Category Archives: Artificial Intelligence

A research group from the Graduate School of Informatics, Nagoya University, has taken a big step towards creating a neural network with metamemory through a computer-based evolution experiment.

In recent years, there has been rapid progress in designing artificial intelligence technology using neural networks that imitate brain circuits. One goal of this field of research is understanding the evolution of metamemory to use it to create artificial intelligence with a human-like mind.

Metamemory is the process by which we ask ourselves whether we remember what we had for dinner yesterday and then use that memory to decide whether to eat something different tonight. While this may seem like a simple question, answering it involves a complex process. Metamemory is important because it involves a person having knowledge of their own memory capabilities and adjusting their behavior accordingly.

In order to elucidate the evolutionary basis of the human mind and consciousness, it is important to understand metamemory, explains lead author Professor Takaya Arita. A truly human-like artificial intelligence, which can be interacted with and enjoyed like a family member in a persons home, is an artificial intelligence that has a certain amount of metamemory, as it has the ability to remember things that it once heard or learned.

When studying metamemory, researchers often employ a delayed matching-to-sample task. In humans, this task consists of the participant seeing an object, such as a red circle, remembering it, and then taking part in a test to select the thing that they had previously seen from multiple similar objects. Correct answers are rewarded and wrong answers punished. However, the subject can choose not to do the test and still earn a smaller reward.

A human performing this task would naturally use their metamemory to consider if they remembered seeing the object. If they remembered it, they would take the test to get the bigger reward, and if they were unsure, they would avoid risking the penalty and receive the smaller reward instead. Previous studies reported that monkeys could perform this task as well.

The Nagoya University team comprising Professor Takaya Arita, Yusuke Yamato, and Reiji Suzuki of the Graduate School of Informatics created an artificial neural network model that performed the delayed matching-to-sample task and analyzed how it behaved.

Despite starting from random neural networks that did not even have a memory function, the model was able to evolve to the point that it performed similarly to the monkeys in previous studies. The neural network could examine its memories, keep them, and separate outputs. The intelligence was able to do this without requiring any assistance or intervention by the researchers, suggesting the plausibility of it having metamemory mechanisms.The need for metamemory depends on the user's environment. Therefore, it is important for artificial intelligence to have a metamemory that adapts to its environment by learning and evolving, says Professor Arita of the finding. The key point is that the artificial intelligence learns and evolves to create a metamemory that adapts to its environment.

Creating an adaptable intelligence with metamemory is a big step towards making machines that have memories like ours. The team is enthusiastic about the future, This achievement is expected to provide clues to the realization of artificial intelligence with a human-like mind and even consciousness.

The research results were published in the online edition of the international scientific journal Scientific Reports. The study was partly supported by a JSPS/MEXT Grants-in-Aid for Scientific Research KAKENHI (JP17H06383 in #4903).

Scientific Reports

Evolution of metamemory based on self-reference to own memory in artificial neural network with neuromodulation

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Creating artificial intelligence that acts mo - EurekAlert

For unleashing the full potential of Artificial Intelligence (AI), contemporary leadership that can augment AI and humans instead of strategizing to replace AI with humans is the need of the hour from a balanced social, legal, economic, political, and technological perspective.

It is a pre-requisite for the organization in the current scenario post-pandemic to realign the business processes with AI considering adaptability, complexity, scalability, decision making, and customization of products and services. For example, in Morgan Stanley Robo-advisors offers clients an array of investment options based on real-time market information, in Pfizer wearable sensors for Parkinsons patients, track symptoms 24/7 allowing customized treatment and in Unilever automated applicant screening vividly inflates the pool of qualified candidates for hiring managers to appraise.

According to Harvard business review, it is predicted that the performance and bottom line of organizations is enhanced when humans and AI augment each other leading to enhancing life skills of individuals and teams and technical skills of machines with the right fusion of learning and development activities.

Pandemic and resultant transformations have fast-tracked the mechanization of many everyday jobs, with future skepticism towards artificial intelligence (AI) adding to the increase in the rate of unemployment. In reality, if actions are taken appropriately and strategically by the organizations and government combined the reverse might be true that is AI will add to more jobs. For redefining success with AI, humans need to execute three vital roles. These roles are at three entry points input, process, and output. At the input level, humans need to give the right information and construct machines to accomplish a particular departmental and organizational objective. At the process point, the role of humans is to maintain ethics and integrity by responsible use of the machines.

At the output level keep a check on quality and quantity of output to maintain the code of conduct and avoid controversial episodes like Tay an artificial intelligence chatter released by Microsoft Corporation via Twitter in 2016 caused ensuing controversy when the bot began to tweet provocative and offensive messages over its Twitter account, triggering Microsoft to close the service only 16 hours post its launch.

Humans also play a major role in developing traits of virtual personal assistants like Google Assistant, Siri, and Alexa to warrant that they precisely echo their organizations brands be it Apple or Amazon. AI can also increase creativity, which will empower individuals to focus on their unique human strengths like first impressions and judgments. Reflect how Autodesks Dreamcatcher AI captures the creative mind of the best designers. The designer provides the Dreamcatcher with the standard for the desired product. Then the designer can control the software to tell which chair he/she likes or dislikes, leading to a new design round. This allows designers to focus on leveraging their unique human strengths of expert judgment and aesthetics.

In the current context of COVID-19, organizations have speedily applied machine learning expertise in several areas, including expanding customer communication, understanding the COVID-19 epidemic, and augmenting research and treatment. An example is Clevy.io, a French start-up. It launched a chatbot that uses real-time information from the French administration and the WHO to assess recognized symptoms and respond to queries about government policy.

Redefining success for the business, leveraging the utility of AI and humans involves more than the execution of strategic AI plans. It also necessitates a substantial assurance to evolving individuals and teams with the right blend of up skilling and reskilling so that it will empower individuals to take actions and give quantifiable results effectually at the human-machine edge.

(This article is authored by Dr Kasturi R Naik, Assistant Professor, DESs NMITD and Dr Srinivasan R Iyengar Director, JBIMS)

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Redefining success in business with Artificial Intelligence - Free Press Journal

Coral reefs are among Earths most stunning and biodiverse ecosystems. Yet, due to human-induced climate change resulting in warmer oceans, we are seeing growing numbers of these living habitats dying.

The urgency of the crisis facing coral reefs around the world was highlighted in a recent study that showed that 91% of Australias Great Barrier Reef had experienced coral bleaching in the summer of 202122 due to heat stress from rising water temperatures.

Determining reef health is key to gauging the extent of the problem and developing ways of intervening to save these ecosystems, and a new artificial intelligence (AI) tool has been developed to measure reef health using sound.

Research coming out of the UK is using AI to study the soundscape of Indonesian reefs to determine the health of the ecosystems. The results, published in Ecological Indicators, shows that the AI tool could learn the song of the reef and determine reef health with 92% accuracy.

The findings are being used to track the progress of reef restoration.

More on artificial intelligence: Are machine-learning tools the future of healthcare?

Coral reefs are facing multiple threats, including climate change, so monitoring their health and the success of conservation projects is vital, says lead author Ben Williams of the UKs University of Exeter.

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One major difficulty is that visual and acoustic surveys of reefs usually rely on labour-intensive methods. Visual surveys are also limited by the fact that many reef creatures conceal themselves, or are active at night, while the complexity of reef sounds has made it difficult to identify reef health using individual recordings.

Our approach to that problem was to use machine learning to see whether a computer could learn the song of the reef. Our findings show that a computer can pick up patterns that are undetectable to the human ear. It can tell us faster, and more accurately, how the reef is doing.

Fish and other creatures make a variety of sounds in coral reefs. While the meaning of many of these calls remains a mystery, the new machine-learning algorithm can distinguish overall between healthy and unhealthy reefs.

Recordings used in the study were taken at theMars Coral Reef Restoration Project, which is restoring heavily damaged reefs in Indonesia.

The studys co-author Dr Tim Lamont, a marine biologist at Lancaster University, said the AI method provides advantages in monitoring coral reefs.

This is a really exciting development, says Lamont. Sound recorders and AI could be used around the world to monitor the health of reefs, and discover whether attempts to protect and restore them are working.

In many cases its easier and cheaper to deploy an underwater hydrophone on a reef and leave it there than to have expert divers visiting the reef repeatedly to survey it, especially in remote locations.

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Artificial intelligence tool learns song of the reef to determine ecosystem health - Cosmos

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence-enabled Drug Discovery, 2022: Frost Radar Report" report has been added to ResearchAndMarkets.com's offering.

This report presents competitive profiles on each of the companies based on their strengths, opportunities, and a small discussion on their positioning.

The report finds that the impact of AI on the entire pharma value chain can more than double what is achievable using traditional analytics and capture between 2% and 3% of industry revenue, amounting to more than $50 billion in potential annual impact.

Pharmaceutical drug discovery and development has been suffering from declining success rates with new molecules primarily because of poor external validity of preclinical models and lack of efficacy of the molecule in terms of the intended disease indication.

Drug success rates continue to be in the range of only 1 in 10 that enters clinical phases pushing through to FDA approval. Frost & Sullivan finds that traditional solutions focused primarily on data from limited sources and rule-based computational techniques used to address the understanding of targets and leads are inefficient.

Artificial intelligence (AI) is set to transform the drug discovery landscape. AI-based products and solutions are transforming drug discovery and development dynamics by enabling pharmaceutical players to shorten discovery timelines, enhance process agility, increase prediction accuracy on efficacy and safety, and improve the opportunity to diversify drug pipelines using a cost-effective model.

Most pharmaceutical vendors are focused on collecting, creating, and augmenting data from across laboratories, clinical trials, real-world evidence, biobanks, and repositories. The increasing volume and veracity of clinical and research data is compelling traditional providers to leverage enabling tools and technologies such as cloud computing, AI and machine learning, natural language processing, and advanced analytics to make a shift to a relatively fast, rational data-driven drug discovery and development approach.

To remain competitive, companies must strike the right balance of data, AI, and computational capability and match it with the wet lab capability. There remains inadequate understanding of the biological networks and drug-target interactions. Enter AI, which has been able to support the identification and prioritization of disease-specific therapeutic targets based on gene-disease associations. Such results must be replicated and validated through in vitro experiments and in vivo models.

Key Topics Covered:

1. Strategic Imperative and Growth Environment

2. Frost Radar

3. Companies to Action

4. Strategic Insights

5. Next Steps

For more information about this report visit https://www.researchandmarkets.com/r/h6d2f7

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Artificial Intelligence-enabled Drug Discovery Competitive Analysis Report 2022: A Benchmarking System to Spark Companies to Action - Innovation that...

AN ARTIFICIAL intelligence program has developed its own language and no one can understand it.

OpenAI is an artificial intelligence systems developer - their programs are fantastic examples of super-computing but there are quirks.

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DALLE-E2 is OpenAI's latest AI system - it can generate realistic or artistic images from user-entered text descriptions.

DALLE-E2 represents a milestone in machine learning - OpenAI's site says the program "learned the relationship between images and the text used to describe them."

A DALLE-E2 demonstration includes interactive keywords for visiting users to play with and generate images - toggling different keywords will result in different images, styles, and subjects.

But the system has one strange behavior - it's writing its own language of random arrangements of letters, and researchers don't know why.

Giannis Daras, a computer science Ph.D. student at the University of Texas, published a Twitter thread detailing DALLE-E2's unexplained new language.

Daras told DALLE-E2 to create an image of "farmers talking about vegetables" and the program did so, but the farmers' speech read "vicootes" - some unknown AI word.

Daras fed "vicootes" back into the DALLE-E2 system and got back pictures of vegetables.

"We then feed the words: 'Apoploe vesrreaitars' and we get birds." Daras wrote on Twitter.

"It seems that the farmers are talking about birds, messing with their vegetables!"

Daras and a co-author have written a paper on DALLE-E2's "hidden vocabulary".

They acknowledge that telling DALLE-E2 to generate images of words - the command "an image of the word airplane" is Daras' example - normally results in DALLE-E2 spitting out "gibberish text".

When plugged back into DALLE-E2, that gibberish text will result in images of airplanes - which says something about the way DALLE-E2 talks to and thinks of itself.

Some AI researchers argued that DALLE-E2's gibberish text is "random noise".

Hopefully, we don't come to find the DALLE-E2's second language was a security flaw that needed patching after it's too late.

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Artificial intelligence spotted inventing its own creepy language and its baffling researchers... - The US Sun

CARY A SAS executive has been named to the board of directors for EqualAI, a nonprofit that aims to reduce unconscious bias in how artificial intelligence is both developed and used.

Reggie Townsend, the director of the data ethics practice at SAS, officially joined the organizations board of directors. According to SAS, the company has also joined the organization as a corporate partner.

Im honored to join the board of EqualAI and work with the team on expanding AI accountability, inclusivity and equity, said Townsend, in a statement released by the nonprofit organization this week. AI comes with promise and peril. As AI proliferates and penetrates so many aspects of our lives, now is a critical time in our history to take action.

According to the statement, Townsend will serve on the board in a capacity that enables him to leverage and lend technical expertise, as well as share experiences and best practices from SAS.

SAS executive to serve as artificial intelligence advisor to Biden Administration

Townsend is already a member of the National Artificial Intelligence Advisory Committee and is one of the advisors to the administration of President Joe Biden, according to prior reporting from WRAL TechWire. He recently earned a credential from EqualAI that pertains to responsible governance of artificial intelligence, the company statement noted.

Reggies deep understanding with regard to mitigating harms through fair, sustainable applications of data, artificial intelligence and other technologies is a critical piece of delivering on our mission, said Miriam Vogel, president and CEO of EqualAI and Chair of the National Artificial Intelligence Advisory Committee (NAIAC), in a statement. I am also highly appreciative to have SAS take a position of leadership in their industry by becoming a member of EqualAI and committing to the innovative, responsible, and inclusive artificial intelligence.

The responsible use of artificial intelligence was one of the primary focal points of a SAS event earlier this quarter, as the company prepares to become what it calls IPO ready.

SAS moving toward IPO readiness, says global cloud revenue up 19%

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SAS exec tapped to join board of EqualAI to fight bias in artificial intelligence - WRAL TechWire

The book Artificial Intelligence Based Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics gives a comprehensive explanation of the role of machine learning and artificial intelligence in cancer nanomedicine. It presents 10 chapters that cover multiple dimensions of the subject. These dimensions are:

- The need of AI and ML in designing new cancer drugs

- Application of AI in cancer drug design

- AI-based drug delivery models for cancer drugs

- Diagnostic applications of AI

- Intelligent nanosensors for biomarker profiling

- Predictive models for metastatic cancer

- Cancer nanotheranostics

- Ethics of AI in medicine

The book serves as a reference for scholars learning about cancer diagnostics and therapeutics. Biomedical engineers who are involved in healthcare projects will also find the concepts and techniques highlighted in the book informative for understanding modern computer-based approaches used to solve clinical problems.

To overcome this challenge application of artificial intelligence (AI) along with nanomedicine can serve as a helping tool for optimizing the drug and dose parameters. Conversion between these two fields enables up gradation of patient data acquisition, improved design of nanomaterials. In cancer the high intratumor and interpatient heterogeneity behavior is quite difficult to plan for a rational therapeutic design and further to analyse their output is extremely difficult. In this scenario application and integration of AI based approaches such as pattern analysis and algorithms models can bridge the gap, for improved accuracy of diagnostics and therapeutics. With the help of AI algorithms large datasets can be processed, complex patterns can be exploited for improvement of nanotechnology based design for cancer diagnostics and treatments. Application of precision cancer nanomedicine is highly essential as every patient is unique. Patient groups have varied differences, such as age, gender, height, eye color, blood type as well as unique molecular signatures, which leads to different phenotypic changes and wide-ranging of drug responses amongst patients. Further, patients vary substantially with regard to the dosages needed to attain drug synergy, and desirable degree of drug exposure to reach optimal treatment outcomes. Optimization of dosing in oncology highly essential, often dose reductions are implemented to manage treatment-related toxicity and it faces key challenges while translating it to a clinical practice for dosing establishment. This type of challenges can be addresses via recent advances in AI.

In this regard, AI plays a critical role in reconciling this space into an actionable treatment response.

In the era of computer aided technology, almost all field are involved with information technology. AI is the amalgamation of computer ethics and bioethics. During application all aspects of research technology pertaining to the their field needs to be ethics free so that they can be freely used for human welfare. These AI enabled novel technologies based therapy needs to be followed at all levels the ethical principles like human privacy, dignity, justice, morality and fair access to the knowledge for possible beneficial of therapy. The book entitled Artificial Intelligence Based Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics. by Dr. Fahima Dilnawaz and Dr. Ajit Kumar Behura exemplifies various modes of the application of AI towards cancer nanomedicine and its related aspects of bioethics. This book indeed is a modest effort to the several approaches of cancer nanomedicine having a broad readership that includes researchers, scholars, academicians, clinicians and their allied partners. The authors have made intensive efforts by inviting various reputed contributors to contribute their views.

About the Editors:

Dr. Fahima Dilnawaz is a Women Scientist at the Department of Science and Technology, in the laboratory of nanomedicine of the Institute of Life Sciences, Bhubaneswor, Odisha, India. She received a doctorate in botany from the Mal University, on M.Phil from Berhampur University, on ITC fellowship from the Hungarian Academy of Sciences, and o post-doctoral fellowship horn the Department of Biotechnology. Being a dynamic researcher, she hos on h.index of 17, her more than 30 scientific papers, review articles, 17 book chapter in reputed journals os well as publishing house have fetched citations of around 2413. Her expertise hos been much admired for which she was invited to deliver sessions in various scientific gatherings in India as well as abroad. She has co-authored the book "Remedial Biology' and co-edited book Nanomedicine Approaches towards Cardiovascular Disease'. To her credit, she has coauthored two patents, which hove acclaimed approval from the USA, Europe, Australian and another one from Indio. The patented technology was commercialized for "magnetic cell separation kit (Quicksort TM)'. She is serving as a reviewer for various Nano medicinal journals, as well as on associated editorial board member.

The author, Dr. Ajit Kumar Behura, is a senior faculty working in the Department of Humanities and Social Sciences, Indian Institute of Technology, Dhanbad-826004. He has earned his doctorate in philosophy from the Central University of Hyderabad. His main areas of teaching and research interests are applied ethics, environmental ethics, and ethics in scientific and technological research, engineering ethics, sustainable development and Indian philosophy. Under his guidance, 9 Ph.D. students were supervised in different areas of ethics and philosophy. He has 39 research publications in index journals. There are a number of training programs, consultancy and projects to his credit. He is a life member of several professional bodies.

Keywords:

Artificial intelligence, Nanomedicine, Nanotechnology, Target site, Cancer nanomedicine, Deep learning, Drug discovery, Machine learning, Robotics.

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Artificial Intelligence Centered Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics - EurekAlert

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Grace' is a lifelike robot nurse, built withartificial intelligence to bring emotional care for patients duringthe pandemic and make them feel comfortable and at ease.

Artificial intelligence (AI), the self-didactic technology whichdetects patterns from historical data, is pervading all walks oflife, be it healthcare or the financial services industry.

The High-Level Expert Group on AI, tasked by the EuropeanCommission to draft AI ethics guidelines, defined AI assystems that display intelligent behaviour byanalysing their environment and taking actions with somedegree of autonomy to achieve specificgoals''.

In the finance world, AI has evolved substantially over therecent decades and its utility ranges from the performance datamonitoring, establishing creditworthiness and credit scoring, aswell as in combatting cybercrime and money laundering. However, theexponential use does not come without a fair amount of risksattached, in particular in machine learning applications whererisks of data bias can lead to erroneous results being generated bythe AI due to statistical errors or interference during the machinelearning process.

The paucity in AI regulation and the multiplicity in AIpractices led the European Commission to focus on this technologyin its Digital Finance Package, launched at the end of 2020 toensure that the EU financial sector remains competitive whilecatering for digital financial resilience and consumerprotection.

Towards the end of last year, the European Central Bankpublished its opinion welcoming the Artificial Intelligence Act.While noting the increased importance of AI-enabled innovation inthe banking sector, given the cross-border nature of suchtechnology, the supranational body held that the ArtificialIntelligence Act should be without prejudice to the prudentialregulatory framework to which credit institutions are subject.

The ECB acknowledged that the proposal cross-refers to theobligations under the Capital Requirements Directive (2013/36 orCRD V') including risk management and governanceobligations to ensure consistency. Yet the ECB sought clarificationon internal governance and outsourcing by banks who are users ofhigh-risk AI systems.

Raising its concerns as to its role under the new ArtificialIntelligence Act, the ECB reiterated that its powers derive fromarticle 127(6) of the Treaty on the Functioning of the EuropeanUnion (TFEU) and the Single Supervisory Mechanism regulation (EU)1024/2013 (SSM regulation), which instruments confer on the ECBspecific tasks concerning prudential supervision policies of creditinstitutions and other financial institutions.

Recital 80 of the proposal provides thatauthorities responsible for the supervision andenforcement of the financial services legislation, including whereapplicable the European Central Bank, should be designated ascompetent authorities for the purpose of supervising theimplementation of this regulation, including for marketsurveillance activities, as regards AI systems provided or used byregulated and supervised financial institutions.

The bank held that market surveillance' under theArtificial Intelligence Act would also consist in ensuring thepublic interest of individuals (including health and safety). In anutshell, the ECB informed the Commission that the ECB has nocompetence to regulate solutions like Grace the robot, but it willonly ensure the safety and soundness of credit institutions. Tothis effect, the bank suggested that (i) a relevant authority beappointed for health and safety risks related obligations; and (ii)another AI authority be set up at Union level to ensureharmonisation.

In parallel, the ECB also recommended that the ArtificialIntelligence Act be amended so as to mandate that, that in relationto credit institutions evaluating the creditworthiness of personsand credit scoring, an ex-post assessment be carried out by theprudential supervisor as part of the SREP, in addition to theex-ante internal controls that are already listed in theproposal.

Interestingly, the Bank for International Settlements, in itsnewsletter on artificial intelligence and machine learning, raisedits concerns in view of the cyber, security and confidentialityrisks, data governance challenges, risk management, biases,inaccuracies and potential unethical outcomes of AI systems,the committee believes that the rapid evolution anduse of AI/ML by banks warrant more discussions on the supervisoryimplications.

While the Artificial Intelligence Act has not been agreed uponin its final form and may be substantially changed before itsacceptance, it is safe to say that the financial sector is one inwhich the challenges relating to the use of AI need to be evaluatedwell, before and when deploying such technological solutions, inview of the risks and individual rights that are at stake.

Originally Published by Times of Malta

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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ECB Publishes Its Bug Report On The Proposed EU Artificial Intelligence Act - New Technology - Malta - Mondaq

Vinita Gupta-

Vinita Gupta is a Silicon Valley Entrepreneur and was the first Indian-American woman to take her company public. Since retiring, she has propelled herself through her journalism, mentoring women entrepreneurs and playing competitive bridge at the highest levels. She has won several National titles in bridge.

The world is excited about Artificial Intelligence (AI).In the last 5000 years, starting with the invention of thewheel, machines have saved humans from existential threats despite our smaller-sized bodies.

Human civilization has evolved due to our superior skills in taking machines to the next level because machines did the kinds of work that humans could not.Consequently, we could become city dwellers with high rises.Mining became possible.Drilling for oil in the ocean became possible.

As we well know human ingenuity has been at play, which helped rationalize how we overcome threats and adversities.

Now we are taking our intelligence to the next level, by making machines more intelligent.That is the ultimate promise of Artificial Intelligence (AI), hoping machines will get smarter than we are.

Will that be good or bad?

What have we learned by becoming super trainers of machines?

Tesla has the most real-life experience with AI, when they put autonomous cars on the streets, equipped with drivers to train the cars.The starting point of self-driving cars is AI algorithms based neural networks similar to those found in the brain.

Say the goal is to have aneural networkrecognize photos that contain a dog. The concept of neural networks entails thatthe machines be not explicitly told what makes a dog.When the computer sees something furry, has a snout, and has four legs it may conclude that it is a dog.Then the machines are shown a lot of images of dogs more data. With minimal training by reinforcement learningwhen the machines are told when they make a mistake the computers start learning from their own mistakes and begin to recognize dogs more reliably.

Tesla has hundreds of thousands of hours of experience based on more than ten years of data collected from training autonomous cars. Based on his experience is why in2017 Elon Muskwarned a bipartisan gathering of U.S. governors that AI is afundamental risk to the existence of human civilization.

Elon now wants to focus on Tesla Bot instead, incorporating Teslas automotive artificial intelligence and autopilot technologies.A car is a Bot on steroids at those speeds. He thinks that a Bot should be perfected first.

Major Hurdles to AI:

1. Humans know only what decision an autonomous car has made, but not why.AI does not permit reverse engineering.Letting machines learn on the fly, on their own, is dangerous when it comes to life-and-death situations or what they might do in the future.

2. Machines by definition do not have common sense, which comes from lived experiences.These broad set of rules of thumb are impossible to be incorporated into machines.Common sense is essential for the robots to operate usefully and safely in the human environment.When a deer jumps in front of an autonomous car, the algorithm will not know what to do. It is even harder to teach machines to make moral or ethical decisions.

3. Intelligent machines will not know not to kill the human specie that helps them survive.Machines will never evolve as organisms do perthe theory of natural evolution.

The idea of Teslas autonomous autos, with current technology, can work for delivery trucks, but it needs infrastructure. Such trucks for example can use dedicated special lanes with barriers maybe only at night.There can be stations along the way where the drivers can hop in, for safe last-mile delivery to the warehouses inside the cities.During commute hours similar concept could be applied to carpoolers.This may not even require the expansion of freeways.

Similarly, smaller walking or even flying robots for making home deliveries sounds promising. On city streets, they can drive in special lanes dedicated to them, just like bike lanes.Integrating the concept with delivery hubs on major street corners may be a more practical solution.

With more people working remotely, and reduced delivery truck traffic on highways and city streets, AI can help us dramatically reduce the carbon footprint to save the environment.

Musk is also planning to introduce a home robot as a personal valet. Some people think it will eliminate hired home help.Another example of machines replacing human labor.

Last but not least, regulatory bodies need to start building expertise in AI, expediently.When in 2018 Facebook CEO Mark Zuckerbergtestifiedbefore a joint hearing in Congress to address steps the social network was taking in light of the Cambridge Analyticas connection with the 2016 presidential elections interference,it was scary to see how little older legislators knew about social media. This was more than 12 years after Facebook was open for general business beyond university campuses.

If we have AI development without regulatory oversight, we will pay a catastrophic price when applied to warfare.According toBill Gates, A.I. is like nuclear energy both promising and dangerous

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Artificial Intelligence : Revolutionary without being Evolutionary - indica News

Artificial intelligence in medicine is the use of machine learning models to search medical data and uncover insights to help improve health outcomes and patient experiences. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is quickly becoming an integral part of modern healthcare. AI algorithms and other applications powered by AI are being used to support medical professionals in clinical settings and in ongoing research.

Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Clinical decision support tools help providers make decisions about treatments, medications, mental health and other patient needs by providing them with quick access to information or research that's relevant to their patient. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs and other images for lesions or other findings that a human radiologist might miss.

The challenges that the COVID-19 pandemic created for many health systems also led many healthcare organizations around the world to start field-testing new AI-supported technologies, such as algorithms designed to help monitor patients and AI-powered tools to screen COVID-19 patients.

The research and results of these tests are still being gathered, and the overall standards for the use AI in medicine are still being defined. Yet opportunities for AI to benefit clinicians, researchers and the patients they serve are steadily increasing. At this point, there is little doubt that AI will become a core part of the digital health systems that shape and support modern medicine.

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Artificial Intelligence in Medicine | IBM