Category Archives: Artificial Intelligence

Latest released the research study on Global Artificial Intelligence In Remote Patient Monitoring Market, offers a detailed overview of the factors influencing the global business scope. Artificial Intelligence In Remote Patient Monitoring Market research report shows the latest market insights, current situation analysis with upcoming trends and breakdown of the products and services. The report provides key statistics on the market status, size, share, growth factors of the Artificial Intelligence In Remote Patient Monitoring The study covers emerging players data, including: competitive landscape, sales, revenue and global market share of top manufacturers are Dexcom (United states),Honeywell Life Sciences (United States),Medtronic (Ireland),Philips Healthcare (Netherlands),ResMed (United States),Senseonics (United States),GYANT (United States),Medopad (United Kingdom),Chronisense Medical (Israel),Ejenta (United States),Cardiomo, Inc. (United States)

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Artificial Intelligence In Remote Patient Monitoring Market Definition:

AI and remote patient monitoring is a powerful tools to give providers insight into the day-to-day factors that may be impacting an individuals health AI can be leveraged to yield more successful remote patient monitoring programs before they even begin. Advanced algorithms can analyze large data sets like clinical, socioeconomic, and experiential data for a more holistic view of an individual. Based on this analysis, stratification can occur to identify not only risk levels, but to gain a better understanding of which individuals would respond most optimally to remote patient monitoring.

Keep yourself up-to-date with latest market trends and changing dynamics due to COVID Impact and Economic Slowdown globally. Maintain a competitive edge by sizing up with available business opportunity in Global Artificial Intelligence In Remote Patient Monitoring Market various segments and emerging territory.

Market Trend:

Market Drivers:

Market Opportunities:

The Global Artificial Intelligence In Remote Patient Monitoring Market segments and Market Data Break Down are illuminated below:

by Type (Braille Displays, Note Takers, Magnifiers), Application (Blind School, Disabled Persons Federation & Hospital, Enterprises & Social Organizations, Others), End Users (Children, Old Age People), Category (Communication, Vision, Hearing, Cognition, Self-Care, Mobility, Others)

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa

Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

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Strategic Points Covered in Table of Content of Global Artificial Intelligence In Remote Patient Monitoring Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Artificial Intelligence In Remote Patient Monitoring market

Chapter 2: Exclusive Summary the basic information of the Artificial Intelligence In Remote Patient Monitoring Market.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the Artificial Intelligence In Remote Patient Monitoring

Chapter 4: Presenting the Artificial Intelligence In Remote Patient Monitoring Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

Chapter 5: Displaying market size by Type, End User and Region 2015-2020

Chapter 6: Evaluating the leading manufacturers of the Artificial Intelligence In Remote Patient Monitoring market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile

Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries (2021-2026).

Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

Finally, Artificial Intelligence In Remote Patient Monitoring Market is a valuable source of guidance for individuals and companies in decision framework.

Data Sources & MethodologyThe primary sources involves the industry experts from the Global Artificial Intelligence In Remote Patient Monitoring Market including the management organizations, processing organizations, analytics service providers of the industrys value chain. All primary sources were interviewed to gather and authenticate qualitative & quantitative information and determine the future prospects.

In the extensive primary research process undertaken for this study, the primary sources Postal Surveys, telephone, Online & Face-to-Face Survey were considered to obtain and verify both qualitative and quantitative aspects of this research study. When it comes to secondary sources Companys Annual reports, press Releases, Websites, Investor Presentation, Conference Call transcripts, Webinar, Journals, Regulators, National Customs and Industry Associations were given primary weight-age.

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Artificial Intelligence In Remote Patient Monitoring Market Look a Witness of Excellent Long-Term Growth | Cardiomo, Chronisense Medical, Medtronic ...

Many fear that artificial intelligence will be the end of humankind heres the truth according to experts.

By now, most people around the world use some sort ofAI-utilizing devicethat is integrated into their daily lives.

They use Siri to check the weather, or ask Alexa to turn off their smart lights these are all forms of AI that many people dont realize.

However, despite the widespread (and relatively harmless) use of this technology in nearly every facet of our lives, some people still seem to believe that machines could one day wipe out humanity.

This apocalyptic ideal has been perpetuated through various texts and movies over the years.

Even staple figures in the field of science such as Stephen Hawking and Elon Musk have been vocal about technologys threat against humanity.

In 2020, Musk told the New York Times that AI would grow vastly smarter than humans and would overtake the human race by 2025, adding that things would get unstable or weird.

Despite Musks prediction, most experts in the field say humanity has nothing to worry about when it comes to AI at least, not yet.

The fear of AI taking over has developed from the idea that machines will somehow gain consciousness and turn on their creators.

In order for AI to achieve this, it would not only need to possess human-like intelligence, but it would also need to be able to predict the future or plan ahead.

As it stands, AI is not capable of doing either.

When prompted with the question Is AI an existential threat to humanity, Matthew OBrien, a robotics engineer from the Georgia Institute of Technologywrote onMetafact: The long-sought goal of a general AI is not on the horizon. We simply do not know how to make a general adaptable intelligence, and its unclear how much more progress is needed to get to that point.

The facts of the matter are that machines generally operate how theyre programmed to and we are a long way from developing the ASI (artificial superintelligence) needed for this takeover to even be feasible.

At present, most of the AI technology utilized by machines is considered narrow or weak, meaning it can only apply its knowledge towards one or a few tasks.

Machine learning and AI systems are a long way from cracking the hard problem of consciousness and being able to generate their own goals contrary to their programming, George Montanez, a data scientist at Microsoft, wrote under the same Metafact thread.

Some experts even go as far as to say that not only is AI not a threat to mankind, but could help us to better understand ourselves.

Thanks to AI and robotics today we are in the position to simulate in robots and colonies of robots the theories related with consciousness, emotions, intelligence, ethics and compare them on a scientific base, said Antonio Chella, a professor in Robotics at the University of Palermo.

So, we can use AI and robotics to understand ourselves better. In summary, I think AI is not a threat but an opportunity to become better humans by better knowing ourselves, he added.

That said, it is clear that AI (and any technology) could pose a risk to humans.

Some of these risks include overoptimization, weaponization, and ecological collapse, according to Ben Nye, the Director of Learning Sciences at the University of Southern California, Institute for Creative Technologies (USC-ICT).

If the AI is explicitly designed to kill or destabilize nationsaccidental or test releases of a weaponized, viral AI could easily be one of the next significant Manhattan Project scenarios, he stated on Metafact.

We are already seeing smarter virus-based attacks by state-sponsored actors, which is most assuredly how this starts, Nye added.

This story originally appeared on The Sun and has been reproduced here with permission.

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Could artificial intelligence really wipe out humanity? - New York Post

Adoption of Artificial Intelligence (AI) has accelerated since the pandemic hit as the whole world moved towards digitization. A study by Oxford University and Yale University indicates that AI will outperform humans in many ways and will automate all human jobs in the next 120 years. By 2024, AI will be better than humans at translation, will write bestselling books by 2049, and will perform surgeries by 2053. Machine learning (ML), the proficiency of a machine to mimic human ability to accumulate knowledge and use it to drive insights, is generally considered the basis of AI.

Data is the driving force for AI

Although AI might depend on its machine learning abilities, we need to take a step back and realize ML doesnt happen in vacuum. ML is driven by big data, without which it cant take place. Effectively, therefore, AI depends completely on the amount of data we can capture and the methods we use to process and manage it. For this reason, we need to pay more attention to data capture, transport, processing, and storage if we want to realize the promise of AI in the future.

Data Capture is pivotal

Capturing data is essential, whether its for software-based AI applications, smart robots based on AI, or machine learning. When AI products were initially designed, developers spent huge research and development resources collecting human behavioral data, both on the industry side and the consumer side.

In healthcare, many smart applications offer predictive analysis for prognoses and treatments. While these programs are becoming progressively smarter, they could be made even more accurate by applying increased intelligence gathered from human data.

User data is critical for developing technologies with higher intelligence, whether these are software systems, hardware devices, IoT devices, or home automation equipment. However, one of the most difficult aspects of capturing data in edge environments is transmitting it securely to a data center/ cloud because of the threat of ransomware attacks or viruses.

With Data, More is More

Projections from Statista indicate that by the end of 2025, the world will potentially generate 181 zettabytes of data, an increase of 129% over 2021s 79 zettabytes. This applies particularly in medical science, where various organizations collect massive amounts of data.

For example, data from the first Covid-19 vaccines administered helped to determine the accuracy of doses for all age groups.

Similarly, we need more data to achieve greater accuracy and more effective devices, whether for software, robotics, or anything else.We also need more data from real edges, whether these are static or moving, and regardless of how remote their location, to be able to run timely AI and ML applications.

The future of AI will depend on capturing more data through real-time applications from edges such as a gas pipeline, a submarine in the ocean, a defense front, healthcare, IoT devices, satellites, or rockets in space.

The Challenges of Managing Data

To optimize AI for the future, we also need high-performance systems. These could be storage or cloud-based systems, processed by modern, data-hungry applications. The more data you feed these applications, the faster they can run their algorithms and deliver insights, whether these are for micro strategy tools or business intelligence tools. This is usually called data mining, and, in the past, we did it by placing the data into a warehouse and then running applications to process it.

However, these methods are rife with challenges. Data-generating devices are now continuously churning out ever-growing amounts of information. Whether the source is autonomous vehicles or healthcare, and whether the platform is a drone or edge device, everything is capable of generating larger amounts of data than before. Until now, the data management industry has not been able to capture these quantities, either through networks, 5G, cloud, or any other storage method.

These circumstances have led to 90% of data gathered being dropped because of inadequate storage capacity and the inability to process it quickly and deliver it to a data center. The outcomes also apply to critical data captured at remote sites that have no connectivity or cloud applications running at the edge.

Forward to the Future

The more data we have, the better AI performs. The more information we can gather in real-time from real users on the ground, the smarter we can make our AI devices. The more we can make AI applicable to the use cases, the more human we can make the connection, and the better we can solve the users problems.

To date, much of the big data we generate goes unused, primarily because organizations cannot capture, transport, and analyze it fast enough to create real-time insights. Its essential for us to develop ways to resolve these challenges, to enable us to enjoy the advantages of putting AI to work for humanity.

Views expressed above are the author's own.

END OF ARTICLE

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Artificial Intelligence: The future is data capture, not machine learning - The Times of India Blog

What if the founder of the Qunun conspiracy movement, a man named Q, was finally identified? The The New York Times They shared the findings of two independent teams of forensic linguists who claimed to have identified not one, but two individuals hiding behind this famous pseudonym. Even more surprisingly, the two teams, who worked with different protocols, identified the same people.

This mysterious Q, whose name will be a reference to the level of secret defense clearance in the United States, first appeared on the web on October 28, 2017, on the American forum 4chan. In the wake of Pizzaa conspiracy theory targeting Hillary Clinton that there is a network of pedophiles in the Democratic camp, these regularly published messages are presented as alerts to an alleged plot organized by the deep state against Donald Trump.

On these letters two teams of scholars worked. The first, from Swiss startup OrphAnalytics, cut the messages into three-character sequences, then analyzed their frequency to compare them to messages sent by six potential authors. The second team, the French, relied on artificial intelligence and machine learning To compare Qs posts with those of 13 potential authors.

Rather than without consulting each other, the two teams first identified Paul Furber, a software developer based in South Africa. He would be the first to post under the pseudonym Q. The second author gradually took over, until he became the only one to use the Q account after migrating from 4chan to 8kun. It will be American conspiracy theorist Ron Watkins, the son of the founder of 8kun. So confident that scientists have reported a level of certainty ranging, depending on the difference, from 93 to 99% about these definitions. call before The New York TimesBoth men denied involvement.

This is not the first time that machine learning Used to identify the author. Another popular case dates back to 2013, when J.K. Rowling, the mother of the Harry Potter series, was identified as the secret writer of the Harry Potter story. cuckoo call, a detective novel written under a pseudonym by Robert Galbraith. However, in the case of Q, this discovery is more problematic for the authors of the letters, as the theories that partially formed the idea of an election stolen from Donald Trump and led to the events on Capitol Hill on January 6, 2021 have been transmitted.

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Qanun: The famous Q could have been recognized thanks to artificial intelligence - Persia Digest

Software tool is being developed to identify aggressive cancer-type HER2-low so 'targeted' treatment drugs can be prescribed

Image: Getty Images)

A particularly aggressive form of breast cancer, known as HER2-low, is thought to be present in about half the 55,000 new cases of the disease that emerge in the UK each year.

This is a sub-category of breast cancer that isnt picked up with conventional tests.

But researchers at Kings College London are working with Guys and St Thomas hospitals and Google-backed start up Owkin on a new artificial intelligence breast cancer diagnosis tool that could be available on the NHS within three years.

The tool would enable HER2-low sufferers to be found so they could then be given targeted treatment breast cancer drugs.

The group says: Research suggests many more women could benefit from targeted breast cancer treatments we just need to find them.

We hope to help thousands more women to benefit from targeted anti-HER2 treatments in the UK every year, with the transformative drugs able to extend and save patients lives.

The HER2 proteins behind this form of aggressive cancer are overproduced by faulty genes. The new AI models can analyse the amount of HER2 proteins in a cancer quickly.

Dr Jakob Nikolas Kather, assistant professor at the University of Aachen and visiting professor at Leeds University, said: This could speed up the detection of aggressive subtypes of cancer and help more women to receive optimal treatment.

Apart from spotting these HER2-low cases for the first time, the researchers are confident their device will be quicker and more accurate at diagnosing standard cases of HER2.

Once identified, the patients can be given a number of drugs used to treat HER2-positive cancer that potentially work well against this HER2-low subtype. AstraZenecas new drug Enhertu (trastuzumab deruxtecan), looks particularly promising.

Recently it was approved for use in Scotland, following approval in England, Wales and Northern Ireland.

Enhertu combines two drugs and attaches itself to the HER2 proteins, which can stop cancer cells growing. When the trastuzumab attaches itself to the proteins, it delivers antibody drug deruxtecan into breast cancer cells to kill them.

An AstraZeneca study published in September found the drug reduced the risk of death or progressive disease by 72% compared to an existing drug.

Meanwhile, the breast cancer diagnostic device is part of a new generation of AI with the power to transform cancer treatment and other aspects of healthcare.

The developers of the HER2 tool are planning to use it to better diagnose gastric cancer further down the line.

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'Artificial intelligence may find aggressive breast cancer cases tests fail to spot' - The Mirror

MANY fear that artificial intelligence will be the end of humankind here's the truth according to experts.

By now, most people around the world use some sort of AI-utilizing device that is integrated into their daily lives.

2

They use Siri to check the weather, or ask Alexa to turn off their smart lights these are all forms of AI that many people don't realize.

However, despite the widespread (and relatively harmless) use of this technology in nearly every facet of our lives, some people still seem to believe that machines could one day wipe out humanity.

This apocalyptic ideal has been perpetuated through various texts and movies over the years.

Even staple figures in the field of science such as Stephen Hawking and Elon Musk have been vocal about technology's threat against humanity.

In 2020, Musk told the New York Times that AI would grow vastly smarter than humans and would overtake the human race by 2025, adding that things would get "unstable or weird."

Despite Musk's prediction, most experts in the field say humanity has nothing to worry about when it comes to AI at least, not yet.

The fear of AI taking over has developed from the idea that machines will somehow gain consciousness and turn on their creators.

In order for AI to achieve this, it would not only need to possess human-like intelligence, but it would also need to be able to predict the future or plan ahead.

As it stands, AI is not capable of doing either.

When prompted with the question "Is AI an existential threat to humanity," Matthew O'Brien, a robotics engineer from the Georgia Institute of Technologywrote on Metafact: "The long-sought goal of a 'general AI' is not on the horizon. We simply do not know how to make a general adaptable intelligence, and it's unclear how much more progress is needed to get to that point".

The facts of the matter are that machines generally operate how theyre programmed to and we are a long way from developing the ASI (artificial superintelligence) needed for this takeover to even be feasible.

At present, most of the AI technology utilized by machines is considered "narrow" or "weak," meaning it can only apply its knowledge towards one or a few tasks.

"Machine learning and AI systems are a long way from cracking the hard problem of consciousness and being able to generate their own goals contrary to their programming," George Montanez, a data scientist at Microsoft, wrote under the same Metafact thread.

Some experts even go as far as to say that not only is AI not a threat to mankind, but could help us to better understand ourselves.

"Thanks to AI and robotics today we are in the position to 'simulate' in robots and colonies of robots the theories related with consciousness, emotions, intelligence, ethics and compare them on a scientific base," said Antonio Chella, a professor in Robotics at the University of Palermo.

"So, we can use AI and robotics to understand ourselves better. In summary, I think AI is not a threat but an opportunity to become better humans by better knowing ourselves," he added.

That said, it is clear that AI (and any technology) could pose a risk to humans.

Some of these risks include overoptimization, weaponization, and ecological collapse, according to Ben Nye, the Director of Learning Sciences at the University of Southern California, Institute for Creative Technologies (USC-ICT).

"If the AI is explicitly designed to kill or destabilize nations...accidental or test releases of a weaponized, viral AI could easily be one of the next significant Manhattan Project scenarios," he stated on Metafact.

"We are already seeing smarter virus-based attacks by state-sponsored actors, which is most assuredly how this starts," Nye added.

2

In other news, a four-tonne chunk of a SpaceX rocket ison a collision coursewith the Moon, according to online space junk trackers.

Boeing hassunk $450millioninto a flying taxi startup that hopes to whisk passengers across cities by the end of the decade.

Personalizedsmart guns, which can be fired only by verified users, may finally become available to U.S. consumers this year.

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Could artificial intelligence REALLY wipe out humanity?... - The Sun

What GAO Found

The Department of Defense (DOD) is actively pursuing artificial intelligence (AI) capabilities. AI refers to computer systems designed to replicate a range of human functions and continually get better at their assigned tasks. GAO previously identified three waves or types of AI, shown below.

Types of Artificial Intelligence (AI) and Associated DOD Examples

DOD recognizes that developing and using AI differs from traditional software. Traditional software is programmed to perform tasks based on static instructions, whereas AI is programmed to learn to improve at its given tasks. This requires large data sets, computing power, and continuous monitoring to ensure the capability performs as intended. The majority of AI capabilities that support DOD's warfighting mission are still in development. These capabilities largely focus on analyzing intelligence, enhancing weapon system platforms such as aircraft and ships that do not require human operators, and providing recommendations on the battlefield (such as where to move troops).

When acquiring new capabilities that depend on complex software, DOD has historically faced challenges, such as long acquisition processes and a shortage of skilled workers. GAO found that it continues to face these challenges along with others specific to AI, including having usable data available to train the AI. For example, AI for detecting an adversary's submarines requires gathering many images of various submarines and labeling them so the AI can learn to identify one on its own. DOD also faces difficulties integrating trained AI into existing weapon systems that were not designed for it and building trust in AI among its personnel. DOD initiated a variety of effortssuch as establishing a cross-service digital platform for AI and AI-specific trainingsto address these challenges and support its pursuit of AI, but it is too soon to assess effectiveness.

DOD has reported that AI is poised to change future battlefields and the pace of threats the U.S. faces. AI capabilities could enable machines to perform tasks that usually require human intelligence, such as identifying potential threats or targets on the battlefield. DOD designated AI a top modernization area and is investing heavily in AI tools and capabilities. Other nations are making significant investments in this area that threaten to erode the U.S. military technological and operational advantage.

The National Security Commission on Artificial Intelligence concluded in its March 2021 report that the U.S. needs to act quickly to ensure AI readiness. AI experts from inside and outside DOD agree that ensuring the department has the necessary infrastructure in place will be essential to developing, acquiring, and scaling AI for weapon systems effectively.

Senate Report 116-236 includes a provision for GAO to review DOD's AI warfighting acquisition-related efforts. This report examines (1) the unique nature of AI and current status of AI capabilities that support weapon systems, and (2) how DOD is addressing challenges in developing, acquiring, and deploying AI capabilities for weapon systems.

To do this work, GAO interviewed officials from over 20 DOD entities and reviewed DOD documentation as well as reports and recommendations from the National Security Commission on Artificial Intelligence, among others.

For more information, contact Jon Ludwigson at (202) 512-4841 or ludwigsonj@gao.govor Candice N. Wright at (202) 512-6888 or wrightc@gao.gov.

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Artificial Intelligence: Status of Developing and Acquiring Capabilities for Weapon Systems - Government Accountability Office

While future developments in artificial intelligence research are likely to yield useful industrial applications, in the medium term the technology may turn out to be less revolutionary than many expect.

Artificial intelligence (AI) can beat humans at complex tasks like chess and video games, but it still cannot reproduce behaviors that come naturally to people, like making small talk about the weather.

In his 1994 book The Language Instinct, linguist and cognitive scientist Steven Pinker concluded that: The main lesson of thirty-five years of AI research is that the hard problems are easy, and the easy problems are hard.

This paradox has led researchers to divide AI into two different types: artificial general intelligence or strong AI, and weak (or narrow) AI.

Strong AI means the ability to learn any task that people can perform. In contrast, weak AI is not intended to have cognitive abilities; it is a program designed to solve a single problem, like computers that play chess.

There are already several ways to program AI, like machine learning, deep learning or artificial neural networks. Programming, however, does not equal intelligence it is only part of the input necessary to generate intelligence. Advances in programming have led to better and broader applications of weak AI. For example, Googles AlphaGo is designed to play the board game Go using a mix of deep learning and statistical simulations. While it can outperform humans at the game, it cannot do any other task.

Despite the growing sophistication of programming, strong AI is slow to develop.

Despite the growing sophistication of programming, strong AI is slow to develop. The bar for intelligence can be set at different levels: sentience, conscience, acting as if understanding or merely interacting. The lowest benchmark is coping with a task involving unforeseen parameters, like driving a car. And even by this measure, artificial general intelligence still has a long way to go.

Driving is relatively easy: it involves anticipating and executing different moves. However, the driver also needs two additional abilities: estimating physical interactions and predicting human movements. In other words, a driver needs intuitive physics and psychology.

Intuitive physics is the human capacity to understand how objects interact. Researchers believe that people have an instinctive knowledge of physics that allows them to navigate the world and make predictions even in entirely new situations. AI, however, currently cannot reproduce this behavior. Different experiments have shown that it fails to predict and react to physical interactions within a 200-meter radius.

Intuitive psychology is the ability to gain insight into the motives of animate agents and to make predictions based on inferences. This does not mean reading minds, but rather understanding that other people have mental states like goals and beliefs. This intuition allows humans to anticipate other peoples actions and to plan their reactions. Here too, AI struggles. In this area, many animals perform much better than AI at this stage.

A self-driving car requires both of these skills. It must be able to judge whether it is safe to drive over an object on the road to evaluate whether something is a plastic bag or a brick. And it must be able to anticipate the intentions of human drivers whether a car is only trying to pass by or whether it is out of control and might collide. And even in an environment where all drivers are automated, the issue of predicting physical interactions remains.

It is possible that part of human knowledge is simply not transferable to machines.

Intuitive physics and psychology come easily to humans because they are acquired not through conscious learning but as a result of adaptive behavior. It is difficult to teach AI similar skills because there is no learning program for them. Researchers struggle to reproduce the adaptive behavior process that creates these abilities for the sake of teaching AI. And it is still unclear whether this is the right path maybe AI needs a different method to acquire these competencies.

Some analysts even claim that it is impossible for AI to learn these abilities because AI is not human and will therefore never be able to act and behave like people. AI, as its name indicates, is artificial. All it knows is, in principle, programmed by humans. And it is possible that part of human knowledge is simply not transferable to machines.

There are three basic scenarios for the medium-term development of AI. A first base scenario can be combined with either of the other two.

The base scenario is that the development of weak AI will continue. Programming will lead to increasingly refined single-task feats. This type of AI can be used in manufacturing, healthcare, administration and some services, but its constraints are apparent. Once it is dedicated to a function, it is not able to perform another. And it can only perform tasks with limited scope for unforeseen factors. Still, weak AI can free up human labor capabilities and work more precisely. It can also potentially lower costs.

Building on the base scenario, one possibility is the development of strong AI in an upward concave curve. The first developments would make larger progress than subsequent ones. There would be a diminishing marginal increase of benefits to strong AI innovation with time. This scenario would confirm the claim that there are limits to what machines can learn. If there is a fundamental difference between human and nonhuman intelligence, then strong AI will develop only to a point. This is not necessarily bad news. While teaching computers, scientists could find out a lot about human intelligence and discover new programming principles. These benefits could make it worth pursuing artificial general intelligence even if research falls short of creating strong AI. It could also lead to humans specializing in some areas and machines in others, which would bring welfare gains.

Strong AI could also develop in an upward convex curve. The first steps would be arduous but later developments would accelerate. In this scenario, there would be no difference between human and machine intelligence, and therefore no limitations to what strong AI can learn. Coming up with the first building blocks of artificial general intelligence is difficult because it involves discovering new programming principles and new hardware. But once those are in place, innovation could speed up. During the Industrial Revolution, building the first machines and connecting them to a power grid was difficult. But once the technology became widespread, new inventions multiplied. In this scenario, strong AI could usher in a new way of organizing human life most likely for the better.

Hypothetically, research in AI could implode because of government intervention or spiraling costs. This is, however, extremely unlikely. Even if governments curb AI research, the potential rewards are high enough to attract resources.

It is also extremely unlikely that the singularity would occur, with artificial agents becoming more intelligent than humans and ultimately taking over. Even if artificial intelligence were to surpass human capabilities, machines would not necessarily conquer the world. Intelligence is only one aspect of life some people are more intelligent than others and it does not determine social order.

Overall, AI and its implementations hold much promise. However, its proponents have been overpromising. Weak AI is the frontier of innovation at the moment, and the technology has more benefits than its name implies. Strong AI is more science fiction than reality for now. It is not even clear whether it can ever become reality. But this, too, is not bad news. Researching strong AI can still lead to many beneficial innovations.

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The artificial intelligence of the future - Geopolitical Intelligence Services AG

SOME OF Africas leading female business leaders will gather in Johannesburg next month for a special conference examining the future role that artificial intelligence (AI) will play in the continents economy.

What Women CEOs in Africa Need to Know about Artificial Intelligence,will feature global experts who will share insights on how the latest developments in AI are likely to affect some of Africas key industries such as financial services, telecoms, natural resources, agriculture, and technology.

The conference will be hosted by Harvard Business School professor Karim Lakhani and Tsedal Neeley, also a professor at Harvard Business school who will give a presentation on how to get large organisations to adopt an AI mindset.

The event, which will take place on March 8 to mark International Women Day, will also see the launch of Africa.coms Definitive List of Women CEOs. The list highlights the women who lead corporations in Africa with over $100 million in revenue based on data provided by Bloomberg.

Among the high profile speakers at the event are Lillian Barnard, Managing Director, Microsoft, South Africa; Brenda Mbathi, President, GE East Africa; Aida Diarra, Head of Sub-Saharan Africa, VISA; Amelia Beattie, Chief Executive, Liberty Two Degrees and Helene Echevin, CEO, CIEL Healthcare.

Africa.com ChairTeresa Clarke said: We are thrilled to empower African women CEOs by providing them with a leading edge understanding of how AI is changing business throughout the world. We expect that African male CEOs will also join this event to take advantage of the valuable content being presented.

A 2019 study from the Boston Consulting Group found that in Africa alone, data-driven online marketplaces that rely on AI in fields such as fintech and e-commerce could create 3 million jobs by 2025, while dramatically expanding access to goods and services across the region.

Some of the leading players in the global tech sector are already positioning themselves to tap into Africas digital potential.

In 2019 Google openedits first Africa-based artificial intelligence research center in Ghana.

Other big tech companies such as IBM and Microssoft have also committed to developing Africas talent and contributing to its digital growth.

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Africa's women business leaders to examine the future role of artificial intelligence - The Voice Online

Advanced forms of artificial intelligence may already be displaying glimmers of consciousness, according to leading computer scientists.

MIT researcher Tamay Besiroglu joined OpenAI cofounder Ilya Sutskever in warning that some machine learning AI may have achieved a limited form of sentience, sparking debate among neuroscientists and AI researchers.

It may be that todays large neural networks are slightly conscious, tweeted Mr Sutskever, who co-founded OpenAI alongside tech billionaire Elon Musk.

The comment drew a strong response from leaders in the field, including Professor Murray Shanahan from Imperial College London, who said: In the same sense that it may be that a large field of wheat is slightly pasta.

Mr Besiroglu, defended Dr Sutskevers idea, claiming that such possibilities should not be derided or dismissed.

Seeing so many prominent machine learning folks ridiculing this idea is disappointing, he tweeted. It makes me less hopeful in the fields ability to seriously take on some of the profound, weird and important questions that theyll undoubtedly be faced with over the next few decades.

Attempts to define consciousness have divided neuroscientists and philosophers for centuries, though a broad way of describing it is as a narrative constructed by our brains, capable of perception through senses and imagination. Some definitions also include the ability to experience positive and negative emotions, like love and hate.

A recent study attempted to track advances in machine learning over the last decade, showing a clear trend in major advances in vision and language

One of the authors of the research was Mr Besiroglu, who drew a line across across a trend graph in a tongue-in-cheek attempt to classify which leading AI algorithms could be considered to have some form of consciousness.

OpenAIs sophisticated text generator GPT-3 was placed in the maybe slightly conscious category, as well as AlphaGo Zero, developed by Googles DeepMind AI division.

MIT researcher Tamay Besiroglu

(Tamay Besiroglu/ MIT)

I dont actually think we can draw a clear line between models that are not conscious vs. maybe slightly conscious. Im also not sure any of these models are conscious, Dr Besiroglu told Futurism.

That said, I do think the question could be a meaningful one that shouldnt just be neglected.

OpenAI CEO Sam Altman offered his thoughts on his companys most powerful AI, tweeting: I think GPT-3 or -4 will very, very likely not be conscious in any way we use that word. If they are, its a very alien form of consciousness.

The prospect of artificial consciousness rather than simply artificial intelligence raises ethical and practical questions: If machines achieve sentience, then would it be ethically wrong to destroy them or turn them off if they malfunction or are no longer useful?

Jacy Reese Anthis, who researches technology and ethics, described such a dilemma as one of the most important questions for the long-term future.

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Artificial intelligence may already be slightly conscious, AI scientists warn - The Independent