming experiences that feel unique and engaging. Not only is this creating games that boost user retention, but it is also opening up exciting possibilities for multiplayer gaming.

Additionally, developers are increasingly leaning towards AI and ML to create more immersive worlds for gamers to explore. Despite challenges in implementation, the advancements of AI and Machine Learning are offering a wide range of captivating new experiences for online gamers from improved graphics to real-time learning obstacles making them an important component in crafting better gameplay experiences than ever before.

As players continue to enjoy the ever-evolving exciting world of online gaming, they must keep up with the latest trends related to AI and Machine Learning technology to make sure they are getting the most out of their experience.

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New Individualized PATE Versions Support the Training of Machine Learning Models with Individualized Privacy Guarantees  MarkTechPost

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Discover how AI and ML can potentially change the software development industry, and how AI affects software development and minimizes developers workload

Software development is a long, complex, and expensive process. Business owners and developers themselves constantly seek ways to optimize it. Good news for you, using artificial intelligence (AI) and machine learning (ML) is becoming increasingly popular in that regard.

According to a recent survey by Gartner, AI and ML are some of the trends that will shape the future of software development. For instance, early 73 percent of adopters of GitHub Copilot, an AI-driven assistant for engineers, reported that it helped them stay in the flow.

The use of this tool resulted in 87 percent of developers conserving mental energy while performing repetitive tasks. That increased their productivity and performance.

Twinslash and other software vendors and developers, on other hand, build AI-driven tools to help engineers with testing, debugging, code maintenance, and so on.

So: lets learn more about AI and ML and their impact on software development.

The ability to automate monotonous manual tasks is one of the significant benefits of AI. There are several ways to effectively implement AI in the development process that completely replace human intervention or, at least, reduce it enough to remove the tediousness of repetitive tasks and allow your engineers to focus on more critical issues.

One of the common applications of AI in development is utilizing it to reduce the number of errors in the code.

AI-powered tools can analyze historical data to identify recurring errors or faults, spot them, and either highlight them for developers to fix or fix them independently in the background. The latter option will reduce the need to roll back for fixes when something goes wrong during your software development process.

AI improves the quality, coverage, and efficiency of software testing. This is because it can analyze large amounts of data without making mistakes. Eggplant and Test Sigma are two well-known AI-assisted software testing tools.

They aid software testers in writing, conducting, and maintaining automated tests to reduce the number of errors and boost the quality of software code. AI in testing is extremely useful in large-scale projects usually combined with automated testing tools, it helps to check through multi-leveled, modular software faster.

ML software can track how a user interacts with a particular platform and process this data to pinpoint patterns that can be used by developers and UX/UI designers to generate a more dynamic, slick software experience.

AI can also help discover UI blocks or elements of UX people are struggling with, so designers and developers can reconfigure and fix them.

Code security is of utmost importance in software development. You can use AI to analyze data and create models to distinguish abnormal activity from ordinary behavior. This will help software development companies catch issues and threats before they can cause any problems.

Apart from that, tools like Snyk, integrated into engineers Integrated Development Environment (IDE) can help pinpoint security vulnerabilities in the apps before releasing them in production.

Lets talk about the main overall trends that are changing the field of software engineering and product development.

Generative AI is a powerful technology that uses AI algorithms to create any kind of data code, design layouts, images, audio or video files, text, and even entire applications. It studies datasets independently and can help produce a wide range of content.

One of the most significant benefits of generative AI is that it can help developers create software quickly and efficiently. For instance, it assists with:

Code completion. AI-enabled code completion tools in IDEs, such as Microsofts Visual Studio Code, can help developers write code faster. For VS, such a tool is called IntelliCode it analyzes a ton of GitHub repos and searches for code snippets that might be relevant for the developers next step and completes the lines for them.

Layout design. AI-powered design tools can analyze user behavior and preferences to generate optimized layouts for websites and mobile applications. For example, for some AI-powered plugins on the design platform, Canva uses machine learning algorithms to suggest layouts, fonts, and colors for marketing materials.

(Entire) app development. With generative AI, developers can automate the process of creating software or pieces of software by telling the AI the prompts for an app one wants to build. OpenAIs Codex can do that, using natural language processing models both for parsing through conversational language and syntax of a programming language.

Continuous delivery is a software development practice where code updates are automatically built, tested, and deployed to production environments. AI-powered continuous delivery can optimize this process by using machine learning algorithms to identify and address issues before they become critical.

Machine learning algorithms can analyze the performance of production environments and predict potential issues before they occur, reducing downtime and improving software reliability.

Apart from that, ML can parse through different deployment strategies and recommend the best approach based on past performance and current conditions of the system.

Now, that trend isnt directly tied to software development, but it impacts it quite significantly. Product and project managers can use AI tools to plan the project faster.

Of course, tools like ChatGPT wont replace the experience of talking to actual potential users, but it can still help them quickly get a grasp of the market situation, trends, or common concerns users have with the competitors product.

Tools like that one can also be utilized to conduct drafts for SWOT analysis, which is also extra vital for planning out the value proposition of the software and prioritizing features-to-be-built for a roadmap. Now, ChatGPT is also a generative AI, but we thought that its application deserves a separate section.

As Eric Schmidt, former CEO of Google, once said, I think theres going to be a huge revolution in software development with AI. That revolution is now. It is safe to say that the future of software development lies in AI and ML.

With the rise of AI-powered programming assistants and AI-enabled design work and security assessments, software development will become more cost-effective. Utilizing AI and ML in software development will also increase productivity, fasten time-to-market, and improve software quality.

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Manually evaluating transaction monitoring models is slow and error-prone, with mistakes resulting in potentially large fines. To avoid this, banks are increasingly turning to automated machine learning.

Regulators increasingly expect banks and financial institutions to be able to demonstrate the effectiveness of their transaction monitoring systems.

As part of this process, banks need to evaluate the models they use and verify (and document) that theyre up to the task. Institutions that fail to maintain a sufficiently effective anti-money laundering program arefrequently hit with huge fines, including several that have totaled over USD1 billion.

Lisa Monaco, the deputy attorney general at the US Department of Justice (DoJ) while announcing arecent fine for Danske Bank, said to expect companies to invest in robust compliance programs. Failure to do so may well be a one-way ticket to a multi-billion-dollar guilty plea.

Such threats are putting added pressure on smaller banks and FIs. While the larger institutions often will struggle less because of their army of data scientists, model validation and evaluation can be a burden for players with more limited resources.

What is a model?

In the US, banks commonly monitor transactions using a rule-based system of parameters and thresholds. Common rules detect the value of transactions over a period of time or an increase in the volume or value of transactions. If sufficient conditions are met, an alert is triggered.

Even in their simplest incarnation, regulators consider such systems to be models. According to supervisory guidanceOCC 2011-12, a model is defined as any quantitative approach that processes inputs and produces reports. In practice, a typical rule-based transaction monitoring system involves multiple layers of rules.

Regardless of complexity, banks must manage model risks appropriately. There are three main types of model risk that banks need to consider:

These are easy questions to ask, but answering them can be extremely challenging. The OCC supervisory guidance stipulates that banks should manage model risks just like any other type of risk, which includes critical analysis by objective, informed parties who can identify model limitations and assumptions and produce appropriate change.

This guidance states that banks should ensure their models are performing as expected, in line with their design objectives and business uses. It defines the key elements of an effective validation framework as:

Regulatory compliance

Regulators have continued to raise the bar as the US seeks to restrict access to sanctioned countries and individuals, as well as cracking down on financial crime in general.

Since 2018, the New York State Department of Financial Services has required boards or senior officers to submitan annual compliance finding that certifies the effectiveness of an institutions transaction monitoring and sanctions filtering programs.

Taking this a step further, the DoJ announced in 2022 that it was considering a requirement for chief executives and chief compliance officers to certify the design and implementation of their compliance program. With continued geopolitical tensions as the war in Ukraine drags on, the potential cost of a compliance failure is only going to increase.

The regulation of models comes under these broad requirements for effective risk controls. While the approach that banks take to evaluate models will vary on a case-by-case basis, the general principles apply equally.

Similarly, the frequency of model evaluation should be determined using a risk-based approach, typically prompted by any significant changes to the institutions risk profile, such as a merger or acquisition, or expansion into new products, services, customer types or geographic areas. However, regulators increasingly expect models to be evaluated as often as every 12-18 months.

Model evaluation challenges

Rule-based models are being asked to do much more as the nature and volume of financial transactions has evolved. As new threats have emerged, models have become more and more complex (though not more effective). Unfortunately, many are not up to the task.

In many cases, the model has become a confusing black box that few people within the institution understand. Over the years, changes to data feeds, scenario logic, system functions, and staffing can mean that documentation explaining how the model works is incomplete or inaccurate. All of this can make evaluation very difficult for smaller banks. A first-time assessment will almost certainly be time-consuming and costly, and possibly flawed.

However, the challenges are not going away. Changes in consumer behavior, which accelerated during the pandemic, are here to stay. Banks and FIs have digitized their operations, vastly increasing their range of online services and payment methods. Consumers are also showing greater willingness to switch to challenger banks with digital-first business models.

These changes have created more vulnerabilities. Competitive pressures are putting compliance budgets under pressure, while the expansion of online services has created more opportunities for AML failures. To keep up, FIs need to respond quickly and flexibly to new threats.

Better model evaluation with Automated Machine Learning

This process of model evaluation can be optimized using automated machine learning (AutoML). This allows models to be evaluated continuously (or on short cycles) with a standardized process, which leads to higher quality evaluations. By contrast, the manual approach is slow and very error prone.

AutoML models take huge sets of data, learn from the behaviors encoded in that data and reveal patterns that indicate evidence of money laundering. The rapidly changing landscape of AML regulations, in combination with the growing number of transactions and customers, leaves almost no room for a traditional manual project-by-project approach. That is why the industry is increasingly looking at a more disruptive approach:models that are trained with customers' good behavior. The results of this non-traditional method in combination with AutoML let banks adaptto the new reality and stay ahead of almost any new criminal pattern.

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Securing weak spots in AML: Optimizing Model Evaluation with ... - Finextra

PRESS RELEASE

Published April 27, 2023

The recent analysis by Quadintel on the India Machine Learning Market Report 2023 revolves around various aspects of the market, including characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends, strategies, etc. It also includes COVID-19 Outbreak Impact, accompanied by traces of the historic events. The study highlights the list of projected opportunities, sales and revenue on the basis of region and segments. Apart from that, it also documents other topics such as manufacturing cost analysis, Industrial Chain, etc. For better demonstration, it throws light on the precisely obtained data with the thoroughly crafted graphs, tables, Bar & Pie Charts, etc.

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Machine learning (ML) is an emerging technology in India that applies artificial intelligence (AI) to develop systems capable of learning and improving their performance without explicit programming.The retail, transportation, and financial services industries are among the sectors that have adopted ML, and there is a rise in demand for skilled professionals in ML across industries.

The AI market in India was valued at INR 472.73 Billion in 2020 and is expected to reach INR 2113.60 Billion by 2027, while the global machine learning market was valued at INR 839.55 Billion in 2020 and is anticipated to reach INR 7632.45 Billion by 2027, expanding at a CAGR of ~37.16% during the 2021-2027 period. AI adoption has become significant in various corporations, with employees from non-technological backgrounds incorporating AI processes into their functional roles.

The COVID-19 pandemic has impacted businesses, economies, and management strategies employed by corporations. Businesses are facing challenges in meeting customer expectations regarding process optimization and increased security concerns due to connectivity issues.

The demand for cloud-based collaboration tools, content management solutions, and online streaming platforms has picked up. All organizations use analytics to improve decision-making and automate processes for increased productivity and cost-effectiveness. New entrants use machine learning for a variety of activities, such as designing games, translating languages, predicting future market trends, composing music, and diagnosing diseases.

However, customers often show concerns about sharing information since their sensitive data may get leaked, resulting in difficulty in implementing cloud-based ML applications for most entrepreneurs. The IT industry infrastructure in third-world countries is not developed enough to enhance cloud-based business activities. System defects in data flow occur when system requirements are omitted or not fully met due to human error intervention in the development, testing, or verification processes.

Download Free Sample Copy of India Machine Learning Market Report @-https://www.quadintel.com/request-sample/india-machine-learning-market/QI042

Our tailormade report can help companies and investors make efficient strategic moves by exploring the crucial information on market size, business trends, industry structure, market share, and market predictions.

Apart from the general projections, our report outstands as it includes thoroughly studied variables, such as the COVID-19 containment status, the recovery of the end-use market, and the recovery timeline for 2020/ 2021

Analysis on COVID-19 Outbreak Impact Include:In light of COVID-19, the report includes a range of factors that impacted the market. It also discusses the trends. Based on the upstream and downstream markets, the report precisely covers all factors, including an analysis of the supply chain, consumer behavior, demand, etc. Our report also describes how vigorously COVID-19 has affected diverse regions and significant nations.

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Each report by the Quadintel contains more than 100+ pages, specifically crafted with precise tables, charts, and engaging narrative: The tailor-made reports deliver vast information on the market with high accuracy. The report encompasses: Micro and macro analysis, Competitive landscape, Regional dynamics, Operational landscape, Legal Set-up, and Regulatory frameworks, Market Sizing and Structuring, Profitability and Cost analysis, Demographic profiling and Addressable market, Existing marketing strategies in the market, Segmentation analysis of Market, Best practice, GAP analysis, Leading market players, Benchmarking, Future market trends and opportunities.

Geographical Breakdown:The regional section of the report analyses the market on the basis of region and national breakdowns, which includes size estimations, and accurate data on previous and future growth. It also mentions the effects and the estimated course of Covid-19 recovery for all geographical areas. The report gives the outlook of the emerging market trends and the factors driving the growth of the dominating region to give readers an outlook of prevailing trends and help in decision making.

Nations:Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, New Zealand, Nigeria, Norway, Peru, Philippines, Poland, Portugal, Romania, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, UAE, UK, USA, Venezuela, Vietnam

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Thoroughly Described Qualitative COVID 19 Outbreak Impact Include Identification and Investigation on:Market Structure, Growth Drivers, Restraints and Challenges, Emerging Product Trends & Market Opportunities, Porters Fiver Forces. The report also inspects the financial standing of the leading companies, which includes gross profit, revenue generation, sales volume, sales revenue, manufacturing cost, individual growth rate, and other financial ratios. The report basically gives information about the Market trends, growth factors, limitations, opportunities, challenges, future forecasts, and information on the prominent and other key market players.

Key questions answered:This study documents the affect ofCOVID 19 Outbreak: Our professionally crafted report contains precise responses and pinpoints the excellent opportunities for investors to make new investments. It also suggests superior market plan trajectories along with a comprehensive analysis of current market infrastructures, prevailing challenges, opportunities, etc. To help companies design their superior strategies, this report mentions information about end-consumer target groups and their potential operational volumes, along with the potential regions and segments to target and the benefits and limitations of contributing to the market. Any markets robust growth is derived by its driving forces, challenges, key suppliers, key industry trends, etc., which is thoroughly covered in our report. Apart from that, the accuracy of the data can be specified by the effective SWOT analysis incorporated in the study.

A section of the report is dedicated to the details related to import and export, key players, production, and revenue, on the basis of the regional markets. The report is wrapped with information about key manufacturers, key market segments, the scope of products, years considered, and study objectives.

It also guides readers through segmentation analysis based on product type, application, end-users, etc. Apart from that, the study encompasses a SWOT analysis of each player along with their product offerings, production, value, capacity, etc.

List of Factors Covered in the Report are:Major Strategic Developments: The report abides by quality and quantity. It covers the major strategic market developments, including R&D, M&A, agreements, new products launch, collaborations, partnerships, joint ventures, and geographical expansion, accompanied by a list of the prominent industry players thriving in the market on a national and international level.

Key Market Features:Major subjects like revenue, capacity, price, rate, production rate, gross production, capacity utilization, consumption, cost, CAGR, import/export, supply/demand, market share, and gross margin are all assessed in the research and mentioned in the study. It also documents a thorough analysis of the most important market factors and their most recent developments, combined with the pertinent market segments and sub-segments.

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List of Highlights & ApproachThe report is made using a variety of efficient analytical methodologies that offers readers an in-depth research and evaluation on the leading market players and comprehensive insight on what place they are holding within the industry. Analytical techniques, such as Porters five forces analysis, feasibility studies, SWOT analyses, and ROI analyses, are put to use to examine the development of the major market players.

Points Covered in India Machine Learning Market Report:

India Machine Learning Market Research Report

Section 1: India Machine Learning Market Industry Overview

Section 2: Economic Impact on India Machine Learning

Section 3: Market Competition by Industry Producers

Section 4: Productions, Revenue (Value), according to regions

Section 5: Supplies (Production), Consumption, Export, Import, geographically

Section 6: Productions, Revenue (Value), Price Trend, Product Type

Section 7: Market Analysis, on the basis of Application

Section 8: India Machine Learning Market Pricing Analysis

Section 9: Market Chain, Sourcing Strategy, and Downstream Buyers

Section 10: Strategies and key policies by Distributors/Suppliers/Traders

Section 11: Key Marketing Strategy Analysis, by Market Vendors

Section 12: Market Effect Factors Analysis

Section 13: India Machine Learning Market Forecast

..and view more in complete table of Contents

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India Machine Learning Market The Impact of Industry Chain ... - Digital Journal