For years, the answer was by hard graft, with rangers checking burrows and nests for birds and eggs, and observers forced to sit for hours at a time armed with clipboards and no little patience.

But in a marriage of nature and cutting edge technology, the arduous task of establishing the puffin population on the Isle of May is being carried out using artificial intelligence, machine learning, and image recognition software.

Those behind the project believe it could help minimise disruption to birds breeding and feeding habits, particularly when faced with developments such as offshore windfarms.

The initiative uses four cameras placed in stainless steel boxes at various points of the island in the Firth of Forth in order to capture live footage of the puffins. Each box has a condensation heater as well as a backup power supply.

The footage is then stored and processed using an artificial intelligence program which is capable of spotting the puffins and tracking them frame by frame. Each bird is assigned a unique identifier, allowing the software to follow its movements and establish the overall number of puffins.

The scheme is the brainchild of SSE Renewables, which wants to find out if its Beatrice windfarm, situated eight miles off the coast of Wick, is impacting on the flight paths of the birds as they travel to gather food to take back to their burrows.

It teamed up with the tech giant, Microsoft, and Avanade, a US-based artificial intelligence specialist, to roll out the tracking program, which monitored the birds as they landed to breed in late March and early April, before returning to sea in August.

With around 80,000 puffins recorded on the island in March last year, the data is currently being analysed, but there are hopes the approach could be replicated in order to monitor the habitats of other species.

Simon Turner, chief technology officer of data and AI at Avanade, said the use of technology made the counting process more efficient and less invasive.

Using cameras and AI, we are now able to count the number of birds and monitor their burrows all day, every day, without going near them, he said.

The AI will draw a box around each puffin it spots and give them unique tags like 001, 002, or 003. When the camera moves to the next frame, it understands that the puffin closest to a particular box is the same one.

James Scobie, SSE Renewables digital delivery lead for the project, said: Were still looking at the data but through the trial, we have already made some interesting findings.

The land was barren when we first visited the island in February. However, as spring progressed into summer, the array of flowers that had grown tricked the AI. We learned that it would be important to have seasonal training of the AI to maintain the level of accuracy we expect.

We found that on average, the highest level of puffin activity in a colony is observed at dawn and at dusk, but this is dependent on tidal times and fishing conditions. Adult puffins will not turn down a good opportunity to catch food for their young.

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Artificial intelligence used to count tens of thousands of puffins - The Scotsman

Nephila clavata, a golden orb weaver. Image by Kinori via Wikimedia / Public Domain

Despite years of in-depth study there is much still to learn about a spiders web, from the intricate patterns to the tensile strength and optoelectronic architectures. Webs are highly-complex structures, as with spider webs actively springing towards prey as the result of electrically-conductive glue spread across their surface.

Webs also contain multiple silk types, with viscid silk (stretchy, wet and sticky) and dragline silk (stiff and dry) being responsible for the strength of the web.

In a newly reported research topic, scientists from Johns Hopkins University have discovered how spiders build webs. This has been revealed through a combination of night vision and artificial intelligence.

Night vision recording enabled the researchers to track and record each movement of spiders working in the dark. This helped to develop an algorithm that has led to an understanding of how spiders are able to create webs structures of elegance, complexity and geometric precision.

The study reveals that the basis of choreographed web building is from the sense of touch by the spider (vision is not a major feature of the nocturnal process). This requires innate behaviors and finely tuned motor skills.

The first wave of the study involved assessing six spiders. For this, millions of individual leg actions were captured and then assessed using machine vision software, especially designed specifically to detect each individual limb movement.

From subsequent examination of different spiders, it became apparent that web-making behaviors are remarkably similar across spiders. The assessment of the patterns using AI enabled the researchers to predict the part of a web a spider was working on just from seeing the position of a leg. This led to the scientists to propose that a rule-based system for web building was at play, even though the individual webs from different species of spider differed.

This led to the research conclusion that the rules of web-building are encoded in the brains of all species of spider.

The research has led to a web-building playbook that brings new understanding of how web building, across the period of many hours, occurs. The researchers have produced a video that explains more aspects of the research.

Moving beyond the first phase of the research, the scientists intend to conduct experiments using mind-altering drugs on different spiders. The aim here is to determine the specific circuits in the spiders brain that are responsible for the various stages of web-building.

The research is presented in the journal Current Biology, with the paper titled Distinct movement patterns generate stages of spider web building.

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Artificial intelligence reveals the secrets of the spider web - Digital Journal

Last week, the Dutch Scientific Council for Government Policy (WRR) found that the Netherlands is not well prepared for the consequences of artificial intelligence (AI). In Challenge AI, The New Systems Technology (in Dutch), the council calls for regulation of technology and data, its use, and social implications. And rightly so. Machines will have more computing power than humans in a few decades. If devices with artificial intelligence then start to think and decide for themselves, it is to be hoped that they will observe a number of commandments.

AI is also entering mobility, and the problems the WRR refers to are also at play there. The most imaginative AI appearance in mobility is the autonomous car. It is potentially much safer and more comfortable, but there are tricky liability issues if an accident occurs. Should you as a human always be able to override the system? And what would it take for a self-driving car to interpret the law flexibly when necessary? This is something we, as humans, do every minute in daily traffic, precisely in the service of safety.

One day, when I was driving along with traffic at 120 km/h on the E25 through the Ardennes, my automatic cruise control suddenly lowered the speed limit to 70 km/h because the road workers had forgotten to remove a speed sign. Fortunately, I was able to override that and not adhere to that officially legal speed limit. Despite this example, however, in the future, we should not start allowing extremely smart machines to be flexible with the rules, just like us, without any ethical or moral framework. That could lead to dystopian states where machines, perhaps unintentionally, start endangering humanity.

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But the AI issues in mobility go far beyond the self-driving car. What if Google or TomTom takes over traffic management from the road authority? What if the big tech giants take over the entire planning of public transport once people plan their journeys solely through their services? What if those platforms, after a friendly free initial period, start abusing their achieved monopolies? Who will guarantee availability and safety? Cab services like Uber are more popular than the classic taxi, but who can oblige them, as with regulated cab transport, to also accept guide dogs and wheelchairs, for example, so that a significant part of society is not left aside?

Artificial intelligence will make mobility better, safer, and more comfortable. But these systems need ethical and moral frameworks within which they can achieve this. In the Netherlands, companies, and knowledge institutions have already united in the Dutch AI Coalition. They received 276 million from the growth fund earlier this year to strengthen the Dutch position internationally. Wisely, the first part of that goes to so-called Elsa labs: Ethical, Legal & Societal aspects of AI, in which consortia focus on these aspects. Just as in mobility, AI will help steer other areas as well, but we still want to be able to take the wheel ourselves.

Maarten Steinbuch and Carlo van de Weijer are alternately writingthis weekly column, originally published (in Dutch)in FD. Did you like it? Theres more to enjoy: a book with a selection of these columns has just been published by24U and distributed byLecturis.

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Artificial intelligence and mobility, who's at the wheel? - Innovation Origins

December 7th, 2021

2:00 PM - 3:00 PM EDT

*Eligible for HRCI and SHRM recertification credits

With the expansion of Talent Acquisition responsibilities and complex landscape from hiring recovery, talent redeployment, the great resignation, and DE&I initiatives, there has never been a greater need for intelligent, augmentation and automation solutions for recruiters, managers, and sourcers. There is also growing awareness of problematic artificial intelligence solutions being used across the HR space and the perils of efficiency and effectiveness solutions at the cost of fairness and diversity goals. These concerns are compounded with increased inquiries from employees and candidates of the AI solutions used to determine or influence their careers, particularly whats inside the AI and how they are tested for bias. Join this one-hour webinar hosted by HiredScore CEO & Founder Athena Karp as she shares:

Speakers

Athena Karp

CEO & Founder @HiredScore

Athena Karp is the founder and CEO of HiredScore, an artificial intelligence HR technology company that powers the global Fortune 500. HiredScore leverages the power of data science and machine learning to help companies reach diversity and inclusion goals, adapt for the future of work, provide talent mobility and opportunity, and HR efficiencies. HiredScore has won best-in-class industry recognition and honors for delivering business value, accelerating HR transformations, and leading innovation around bias mitigation and ethical AI.

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[Webinar] Balancing Compliance with AI Solutions - How Artificial Intelligence Can Drive the Future of Work by Enabling Fair, Efficient, and Auditable...

On the evening of Oct. 31, 25-year-old Fukuoka native Kyota Hattori wearing makeup and a purple and green ensemble to emulate the villainous Joker of Batman franchise fame boarded a Keio Line train at Keio-Hachioji Station, heading for central Tokyo. After spending half an hour meandering around Shibuya, which was packed with costumed revelers feting Halloween, Hattori headed back toward Hachioji, but reversed direction again at Chofu, where he changed to a Shinjuku-bound limited express train.

Soon after the doors closed, according to eye witness reports, he removed a survival knife and liquids from a backpack. When a 72-year-old male passenger tried to intervene, Hattori allegedly stabbed the man and proceeded to pursue fleeing passengers, splashing them with lighter fluid, which he then ignited. The stabbing victim was hospitalized in a critical condition and 16 other passengers suffered burns and smoke inhalation.

Videos captured on smartphones showed desperate passengers struggling to squeeze out the trains partially opened windows onto the platform of Kokuryo Station.

Id failed at work, my friendships didnt work out and I wanted to die, Shukan Jitsuwa (Nov. 25) reported Hattori as having told police. Since I couldnt die on my own, I wanted to carry out a mass murder on Halloween and get the death penalty.

He had only a few thousand yen on his person at the time of his arrest, after reportedly telling his interrogators he had spent about 200,000 on his Joker costume.

Yukan Fuji (Nov. 12) categorized Hattoris act as essentially a copycat crime, inspired by a similar rampage that occurred in August on an Odakyu Line train.

Watching the news reports of other incidents may have created a sense of sympathy for the criminal, wanting to create a commotion themselves, or perhaps from a sense of frustration that they have beaten him to it, explained Yasuyuki Deguchi, a professor of criminal psychology at Tokyo Future University. Many people are usually not good at taking action on their own, and dont consider the risk and cost of crime.

One reason trains are being singled out for such acts is that they are moving enclosed spaces, so if the driver and conductor arent informed via emergency intercom that something has occurred, they cant take action to halt the train and permit passengers to evacuate.

Then what can rail companies do, proactively, to protect their passengers?

Videos captured by smartphones show desperate passengers struggling to squeeze out the trains partially opened windows onto the platform of Kokuryo Station during the Oct. 31 attack. | KYODO

Toyo University criminologist Masayuki Kiriu told Aera (Nov. 15) that if the rail companies were to broadcast announcements such as Lets be cautious so as not to be confronted by crime over the trains public address systems, it may deter potential criminals.

Increased passenger alertness is also desirable. Kiriu was critical of peoples habitual gazing at their smartphones, which distracts them from awareness of their surroundings.

How about rail companies appealing to passengers directly, using visual images or messages? he suggested. I think it might prove beneficial as symptomatic treatment (i.e., therapy that eases the symptoms without addressing the basic cause of a condition).

In the most extreme cases, however, passengers may be forced to defend themselves. Takeshi Nishio, chief instructor in the Israeli unarmed combat skill of Krav Maga at the MagaGYMs in Akasaka and Roppongi, pointed out that even the tip of a closed umbrella can be aimed at an assailants throat or eye, or used to strike the wrist of a person brandishing a knife. Flinging keys or a smartphone into an attackers face can also be effective.

Nikkan Gendai (Nov. 18) believes the day may be approaching when artificial intelligence can be harnessed to protect the public from random assailants.

A Tokyo-based company named Earth Eyes already markets systems aimed at shoplifters. NEC Corp.s facial recognition technology, in addition to identifying wanted criminals from a database, can be tweaked to spot other suspicious behavior patterns, such as loitering, particularly for long periods, shuffling through a crowd or standing in place. Likewise NTT Docomo has been collaborating with Fujitsu to develop a security system.

Other suspicious behavior might include carrying a bag or shouldering a backpack (which might be used to carry weapons), wearing a face mask (obviously negated during the current pandemic) or headwear, and running shoes (Theyre preferred over street shoes or sandals if somebody might need to make a quick getaway).

Its hypothetical of course, but had an AI system been in place, the Kyoto Animation arson attack in July 2019 the largest mass murder in Japans modern history, with 36 people killed and another 34 injured might have been thwarted. Perpetrators are known to carefully determine the time and place of the crime beforehand, so observing passers-by might have prevented the crime. (The attacker had been seen prowling near the building several days before the incident, walking between parked cars for no apparent reason.)

While most news outlets expressed outrage, a few articles balanced their reports with a degree of empathy toward Hattori, describing him as a himote otoko (an unpopular man) a term that has parallels in the incels (involuntary celibate males) who inhabit the web.

In a story titled The criminal on the Keio Line attack was a monster created by a disparate society, Jitsuwa Bunka Taboo (January) identified such random attackers as single males, many coming from impoverished families, who work at irregular jobs in the service industry, typically at wages so low that even if they live with their families they are unable to save money. Others, once being rejected by a girlfriend, never recover their mental equilibrium.

In such a situation, Jitsuwa Bunka Taboos writer concludes, we dont know when another Hattori-like character will attempt to kill again. We need to build a society in which even losers and unpopular people can live happily in their own way, as opposed to one where the winners get everything. That will be the only way to ensure we can live in safety.

Big in Japan is a weekly column that focuses on issues being discussed by domestic media organizations.

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Can artificial intelligence be harnessed to protect the public from random assailants? - The Japan Times

Artificial intelligence, or AI, has created a lot of buzz, and rightfully so. Anyone remember Skynet? If so drop a comment. Ok, back to our regular programming. Military AI is no different. From self-driving vehicles to drone swarms, military AI will be used to increase the speed of operations and combat effectiveness. Lets look at the future of military AI including some ethical implications.

Military AI is a topic thats been around for a while. Those who know anything about military AI know that it has been around for years, just not talked about for reasons you can imagine. And it has been evolving.

These days Military AI has been helping with complex tasks such as target analysis and surveillance in combat.

Another great use for military AI in the future is to have it work with combat warfighters. AI could possibly be used for a tactical advantage because it would be able to predict an enemys next move before it happens. However, a good question to ask ourselves is, can Chinas AI outperform ours? Based on recent hacks by China on U.S. infrastructure this seems like a concern we should take very seriously.

Artificial intelligence (AI) is any machine or computer-generated intelligence that is intended to emulate the natural intelligence of humans. AI is generated by machines, but its avenues of application are limitless, and its no surprise that the military has taken an interest in this technology.

AI can be used to identify targets on the battlefield. Instead of relying on human intelligence, drones will be able to scan the battlefield and identify targets on their own.

This will help to reduce the number of warfighters on the battlefield, which will in turn save thousands of lives.

The future of Military AI is bright until it isnt. Lets be real, weve all seen the Terminator movies.

Military AI has the potential to increase combat effectiveness and reduce the workforce. It will be used to autonomously pilot vehicles, respond to threats in the air, and conduct reconnaissance and guide smart weapon systems. It will help with strategic planning and even provide assistance during ground combat.

Imagine for a second, the AI version of the disgruntled E-4!

AI is not only beneficial to military operations, it will also help with those boring jobs in logistics and supply chains. It can be used to predict demand for supplies and the most efficient routes for transport.

While there are many benefits of military AI, there are also potential risks. Some of these risks include military AI being hacked, weaponized, or misused in ways not intended by its creators. China or Putins Russia anyone?

Read Next: The Skyborg Program: The Air Forces new plan to give fighter pilots drone sidekicks

The future of Military AI is kind of fuzzy which could be good or bad.

In the near future, AI will be a part of military operations. It will be used in the field for combat and reconnaissance. In fact, AI-powered drones have been used in both battlefields and disaster zones, from Afghanistan to the Fukushima Nuclear Plant.

AI will be used in a variety of ways. It will be a part of combat operations, reconnaissance, and training. For example, AI can be used to build a 3D map of a combat zone. This would allow military personnel to plan their operations based on this map.

Another example of how AI can be used is in the training of new recruits. The military could use AI to simulate possible combat scenarios and determine which recruits are most likely to succeed in these scenarios. In this way, the military could train recruits using AI before deploying them to a combat zone, and we think thats pretty cool.

Perhaps surprisingly, AI may also be used in negotiation scenarios. Military negotiators could use AI to predict and prepare for negotiation outcomes and then use that data to plan their next steps in negotiations, such as predicting what response an opponent might have.

These are just some of the examples of how AI will be used in military operations in the future.

It is no secret that current warfare needs to be reconsidered. What weve done in the past isnt working. Afghanistan anyone? Bueller? With the emergence of new technologies, what we know about warfare needs to be reconsidered as well.

The U.S. Department of Defense has announced a major initiative to invest in artificial intelligence for a range of military operations from predicting the weather to detecting and tracking enemies.

It will have a huge impact on both the speed and combat effectiveness of operations, as well as the ethical implications of what we leave behind for future generations. Military AI is not an issue that will go away anytime soon. And as it becomes more prevalent, it will create a future that is quite different from what we know now.

AI will open up many possibilities for military operations in the future. For example, AI has the potential to take on tasks that are not human-safe. AI will be able to analyze data at a faster rate than humans, which will provide a tactical advantage.

If autonomous tanks are also developed, they could easily take over for soldiers on the ground in the same way that drones have taken over for pilots in the air.

AI can also be used to better coordinate drone swarms. The use of drones in the military has become more popular, and these robots can be used to take on many different tasks. For example, swarms of drones could be used to both attack and defend.

However, these advancements come with ethical implications. For example, autonomous weapons could potentially kill without human input. They could be used indiscriminately and quite possibly create more civilian casualties than conventional weapons.

So, what does this all mean? The future of military AI is unclear and may be full of ethical dilemmas. However, it seems like AI is here to stay and will continue to provide both benefits and hindrances.

The future of military AI is now. We are already seeing the effects of military AI in operations today. For example, Lockheed Martins Aegis system can control multiple air defense systems simultaneously. This means that the Aegis system can monitor more than 100 targets at one time.

However, AI will have a much more significant impact on the military in the near future. AI will have a profound effect on combat operations, logistics, and training. Combat operations will be faster and more precise because AI can handle complex tasks more quickly than humans. Logistics will be more efficient because AI systems will be able to better coordinate the transport of supplies. And training will be more effective because AI can provide personalized instruction to soldiers.

But it may not be too long before we see autonomous killing machines. Russian President Vladimir Putin has indicated an interest in developing robot fighting machines with artificial intelligence. Remember our previous Terminator comment? And other countries are developing autonomous lethal machines, too. Their names rhyme with Russia, and China

There are many ethical implications regarding the use of military AI. For example, there is the risk of AI taking control of military assets, like drones. If one AI-controlled drone gets hacked, it could cause mass destruction.

Another ethical issue is the use of autonomous weapons systems. Many people argue that these systems are immoral because they dont give soldiers the chance to defend themselves.

The use of AI in military operations will continue to grow in the coming years. Its important to keep in mind the ethical implications that come with this growth.

Technology always has a way of evolving and improving. Thats one of its best features. But not all innovation is good.

This means that AI will be used to fight wars, which is a cause for concern.

In the past, humans have had to make difficult decisions in times of war. But with AI, that decision could be made without the input of a human moral compass.

Thats why theres debate over whether or not there should be limits on what can be done with military AI. It usually comes down to two camps Elon Musks camp of, AI will destroy us. Then the more optimistic camp of Tony Robbins, AI will save us from ourselves.

An increasing number of people believe that AI should be regulated (Elon is one, and I tend to agree with him) and that there should be a ban on autonomous weapons. These arguments center on the idea that without a human in the decision-making process, there is no accountability. In fact, rewind that theres often no accountability within the current government. Afghanistan pullout anyone?

In light of these controversies, what does the future hold?

The future of military AI is unclear, but it will be a major force in future wars. There are ethical implications that we need to think about and try to regulate now before Skynet takes over and makes slaves of us all.

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The Future of Artificial Intelligence Autonomous Killing Machines: What You Need to Know About Military AI - SOFREP

I dont see tech or AI as hostile to women. There are many successful women in AI both at the academic as well as industry levels, says Ramya Joseph, the founder of AI-based entrepreneurial start-up Pefin, the worlds first AI financial advisor. And even on my team at Pefin, women hold senior technology positions. There tends to be a misconception that tech tends to attract a geeky or techy kind of personality, which is not the case at all,

Joseph has a bachelors degree in computer science and masters in Artificial Intelligence, Machine Learning and Financial Engineering. As a wife, mother and daughter, Joseph could closely relate to the crisis of financial advice to plan for the future. She came up with the idea of founding Pefin when her father lost his job due to a lack of financial advice when he jeopardised his retirement plans. Navigating and solving his problems, Joseph realised that many were telling the same problem. Hence she came up with the idea of an AI-driven financial adviser.

No doubt Artificial Intelligence is one of the growing industries in the field of professionalism. As new inventions and developments knock at our doors, the relation between humans and computers is being reassessed. With the expansion of AI, new skills and exceptional human labour is in high demand. But the problem is that despite the evolution in society, the gender pay gap is not shrinking. As per the wef forum, only 22 per cent of AI professionals are women. The report suggests that there is a gender gap of around 72 per cent.

Despite this, many women are breaking the glass ceilings and reforming the field of Artificial Intelligence. Through their skills and leadership, these women are carving the path for other women to participate as AI professionals. So in this article, I am going to list out some women AI professionals in India who changing the gender dynamics through their excellence.

Amarjeet Kaur is a research scientist at TechMahindra. She has a PhD in Computer Science and Technology. Kaur specialises in research techniques and technologies like graph-based text analysis, latent semantic analysis and concept maps among others. She also has expertise in experimentation and field research, data collection and analysis and project management. She is known for her organisational skills and willingness to take charge.

Kaur has also worked with the Department of Science and Technology at Women Scientist Scheme. As a part of the scheme, she helped in developing a technique to automatically evaluate long descriptive answers. With more than ten years of research and teaching experience, Kaur has excellent academic skills. Her academic skills and innovative techniques have gained her a gold medal and a toppers position at Mumbai University. Her innovative skills and course material has also received a place in Mumbai Universitys artificial intelligence and machine learning courses.

Sanghamitra Bandyopadhyay works at the Machine Intelligence Unit of the Indian Statistical Institute. She also completed her PhD from the institute and became its director serving for the years 2015 to 2020. Bandyopadhyaya is also a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC). She specialises in fields like machine learning, bioinformatics, data mining and soft and evolutionary computation.

She has been felicitated with several awards for her work like Bhatnagar Prize, Infosys award, TWAS Prize, DBT National Women Bioscientist Award (Young) and more. She has written around 300 research papers and has edited three books.

Ashwini Ashokan is the founder of MadStreetDen, an artificial intelligence company that uses image recognising platforms to power retail, education, health, media and more. Starting up in 2014, the venture is headquartered in California with offices access Chennai, Bangalore, Tokyo, London and more. She co-founded the platform along with her husband. Speaking to SheThePeople, Ashokan said, Its only natural that the AI we build mimics what weve fed it, until the agency of its own, which could be good or bad. As an industry, we need to think about what were teaching our AI, She also added, Every line of code we write, every feature we put in products we need to ask ourselves, what effect does this have on the way the world will be interacting with it.

Apurva Madiraju is a vice president at Swiss Re Global Business Solutions India in Bangalore. She is leading the data analytics and data science team of the audit function. As the leader, she is responsible for building machine learning and text analytics solution to deal with audit compliance risk.

Madiraju flaunts 11 years of experience across diverse fields like artificial intelligence, data science, machine learning and data engineering. She has developed multiple AI and ML-driven solutions like ticket volume forecasting models, turn-around-time prediction solutions and more. She has worked across companies globally to lead the conceptualisation, development and deployment of many AI and ML-based solutions for enterprises.

With more than 20 years of experience as a Data Scientist, Bindu Narayan serves as the Senior Manager with Advanced Analytics and AI at EY GDS Data and Analytics Practice. At EY, Narayan is AI competency leader for EYs Global Delivery Services. She along with her team offers virtual assistant solutions to clients across the industry. Moreover, with her skills, Narayan has developed many innovative AI solutions and leads in the field of machine learning, customer and marketing analytics and predictive modelling. She completed her PhD from IIT Madras on the topic of modelling Customer Satisfaction and Loyalty.

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Who Says AI Is Not For Women? Here Are 6 Women Leading AI Field In India - SheThePeople

Artificial intelligence had an eventful decade so far. With 2021 bringing more into play, technology has stronghold its place in every ecosystem. Especially, business organizations are enhancing their AI capabilities to streamline routine processes. Starting from attending to customer queries to powering autonomous vehicles, the influence of artificial intelligence is no joke. Besides, critical fields like healthcare, education, and space are adopting AI to sophisticate their existing features and bring in innovation. Owing to the increasing usage, the demand for AI skills and AI professionals is also growing. According to a report, the hiring for artificial intelligence jobs has been rising at a rate of 74% annually. People who are taking up top artificial intelligence jobs explore more tech aspects while also getting a high salary. However, improving AI skills and keeping up with the emerging trends, tools, and technologies are important points while working in the digital sphere. MNCs always look for artificial intelligence professionals who have both tech knowledge and business skills. Analytics Insight has listed the top artificial intelligence jobs that aspirants should apply for in MNCs today.

Location: Bengaluru, Karnataka

Roles and Responsibilities: As a team lead/consultant- Artificial Intelligence at Accenture, the candidate will be aligned with the companys insights and intelligence verticals to help it generate insights by leveraging the latest artificial intelligence and analytics techniques to deliver value to clients. He/she should also help Accenture apply their expertise in building world-class solutions, conquering business problems, addressing technical challenges using AI platforms and technologies. In the company, the Artificial Intelligence team is responsible for the creation, deployment, and managing of the operations. Therefore, the candidate will be responsible for building AI solutions that benefit experts, research, and platform engineers for coming up with creative solutions. In the role, the candidate needs to analyze and solve moderately complex problems. They should create new solutions, leveraging and adapting existing methods and procedures.

Qualification:

Apply here for the job.

Location: Bengaluru, Karnataka

Roles and Responsibilities: The data scientist: artificial intelligence at IBM will help transform the companys clients data into tangible business value by analyzing information, communicating outcomes, and collaborating on product development. He/she will develop, maintain, evaluate, and test big data solutions. They will be involved in the design of data solutions using artificial intelligence-based technologies like H2O, Tensorflow. To deliver top-notch service, the candidate is expected to have skills in designing algorithms, implementing pipelines, validating model performance, and developing interfaces such as APIs. They are also responsible for designing and algorithms and implementation including loading from disparate datasets, pre-processing using Hive and Pig.

Qualification:

Apply here for the job.

Location: Pune

Roles and Responsibilities: Through this role, Philips gives an opportunity for the selected candidate to contribute towards the AI solutions roadmap with the responsibility of Architecture of a range of AI solutions and platforms for DXR. He/she will contribute towards the innovation of AI solutions and explore different internal and external providers, work on associated processes and procedures, participate and lead the innovation studios, develop, maintain technology inventory along while keeping the focus on execution. They should prepare proposals and alternatives, guiding towards the optimum balance of short and long-term requirements.

Qualification:

Apply here for the job.

Location: Chennai

Roles and Responsibilities: As a data scientist- manufacturing intelligence at Pfizer Limited, the candidate is expected to provide expert advanced modeling and data analytics support to all teams in the manufacturing intelligence organization and support project execution at manufacturing sites. He/she should translate business requirements into tangible solution specifications and high-quality on-time deliverables. They should leverage data manipulation/transformation, model selection, model training, cross-validation, and deployment support at scale.

Qualification:

Apply here for the job.

Location: Bangalore (WFH during Covid)

Roles and Responsibilities: General Motors expects its senior/lead engineer- ME automation (artificial intelligence) to propose and implement high-impact data and analytic solutions that address business challenges across a variety of manufacturing business units. The candidate should plan and execute projects including direction and oversight of technical work of associate data scientists. They should work with diverse technical teams and provide data and analytic oversight to ensure project deliverables that fulfill business needs and timing.

Qualification:

Apply here for the job.

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Top Artificial Intelligence Jobs in MNCs to Apply this Nov Weekend - Analytics Insight

NEW YORK (CBSNewYork) So many families have felt the pain of losing a loved one to a drug overdose, and now, new artificial intelligence technology is being used to help prevent such tragedies.

When you have a family member who lives this lifestyle, its a call you always know could come, Megan Wohltjen said.

Wohltjens brother, Samuel Grunlund, died of an overdose in March 2020, just two days after leaving a treatment facility. He was 27.

Very happy person. He was extremely athletic. Really intelligent, like, straight A student He started, you know, smoking marijuana and then experimenting with other drugs, Wohltjen told CBS2s Natalie Duddridge.

He wanted to get clean and addiction just destroyed his life, said Maura Grunlund, Sams mother.

Since Sams death, his mother and sister have been advocating for a new program they believe could have saved him. Its called Hotspotting the Opioid Crisis.

Researchers at MIT developed artificial intelligence that aims to stop an overdose before it happens.

This project has never been tried before, and its an effort to combine highly innovative predictive analytics and an AI-based algorithm to identify those who are most at risk of an overdose, said former congressman Max Rose, with the Secure Future Project.

The technology screens thousands of medical records through data sharing with doctors, pharmacies and law enforcement.

For example, over time, it might flag if a known drug user missed a treatment session, didnt show up to court or, in Sams case, just completed a rehab program. It then alerts health care professionals.

Im just calling to check in to see how things are going, said Dr. Joseph Conte,executive director of Staten Island Performing Provider System.

Conte says the program trains dozens of peer advocates who themselves are recovering addicts. They reach out to at-risk individuals and find out what they need from jobs to housing to therapy.

Theres no pressure on the patient to enter rehab. The goal is to keep them alive.

We cant help them if theyre dead If youre not ready for treatment, you should be ready for harm reduction. You should have Narcan available if you or a friend overdoses, Conte said.

Health officials say a record number of people, 100,000, died of overdoses in 2020.

This year alone on Staten Island, more than 70 people have fatally overdosed.

The number of opioid deaths per 100,000 people on Staten Island is about 170% higher than the national rate. Officials say fentanyl is largely to blame, and the lethal drug was found in 80% of Staten Island toxicology reports.

I believe that my son would be alive today if he hadnt used fentanyl I really feel that if this was any other disease, people would be up in arms, Maura Grunlund said.

Wohltjen says her brother always encouraged her to run the New York City Marathon, so this year, she did it, wearing his Little League baseball hat and raising thousands of dollars for the Partnership to End Addiction.

If we could save one life it would make a difference, Wohltjen said.

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Staten Island Family Advocating For New Artificial Intelligence Program That Aims To Prevent Drug Overdoses - CBS New York

ST. PETERSBURG, Fla. There have been plenty of news reports about labor shortages and businesses unable to fill positions throughout the pandemic. But, there is another side of this story that hasn't gotten enough attention; millions of people looking for jobs and can't get hired because of online algorithms, artificial intelligence, and more.

ABC Action News reporter Michael Paluska sat down with St. Petersburg resident Elizabeth Longden. She showed us all of the jobs she's applied for on LinkedIn and Indeed. More than a thousand applications were filed on LinkedIn and more than 140 on Indeed.

"So, business data strategy, talent and culture recruiter, diversity, equity and inclusion specialist, human resources," Longden said as she named off a few of the jobs she's applied for. "There are 128 pages with eight applications per page."

"That's a lot of jobs," Paluska said.

"Yeah, a lot," Longden replied with a half-smile that was more of an acknowledgment of her job woes.

"How do you process 1,000 plus rejections?" Paluska asked.

"It's discouraging, and fortunately, there haven't been 1,000 rejections. Most of the places don't even get back to you one way or the other," Longden said. "So yeah, we're looking at less than that. But it's still a big, you know, it's a big confidence blow, especially when you hear, oh, there's a labor crisis. And nobody wants to work. And like, hi, I would like to work."

According to the Bureau of Labor, a record 4.4 million people quit their jobs in September. That's a new all-time high. So, you would think millions of openings would help Longden. But, that's not the case.

Longden has a college degree, an insurance license, and a decade of work experience in human resources. In May, like many Americans throughout this pandemic, she was laid off from her company. So she took about a month off to reset and started the search in her field as an operations specialist, people ops, HR, and businesses operations.

"Have you ever been in a hole where you lost a job, and you couldn't get another one in the past?" Paluska asked.

"Not where I had lost one and couldn't get another one. I'd had times where I'd moved, you know, and had had trouble finding a job for maybe a month or two. But I was always able to find something," Longden said.

In September, the Harvard Business School released a study called Untapped Workers: Hidden Talent. The study explains this lack of hiring phenomenon. The lead author, Joseph Fuller, estimating millions of Americans are in the same position as Longden.

"So, you have this, this system that systematically excludes people that may not check every box in the employer's description of what they're looking for, but can be highly qualified on multiple parameters, even those the most important for job success, but they still get excluded," Fuller, professor of management practice at Harvard Business School said. "But what happens is, the employer in setting up these filters and ranking systems emphasizes some skills over others, intended to rely on two factors to make a decision."

The job search algorithms and artificial intelligence filter out candidates based on keywords before someone like Longden ever talks to a human being.

"And, the algorithms are unforgiving," Fuller said. "If you don't, if you don't have the right keywords, if you're just missing one of those attributes, you can get excluded from consideration even though you check every box on every other attribute they're looking for."

"Whose fault is that the company or LinkedIn or Indeed?" Paluska asked.

"You know, no company sets out to have a failed hiring process," Fuller said. "They provide the tools that their customers regularly ask for. So I think this is a tragedy, without a villain. It's the way companies have gone about it is optimized around minimizing the time it takes to find candidates in minimizing the cost of finding someone to hire. There's some kind of killer variable that is causing the system to say not qualified or not attractive relative to other applicants. The vast majority of those candidates never hear back anything just ghosted."

Longden has been ghosted a lot. One recruiter called her three times in a week asking for her to apply and when she thought she got the job, radio silence. Longden thought he was dead.

"I even was like, 'Are you alive?' You know, like, I just want to know, you're okay, you've just totally gone dark," Longden said.

Longden's job search hell has her skeptical of the entire process.

"I've also discovered that there's been a huge uptick in companies wanting pre-work from people. So all in all, I've probably done about 25 hours worth of pre-work for various companies, none of which has been compensated, and none of which I've even gotten a roll-out of," Longden said.

"Do you think they are using your work for their benefit?" Paluska asked.

"Oh, I'm sure," Longden said. "One of the things I was asked to create was an onboarding process for new employees. So that's what the role at the company would have been doing was onboarding their new employees as they came in. And so, one of the pre-work examples was to create an onboarding process from the offer to the 90-day mark of employment. And I did that. And I'm certain that they're having multiple people do that and pulling what they like best from everyone."

We reached out to LinkedIn and Indeed for comments but did not get a response back.

"Two or three quick suggestions for Elizabeth, the first is be very, very aware of language terms, and make your submission. Match what's being asked for, to the greatest degree you can with integrity," Fuller said. "The second thing I would say is, go on something like LinkedIn and look at the profiles of people who got the job you want. And what are they saying they do? What keywords are they using? Is there a regularly referenced tool that they claim expertise in that she doesn't have?"

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Job hunting nightmare: 1,000 plus job applications and still no offers - ABC Action News

By Alex Lawson, Financial Mail On Sunday

Published: 21:50, 27 November 2021 | Updated: 21:50, 27 November 2021

ITVs online viewers could soon be targeted with adverts based on the programmes they are watching.

The broadcaster is planning to use artificial intelligence to select advertising tailored to joyous or tragic moments in drama and news programmes on its ITV Hub streaming service.

The media giant is set to launch a pilot of the new technology early next year, internally dubbed moments, objects, moods or MOM.

Futuristic: The broadcaster is planning to use artificial intelligence to select advertising on its ITV Hub streaming service.

Under the plan, a relationship break-up scene in Coronation Street could soon be followed by an advert for a dating app or a holiday firm.

The technology is also able to scan scenes for products such as food and cars, inserting adverts for pizzas and vehicles in the next advert break as soon as 30 seconds afterwards.

ITV director of advanced advertising, Rhys McLachlan, told The Mail on Sunday: Weve always been aware what is in the shows you dont advertise a car just after a car crash in drama. But this takes that to the next level, scanning whats being screened in detail and in real time.

If there are moments of elation, joy, sadness, crisis, we can tell what it is and input it.

ITV this month forecast a sharp rise in its advertising revenue, its best performance in its 66-year history, aided by the post-pandemic bounce-back and the delayed Euro 2020 football tournament.

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ITV Will Use Artificial Intelligence To Tailor Adverts For Its Viewers - Todayuknews - Todayuknews

For decades offside has been one of the most controversial events in any soccer game: whistling them accurately is really difficult even with the help of the linesmen, and the referees have not always hit one of the plays that can most influence the outcome of the matches.

The VAR has helped to minimize the problems although it has not prevented other controversies but FIFA wants to go further and make use of an artificial intelligence system to whistle offside. The idea will be put to the test at the Arab Cup, and if successful, it could be implemented in the Qatar World Cup in 2022.

The tests will consist of an artificial intelligence system that will be installed in six stadiums where the Arab Cup will be held. The operation is closely linked to the VAR (Video Assistant Referee), which for some time has become an important help for referees in the world of professional football.

The artificial intelligence system will send VAR a message instantly when a player is offside, but it will be the referee who decides whether or not that player was intervening in the play.

The technology had already been launched in the preliminary phase by several companies that were working on something like this. Teams like Manchester City, Bayern Munich or Sevilla they have evaluated it in its stadiums.

The system to be installed in the Arab Cup consists of 12 cameras located around the pitch. Those cameras they monitor 29 points on the body of each player, creating a tracking system that allows to record the exact position of each part of the body of each player.

The ball, its movement and the exact moment in which the passes are made are also monitored. This will make it possible for the algorithms to detect if a play has been offside in as little as 0.5 seconds after that possible situation occurs.

The tests in the 2021 FIFA Arab Cup that takes place from November 30 to December 18 will therefore be the scene of some tests that could lead to this system be implanted in the next soccer World Cup in Qatar to be held in November 2022.

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Yes, it has been offside: FIFA tests an artificial intelligence system with a view to using it in the Qatar World Cup - InTallaght

Camille Nebeker, Ed.D., associate professor with appointments in the UC San Diego Herbert Wertheim School of Public Health and Human Longevity Science and the Design Lab

The University of California Presidential Working Group on Artificial Intelligence was launched in 2020 by University of California President Michael V. Drake and former UC President Janet Napolitano to assist UC in determining a set of responsible principles to guide procurement, development, implementation, and monitoring of artificial intelligence (AI) in UC operations.

To support these goals, the working group developed a set of UC Responsible AI Principles and explored four high-risk application areas: health, human resources, policing, and student experience. The working group has published a final report that explores current and future applications of AI in these areas and provides recommendations for how to operationalize the UC Responsible AI Principles. The report concludes with overarching recommendations to help guide UCs strategy for determining whether and how to responsibly implement AI in its operations.

Camille Nebeker, Ed.D., associate professor with appointments in the UC San Diego Herbert Wertheim School of Public Health and Human Longevity Science and the Design Lab, was a member of the working groups health subcommittee.

The use of artificial intelligence within the UC campuses cuts across human resources, procurement, policing, student experience and healthcare. We, as an organization, did not have guiding principles to support responsible decision-making around AI, said Nebeker, who co-founded and directs the Research Center for Optimal Digital Ethics Health at UC San Diego, a multidisciplinary group that conducts research and provides education to support ethical digital health study practices.

The UC Presidential Working Group on AI has met over the past year to develop principles to advance responsible practices specific to the selection, implementation and management of AI systems.

With universities increasingly turning to AI-enabled tools to support greater efficiency and effectiveness, UC is setting an important precedent as one of the first universities, and the largest public university system, to develop governance processes for the responsible use of AI. More info is available on the UC Newsroom.

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UC adopts recommendations for the responsible use of Artificial Intelligence - Preuss School Ucsd

Tech expert Jarno Duursma sees both advantages and disadvantages when it comes to using AI in healthcare. First the advantages: Scientists at Life Lines, a large-scale study into the onset of chronic diseases among 165 thousand people in the northern Netherlands, make use of artificially intelligent software. Duursma: This research has been going on since 2006. A huge database is being compiled from all those studies and questionnaires. With the help of AI, doctors are able to identify connections that they would otherwise never have spotted, like improving the diagnosis of depression or the prediction of cancer.

Or what about research into medicines? At Leiden University in the Netherlands, researchers are working on a model that is based on 3.8 million measurements that have been published on drug candidates since the 1970s. This acts as a kind of library that helps scientists search in the right direction. The system also predicts interactions between a chemical and a protein based on 5.5 billion data points. Using the softwares predictions, a chemist can get to work testing whether the potential drug will work in actual practice. In this regard, the use of artificial intelligence saves a lot of time and money. These are very fine applications that allow you to develop a drug that works faster or to use an existing drug for other diseases. These are great developments that get me fired up, Duursma adds.

Something else that gets Duursma enthused: Avatars in healthcare. For example, in the form of a digital doctor who conducts a simple intake or summarizes complicated and lengthy pieces of text in a short video for patients. By letting artificial intelligence carry out an intake, a doctor has more time to spare. You can also use this digital doctor to explain patient leaflets using a video, which sometimes works better than long texts.

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Despite his enthusiasm, Duursma also warns against using AI in healthcare. Our healthcare is becoming more and more expensive and is putting more pressure on society. We need to do something about this, but we shouldnt be focusing solely on technology. We still need to keep a critical eye on the dangers of AI.

In his view, we tend to overestimate the merits of software. To illustrate this point, he points to an algorithm that predicts whether a mole is malignant or not. The software was perfectly capable of picking out bad birthmarks, but what transpired when the scientists started probing into how it did that? The system did not reach its conclusions by looking at the moles themselves, but saw the ruler that dermatologists use to track the growth of suspicious moles as an important signal. This shows that an algorithm trained with different pictures of birthmarks comes up with an assessment based on something completely different than what you might expect.

Duursma sees the same thing in a host of initiatives that were designed to detect Covid-19 on lung photos with the use of AI. These lung photos are all different qualities and there are a lot of nuances in them. So, in any event, the data is very messy. A specific AI system once again drew a conclusion on the basis of something weird. The algorithm based its diagnosis on a font on the x-ray images of certain hospitals where there were a lot of corona patients. This black box is one danger that AI poses that we need to be aware of.

According to Duursma, another disadvantage of using artificial intelligence is that we want to capture all problems as data. By this datafication of the problem, you might be needlessly diminishing the problem. This creates a techno-solutionism, whereby you only focus on where data can be collected. Whereas when you zoom out, not everything can be captured as data. These problems are then excluded from it.

Nor should we be blind, Duursma believes, to any unintended long-term consequences that technology or artificial intelligence may cause. As an example, he cites the selfie cameras in iPhones: The selfie camera has contributed to making the individual even more of a focal point. Young people now visit a plastic surgeon with their favorite Snapchat filter: This is how I want to look. Thats an unintended consequence of this technology, but no Apple developer had ever considered that before.

Duursma goes on to say that we need to pay more attention to the talents and qualities that we lose along the way as a result of technology, especially in healthcare. I used to be very good at remembering phone numbers. Now my phone does that for me. The same goes for navigating or doing math in your head. These are skills that we are losing through the use of technology. Especially in healthcare, it is important that we treat this very carefully. Look at this from the perspective of a moral compass. Imagine that we will soon have an infallible algorithm for checking moles. Are radiologists then allowed to unlearn this skill? Or do we teach students not to look at photos because the software does that? I dont have answers to these questions, but we should continue to critically examine this aspect.

Tech philosopher at Fontys University of Applied Sciences, Rens van der Vorst, also offers much the same critical examples when talking about AI in healthcare. Generally speaking, you see that the diagnostic results of algorithms are quite disappointing. Following the outbreak of corona, all sorts of claims were made. For example, about an algorithm that could predict whether someone had corona based on the sound of someones cough. All those initiatives turned out not to be so successful after all. We tend to overestimate the impact of technology in the short term but underestimate it in the long term. Maybe the same kind of thing is happening with AI.

Van der Vorst sees mainly advantages to the use of AI in logistics operations in hospitals. Technology often serves as an amplifier. So if you start using AI to help a supermarket operate more efficiently, a supermarket will operate more efficiently. The same is true for a hospital. Weve seen that software is not yet good enough at making diagnoses, but artificial intelligence is capable of planning more efficiently. AI can also play a role in preventive care right now. With measurements taken in the home and advice on healthy living, to name a few things.

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Artificial intelligence in healthcare? 'Don't focus solely on technology' - Innovation Origins

PUNE, India, Oct. 19, 2021 /PRNewswire/ -- According to a recent market study published by Up Market Research titled, "Global Artificial Intelligence Marketby Technology (Machine Learning, Deep Learning, Machine Vision, Natural Language Processing), by Solution (Services, Hardware, Software), by End Use (BFSI, Automotive & Transportation, Advertising & Media, Agriculture, Manufacturing, Retail, Healthcare, Law) and Region: Size, Share, Trends and Opportunity Analysis, 2018-2028", As per the study the market value was USD 62.35 million in 2020. It is expected to grow at a compound annual rate (CAGR) of 40.2% between 2021 and 2028. Tech giants have been directing continuous research and innovation to drive the adoption of new technologies across a variety of industries, including automotive, healthcare, finance, and manufacturing. Technology has been an integral part of these industries for centuries, but Artificial Intelligence has put technology at the heart of many organizations. AI is now being integrated into almost every program and apparatus, from autonomous vehicles to life-saving medical equipment. AI has been proven to be the key element of the digital revolution.

The report covers comprehensive data on emerging trends, market drivers, growth opportunities, and restraints that can change the market dynamics of the industry. It provides an in-depth analysis of the market segments which include products, applications, and competitor analysis.

Key Market Players Profiled in the Report

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This report also includes a complete analysis of industry players that cover their latest developments, product portfolio, pricing, mergers, acquisitions, and collaborations. Moreover, it provides crucial strategies that are helping them to expand their market share.

Highlights on the segments of the Artificial Intelligence Market

Based on Solution, the market is divided into Hardware, Software, and Services. Software solutions dominated the artificial intelligence market, accounting for over 38.0% of global revenue in 2020. This is due to prudent improvements in information storage capacity and high computing power. Parallel processing capabilities are used to deliver high-end AI software for dynamic end-use verticals. Services in artificial intelligence include integration, maintenance, and support. This segment is expected to grow at an impressive rate during the forecast period. AI hardware comprises chipsets like Graphics Processing Unit (GPU), CPU and application-specific integrated circuits.

On the basis of Technology,the market is divided intoDeep Learning, Machine Learning, Natural Language Processing, and Machine Vision. Deep learning dominated the market, accounting for 38.0% of global revenue in 2020. Its complex data-driven applications such as speech recognition and text/content are responsible for the market's high share. This technology allows for the resolution of data volume challenges and offers attractive investment opportunities. Deep learning and machine learning are important investments in AI. This includes AI platforms as well as cognitive applications. These include tagging and clustering, categorization and hypothesis generation. Alerting, filtering and navigation are all part of the AI platform. They allow for the creation of intelligent, advisory and cognitively-enabled solutions.

Based on End Use, the market is divided into Healthcare, BFSI, Law, Retail, Advertising & Media, Automotive & Transportation, Agriculture, Manufacturing, and Others. Advertising and media dominated the market, accounting for over 18.0% of global revenue in 2020. The growing popularity of AI marketing applications is responsible for this high share. The healthcare sector will continue to hold a significant share of the market by 2028. BFSI includes financial analysis, risk assessment and investment/portfolio solicitations. Due to the high demand in this sector for compliance and risk applications, artificial intelligence has seen a significant increase in the BFSI. Retail, law, transportation, agriculture and other verticals are also possible for artificial intelligence systems. Conversational AI platforms are the most popular in each vertical.

On the basis of Regions,the market is categorized as Asia Pacific, North America, Latin America, Europe, and Middle East & Africa. North America was the dominant market, accounting for more than 40.0% of global revenue in 2020. This is due to government initiatives that encourage adoption of AI across different industries. As the United States' strategy to promote leadership in artificial intelligence, the American AI Initiative was launched by President Donald J. Trump in February 2019. Also, In the coming years, significant growth is expected in Asia Pacific. The significant increase in investments in artificial intelligence is responsible for this growth.

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Global Artificial Intelligence Market Is Expected To Set A New Benchmark With A CAGR Of 40.2% By 2028 | Up Market Research - PRNewswire

The global wound care solutions market is estimated to garner $30.5 billion in revenue by 2026 at a compound annual growth rate of 6.7%, finds Frost & Sullivan

SAN ANTONIO, Oct. 18, 2021 /CNW/ --Frost & Sullivan's recent analysis, Global Wound Care Solutions and New-age Technology Growth Opportunities, finds that participants in the wound care industry are investing heavily in technologies and solutions that require minimal/no medical intervention and can be used by patients, family and care providers. Primarily contributed by basic and advanced wound care solutions product types, the global wound care solutions market is estimated to garner $30.5 billion in revenue by 2026 from $20 billion in 2020, an uptick at a compound annual growth rate (CAGR) of 6.7%.

With technological advancements and a diverse array of traditional and advanced wound care solutions comprising apps, software, services, devices, and wearables, North America will dominate the wound care market by 2026. Also, the European wound care market will witness stable growth as the market becomes saturated due to technological advancements. Asia-Pacific will see a maximum growth rate as countries across the region adopt wound care solutions rapidly. Similarly, a surge in demand for faster wound recovery and advanced wound dressings in the Middle East and Latin America, respectively, will drive the wound care solutions market in the rest of the world over the forecast period.

For further information on this analysis, please visit: https://frost.ly/6eh

"The requirement for faster, less-invasive wound healing is boosting the demand for advanced wound care solutions," said Suchismita Das, Healthcare & Life Sciences Research Analyst at Frost & Sullivan. "Additionally, the resumption of elective surgeries that were placed on hold during the pandemic will further boost the post-pandemic demand for surgical wound care solutions."

Das added: "As end-users increasingly prefer 'at-home' solutions, simple and effective wound monitoring devices and solutions that require less intervention from clinicians are gaining traction. Further, the artificial intelligence (AI)-enabled solutions, sensor-based devices/wearables, and wound assessment devices aid care providers with clinical decision support (CDS) for faster diagnosis of complex wounds, leading to effective care pathways."

Government and corporate funding for developing next-gen wound care solutions that primarily enable early wound detection and prevention is set to increase, presenting the following growth opportunities for market participants:

Global Wound Care Solutions and New-age Technology Growth Opportunitiesis the latest addition to Frost & Sullivan's Healthcare & Life Sciences research and analyses available through the Frost & Sullivan Leadership Council, which helps organizations identify a continuous flow of growth opportunities to succeed in an unpredictable future.

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For six decades, Frost & Sullivan has been world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models, and companies to action, resulting in a continuous flow of growth opportunities to drive future success. Contact us: Start the discussion.

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Artificial Intelligence to Boost the Global Wound Care Market by 2026 with Minimal Intervention Solutions - inForney.com

Policymakers around the world are developing guidelines for use of artificial intelligence in health care.

Baymax, the robotic health aide and unlikely hero from the movie Big Hero 6, is an adorable cartoon character, an outlandish vision of a high-tech future. But underlying Baymaxs character is the very realistic concept of an artificial intelligence (AI) system that can be applied to health care.

As AI technology advances, how will regulators encourage innovation while protecting patient safety?

AI does not have a precise definition, but the term generally describes machines that have the capacity to process and respond to stimulation in a manner similar to human thought processes. Many industriessuch as the military, academia, and health carerely on AI today.

For decades, health care professionals have used AI to increase efficiency and enhance the quality of patient care. For example, radiologists employ AI to identify signs of certain diseases in medical imaging. Tech companies are also partnering with health care providers to develop AI-based predictive models to increase the accuracy of diagnoses. A recent study applied AI to predict COVID-19 based on self-reported symptoms.

In the wake of the COVID-19 pandemic and the rise of telemedicine, experts predict that AI technology will continue to be used to prevent and treat illness and will become more prevalent in the health care industry.

The use of AI in health care may improve patient care, but it also raises issues of data privacy and health equity. Although the health care sector is heavily regulated, no regulations target the use of AI in health care settings. Several countries and organizations, including the United States, have proposed regulations addressing the use of AI in health care, but no regulations have been adopted.

Even beyond the context of health care, policymakers have only begun to develop rules for the use of AI. Some existing data privacy laws and industry-specific regulations do apply to the use of AI, but no country has enacted AI-specific regulations. In January 2021, the European Union released its proposal for the first regulatory framework for the use of AI. The proposal establishes a procedure for new AI products entering the market and imposes heightened standards for applications of AI that are considered high risk.

The EUs suggested framework provides some examples of high-risk applications of AI that are related to health care such as the use of AI to triage emergency aid. Although the EUs proposal does not focus on the health care industry in particular, experts predict that the EU regulations will serve as a framework for future, more specific guidelines.

The EUs proposal strikes a balance between ensuring the safety and security of the AI market, while also continuing to promote innovation and investment in AI. These conflicting values also appear in U.S. proposals to address AI in health care. Both the U.S. Food and Drug Administration (FDA) and the U.S. Department of Health and Human Services (HHS) more broadly have begun to develop guidelines on the use of AI in the health industry.

In 2019, FDA published a discussion paper outlining a proposed regulatory framework for modifications to AI-based software as a medical device (SaMD). FDA defines AI-based SaMD as software intended to treat, diagnose, cure, mitigate, or prevent disease. In the agencys discussion paper, FDA asserts its commitment to ensure that AI-based SaMD will deliver safe and effective software functionality that improves the quality of care that patients receive. FDA outlines the regulatory approval cycle for AI-based SaMD, which requires a holistic evaluation of the product and the maker of the product.

Earlier this year, FDA released an action plan for the regulation of AI-based SaMD that reaffirmed its commitment to encourage the development of AI best practices. HHS has also announced its strategy for the regulation of AI applied in health care settings. As with FDA and the EU, HHS balances the health and well-being of patients with the continued innovation of AI technology.

The United States is not alone in its attempt to monitor and govern the use of AI in health care. Countries such as China, Japan, and South Korea have also released guidelines and proposals seeking to ensure patient safety. In June 2021, the World Health Organization (WHO) issued a report on the use of AI in health care and offered six guiding principles for AI regulation: protecting autonomy; promoting safety; ensuring transparency; fostering responsibility; ensuring equity; and promoting sustainable AI.

Scholars are also discussing the use of AI in health care. Some experts have urged policymakers to develop AI systems designed to advance health equity. Others warn that algorithmic bias and unequal data collection in AI can exacerbate existing health inequalities. Experts argue that, to mitigate the risk of discriminatory AI practices, policymakers should consider the unintended consequences of the use of AI.

For example, AI systems must be trained to recognize patterns in data, and the training data may reflect historical discrimination. One study showed that women are less likely to receive certain treatments than men even though they are more likely to need them. Similarly biased data would train an AI system to perpetuate this pattern of discrimination. Health care regulators must address the need to protect patients from potential inequalities without discouraging the development of life-saving innovation in AI.

As the use of AI becomes more prominent in health care, regulators in the United States and elsewhere find themselves considering more robust regulations to ensure quality of care.

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How Will Health Care Regulators Address Artificial Intelligence? - The Regulatory Review

HENDERSON, Nev., October 19, 2021--(BUSINESS WIRE)--Artificial Intelligence Technology Solutions, Inc., (OTCPK:AITX), a global leader in AI-driven security and productivity solutions for enterprise clients, filed its quarterly report on Form 10-Q with the Securities and Exchange Commission for the period ended August 31, 2021. AITX is a full SEC reporting company that files detailed annual and quarterly reports as prepared by a PCAOB registered firm and reviewed by an independent auditor.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20211019005507/en/

A sampling of the many AITX, its subsidiaries RAD, RAD-G and RAD-M, developments in the 2nd quarter of FY 2022. Included are two new apps, RAD Light My Way and RAD AR (Augmented Reality), plus the announcement of the RAD 3.0 product line. (Graphic: Business Wire)

"The first half of our fiscal year saw continued progress, development, plus exponential sales growth," said Steve Reinharz, President and CEO of AITX. "Both subscription revenues and sales revenues saw dramatic increases year over year."

Key Takeaways from the 10-Q Filing

AITX Financials

The Company completed actions to eliminate almost all of its dilutive financial instruments as follows: 1. Substantially all of the convertible debt has been paid or converted; 2. The number of Series F convertible preferred Shares were reduced; 3. An agreement was reached amongst all Series F shareholders not to convert their shares prior to August 2023, unless there is an uplisting of the Companys stock or an asset sale.

Unless subsequent events reinstate a dilutive financial instrument, none of which are under consideration, the number of outstanding shares of the company will only grow as a result of actions related to the effective and current S-3.

Device parts inventory (on hand) as of August 31, 2021 at $488K, up from $25K as of August 31, 2020, an increase of 1,852%. This increase supports both organic growth, inventory for fast delivery, and support for an expanding sales pipeline.

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Research and Development spending fiscal YTD August 31, 2021 at $1,334K, up from the previous FY period of $190K, a 602% increase. The increase supports investments in new project development, increased engineering, programming resources, and other R&D initiatives.

Robotic Assistance Devices (RAD) Sales Growth

The Company reports that for the quarter ended August 31, 2021, AITXs second quarter of fiscal year 2022, device subscription revenues, referred to as Recurring Monthly Revenue (RMR), increased 69% over the same period of the prior year. Six month total revenues, including all sales and subscriptions, increased 404% over the prior fiscal years period.

Sales Funnel Development

The Company reports that its sales funnel continues its solid growth as its sales team continues to produce significant activity and results. RAD President and COO, Mark Folmer commented, "We expect to close the month of October with an additional RMR of nearly $14,000. This will bring RADs total RMR to just over $80,000. Were expected to cross the $1 million annualized RMR run rate in the current fiscal quarter, ending November 30."

New Products Announced RAD 3.0

On Wednesday, October 13, 2021, the Company announced its new lineup of RAD 3.0 devices. RAD 3.0 marks a complete design and re-engineering of nearly all RAD solutions. "The entire RAD team worked feverishly hard throughout Q2 so that we could preview all of the improvements in design and performance that we showcased earlier this week," Reinharz added. "The response to our RAD 3.0 announcements has been overwhelmingly positive. Im sure that we have a hit on our hands and we cannot wait for our customers to see these in person," Reinharz commented.

The AITX Investors Open House and RAD 3.0 Reveal video is available for viewing at https://tinyurl.com/hkp5ds

"Fiscal year 2022 continues to confirm RADs inevitable progress," Reinharz added. "These second quarter results reveal our constant grind, whether its inventing new products, penetrating new markets, or solidifying our financial position. There is so much more on the immediate horizon that we expect to conquer, making FY 2022 an incredibly big year for us in sales, team, tech and industry stature," Reinharz concluded.

The Company recommends that interested parties examine the published 10-Q to review all details, and reminds readers that this release is limited to the applicable highlights of the quarter.

Follow Steve Reinharz on Twitter @SteveReinharz for future AITX and RAD updates.

AITX through its subsidiary, Robotic Assistance Devices, Inc. (RAD), is redefining the $25 billion (US) security and guarding services industry through its broad lineup of innovative, AI-driven Solutions-as-a-Service business model. RAD solutions are specifically designed to provide a cost savings to businesses of between 35%-80% when compared to the industrys existing and costly manned security guarding and monitoring model. RAD delivers this tremendous costs savings via a suite of stationary and mobile robotic solutions that complement, and at times, directly replace the need for human personnel in environments better suited for machines. All RAD technologies, AI-based analytics and software platforms are developed in-house.

CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTS

This release contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E the Securities Exchange Act of 1934, as amended and such forward-looking statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Statements in this news release other than statements of historical fact are "forward-looking statements" that are based on current expectations and assumptions. Forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those expressed or implied by the statements, including, but not limited to, the following: the ability of Artificial Intelligence Technology Solutions to provide for its obligations, to provide working capital needs from operating revenues, to obtain additional financing needed for any future acquisitions, to meet competitive challenges and technological changes, to meet business and financial goals including projections and forecasts, and other risks. Artificial Intelligence Technology Solutions undertakes no duty to update any forward-looking statement(s) and/or to confirm the statement(s) to actual results or changes in Artificial Intelligence Technology Solutions expectations.

About Artificial Intelligence Technology Solutions (AITX)

AITX is an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. Through its next-generation robotic product offerings, AITXs RAD and RAD-M companies help organizations streamline operations, increase ROI and strengthen business. AITX technology improves the simplicity and economics of patrolling and guard services, and allows experienced personnel to focus on more strategic tasks. Customers augment the capabilities of existing staffs and gain higher levels of situational awareness, all at drastically reduced cost. AITX solutions are well suited for use in multiple industries such as enterprises, government, transportation, critical infrastructure, education and healthcare. To learn more, visit http://www.aitx.ai and http://www.roboticassistancedevices.com, or follow Steve Reinharz on Twitter @SteveReinharz.

View source version on businesswire.com: https://www.businesswire.com/news/home/20211019005507/en/

Contacts

Steve Reinharz949-636-7060@SteveReinharz

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Animal testing is considered to be one of the worst cruelties towards any animal in this world over 100 million animals such as mice, frogs, dogs, rabbits, monkeys, cats, and many others are killed in animal experimentation. Meanwhile, cutting-edge technologies like artificial intelligence, machine learning, etc. are helping in boosting productivity while reducing workloads from human employees efficiently through AI models. Thus, artificial intelligence holds the potential to replace animal testing in the future. Artificial intelligence replacing animal testing can be a new approach to save these animals from undergoing lab experiments that hurt and kill them. Lets explore how AI models can save these animals from going through harmful animal testing.

Animal testing has saved millions of human lives at a cost of the precious lives of animals for a long time. It has helped the world with unbelievable medical innovations like vaccines, antibiotics, and many more drugs. But, this is not fair to these animals who sacrifice their lives for these experiments days after days for multiple years. This has been observed that some animal testing is not reliable enough to predict the behavior of drugs in human bodies. There is a huge wastage of time, money, animal lives, and many more. Thus, AI models are suitable and favorable for those experiments. Artificial intelligence and machine learning are known for generating reliable outcomes efficiently and effectively throughout the year if the training data is accurate.

AI models can save the lives of millions of animals with computer vision and accurate datasets. This is one of the true alternatives to animal models that holds huge potential to generate reliable and safe outcomes for drug discoveries. The emergence of quantum computing is creating a massive way with breakthroughs and experiments. Thus, there is no need for utilizing different animals for not-so-reliable animal testing.

In 2016, Thomas Hartung led some researchers from Johns Hopkins University to successfully develop an artificial intelligence algorithm that can determine substance toxicity after comparing it to similar databases and predictions from previously conducted animal testing. This software project showed this group of researchers that testing on animals showed inconsistencies and different animals can show different results to the same experiment. There is a concern that laboratories cannot use animals for these experiments but all kinds of testing are not possible to be completed by computers.

Start-ups like Verisim Life have started utilizing the power of artificial intelligence and machine learning in biosimulation to replace animal testing in the nearby future. It is a San Francisco biotechnology start-up focused on building digital animal simulations to reduce animal testing for drug discoveries. When animal testing is slow and unreliable, this AI model can eradicate the cruelty as well as boost the process of drug discovery to supply at a faster rate.

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West Virginia University researchers are using artificial intelligence and other advanced technologies to help diagnose people with autism.

The program is aimed at more easily identifying phenotypes related to Autism Spectrum Disorder. These phenotypes are noticeable traits or characteristics a person with ASD might have.

Autism phenotyping is something we are still in the dark ages with. We have no clue how many different types of autism we are dealing with, said WVU professor Xin Li, one of the projects head researchers.

Technology like neural imaging and behavior imaging, along with eye-tracking data will help identify these specific traits. Li says he hopes this data will find different types of ASD and help reduce the gap between a childs birth and their diagnosis. The average age of a child newly diagnosed with ASD is 4 years old -- Li says part of the goal of this research is to reduce that age in half, aiming for diagnoses at 2 years old. The earlier the diagnosis, Li says, the more effective the treatment.

Li says this research is important because of how little is known about ASD compared to other disorders. The better the technology available to diagnose those with ASD, the better phenotypes can be successfully grouped into ASD subtypes.

If we think about something were familiar with for example, a butterfly a butterfly can have different wings, have different patterns, colors Those are the easy traits for laymen to tell a different species from one butterfly to another one, Li said.

Recent data from the Centers for Disease Control and Prevention says 1 in 54 children in the U.S. are diagnosed with ASD.

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WVU Researchers Using Artificial Intelligence To Help Diagnose Those With Autism - West Virginia Public Broadcasting