Category Archives: Artificial Intelligence

As we contemplate a future with AI in an African context, we need regulations that make sense for our continent and cultures, writes Zale Hechter.

We may think of Artificial Intelligence as some obscure, futuristic concept of the Fourth Industrial Revolution associated with scenes from "out-there" sci-fi movies likeStar WarsandTerminator. But AI is already here and being used in a wide variety of technological developments that make our daily responsibilities more efficient and convenient. In fact, scientists believe we are already 'cyborgs' because we already have a 'digital self', which lives into perpetuity.

Artificial intelligence (AI) is intelligence demonstrated by machines and contrasts with natural intelligence displayed by humans.Towards Data Sciencesays "Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that we would normally think would rely on the intelligence of a human."

Artificial intelligence is currently being used and employed in increasing measure across all spheres of business and industry. It is technology behind navigation and ridesharing apps you use when ordering a ride and the facial recognition functionality of digital banking. Digital assistants like Siri, Alexa and Google Assistance are all AI-powered and can take voice commands and translate them into actions. Incredibly, AI can also be used to do the chores we hate most: there is an AI-powered vacuum cleaner that scans a room, identifies things in its way and finds the quickest route to clean a space. AI is behind Netflix's personalised 'watchlists' as well as driverless vehicles.

As we contemplate a future with AI in an African context, it's interesting to consider the thoughts of prominent ex-South African, SpaceX and Tesla founder, Elon Musk. Musk is very close to the cutting edge of Artificial Intelligence, and he admits that 'it scares the hell out of me'.

READ |If machines can be inventors, could AI soon monopolise technology?

For efficiency, accuracy and agility in a digital economy and in order to have one that can trade and collaborate with other nations, AI is here to stay. Simply put, those industries and businesses that don't move with the times and fail to adopt AI are going to be left behind.

Musk highlights that AI is capable of vastly more than anyone knows, and the rate of improvement is exponential. The output of goods and services will be expedited, and there's the potential for goods and services to become more cost-effective. With automation will come abundance.

However, that abundance will come at a cost as Musk sees mass unemployment as a big social challenge of the future. He predicts that a universal basic income will be necessary as there will be fewer and fewer jobs that a robot cannot do better.

The difficulty then, of course, is how we find meaning as human beings? What would our purpose be if we are displaced by robots? How do we ensure the future is one we like and want?

Musk states that"We need to find some way of ensuring that the advent of digital super intelligence is one that is symbiotic with humanity. That's the single biggest existential crisis we face and the most pressing one".

AI in an African context

As I see it, Africa has a unique context and although current AI technology is readily available in digital banking, navigation and ride-sharing apps used on the continent, we urgently need to put regulations and policies in place that govern the use of AI in our businesses and societies.

Our greatest danger is that in our bid to keep up with the rest of the world, we leave our people without employment opportunities, having a catastrophic impact on our already fragile post-Covid-19 economic recovery.

Self-driving is rapidly developing and is becoming safer than a person driving on the road, resulting in substantial reductions in accidents. But what does the coming of autonomous vehicles in South Africa mean for our taxi industries, which are the most predominantly used mode of public transport in Africa? Would it put the industry out of business? How is our Department of Transport going to regulate their introduction to our roads? And how will the taxi industry react? Will loneliness and social disconnectedness increase if we rely on AI instead of human connections and relationships to survive and thrive?

READ |OPINION | Artificial intelligence is hijacking art history

The threat of AI to the African continent is that if we don't regulate how AI is used, it has the potential to extend and expand the digital divide by making humans redundant. Beyond that, AI can threaten the relationships that make us uniquely African.

Although part of being human and part of being alive is making mistakes, we need our modern-day workforce to strive for excellence, personalised service, and customer connection, which the best AI will never be able to replicate.

I think the job force needs to recognise that robots and technologies employing AI are, in fact, competitors to our human workforce. If bad service, serious inefficiencies, and human error cost businesses too much, they will no doubt prefer to implement AI solutions over human capital.

The importance of regulation

Musk, who is not normally an advocate for regulation, argues that regulation and oversight are critically important when it comes to AI. He argues this is a case where a very serious danger exists to the public and calls for public bodies that have insight and oversight to ensure AI is being developed safely. The danger of AI is much greater than nuclear warheads, he says. The regulator oversight is frighteningly lacking.

The importance of retaining our humanity

As I see it, AI should not strip us of the core of what makes us human.

The implementation and adoption of AI in an African context needs to take place in a symbiotic way that does not erode or diminish our African essence. Culturally in Africa, relationships and communities and the expression of our cultures and religion are incredibly important. We need to ensure that our rich heritage, culture and diversity are not destroyed or diminished by our desire for convenience and efficiency.

Likewise, AI should be developed with the mindset of uplifting people and building economies that support - and does not compete with - humanity.

- Zale Hechtor is theCEO of Cliqtech the company that created SmartWill.

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OPINION | Zale Hechter: Artificial intelligence is here to stay, and it will affect our future - News24

Dataset

Kvasir is a multi-class dataset from Brum Hospital in Vestre Viken Health Trust (Norway), collected from 2010 to 201424. Kvasir (v2) contains 8000 endoscopic images labelled with eight distinct classes, with approximately 1000 images per class, including ulcerative colitis. The images are assigned only image-level labels, provided by at least one experienced endoscopist as well as medical trainees (minimum of 3 reviewers per label). The images are independent, with only one image per patient.

Standard endoscopy equipment was used. HyperKvasir is an extension of the Kvasir dataset, collected from the same Brum Hospital from 2008 to 2016, containing 110,079 images, 10,662 of which are labelled with 23 classes of findings25. Pathological findings in particular accounted for 12 of 23 classes, which are aggregated and summarized in Table 1. They can be broadly grouped into Barrets esophagus and esophagitis in the upper GI tract, and polyps, ulcerative colitis, and hemorrhoids in the lower GI tract.

Importantly, the dataset includes 851 ulcerative colitis images which are labelled and graded using the Mayo endoscopic subscore26,27 by a minimum of one board certified gastroenterologist and one or more junior doctors or PhD students (total of 3 reviewers per image). The images are in JPEG format, with varying image resolutions, the most common being 576768, 576720, and 10721920. Table 2 shows the number of images available for each Mayo grade.

The HyperKvasir study, including the HyperKvasir dataset available through the Center for Open Science we are using here, was approved by Norwegian Privacy Data Protection Authority, and exempted from patient consent because the data were fully anonymous. All metadata was removed, and all files renamed to randomly generated file names before the internal IT department at Brum hospital exported the files from a central server. The study was exempted from approval from the Regional Committee for Medical and Health Research EthicsSoutheast Norway since the collection of the data did not interfere with the care given to the patient. Since the data is anonymous, the dataset is publicly shareable and complies with Norwegian and General Data Protection Regulation (GDPR) laws. Apart from this, the data has not been pre-processed or augmented in any way.

Two binary classification tasks were formulated from the dataset:

Diagnosis: All pathological findings of ulcerative colitis were grouped along with all other classes of pathological findings in the dataset (Fig.1a). The problem was formulated as a binary classification task to distinguish UC from non-UC pathology on endoscopic still images.

Methods (a) Overview of methods used to train diagnostic classification model of ulcerative colitis from multi-class endoscopic images on Kvasir datasets. (b) Overview of methods used to train diagnostic model for endoscopic grading of ulcerative colitis on HyperKvasir dataset.

Grading: Evaluation of disease severity using endoscopic images of UC pathology. Mayo graded image labels were binned into Grades 01 and 23. (Fig.1b) This grouping has been used in previous machine learning studies and for clinical trial endpoints19. Therefore, the task was to distinguish inactive/mild from moderate/severe UC.

A filter was designed to remove the green picture-in-picture depicting the endoscope. The filter applied a uniform crop to all images, filling in the missing pixels with 0 values, turning them black.

Source images were then normalized to [1, 1] and downscaled to 299299 resolution using bilinear resampling. Images underwent random transformations of rotation, zoom, sheer, vertical and horizontal flip, using a set seed. Image augmentation was only applied to training set images (not validation or test set), inside each fold of the fivefold cross-validation.

There are a growing variety of machine learning frameworks that could provide the foundation for our study. Our choices here acknowledge the current dominance of deep neural network methods, despite the emerging challenges of explainability (explainable artificial intelligence=XAI) and trust in practical clinical implementation41. Most of our choices use the most popular method for classifying images (convolutional neural networks), whose major differences lie in their depth of layering (50160) and recorded dimensionality of annotated relationships amongst segments of images (up to 2048).

The following four different CNN architectures were tested on the Kvasir dataset:

Pre-trained InceptionV3, a 159-layer CNN. The output of InceptionV3 in this configuration is a 2048-dimensional feature vector28.

Pre-trained ResNet50, a Keras implementation of ResNet50, a 50-layer CNN which uses residual functions that reference previous layer inputs29.

Pre-trained VGG19, a Keras implementation of VGG which is a 19 layer CNN developed by Visual Geometry Group30.

Pre-trained DenseNet121, a Keras implementation of DenseNet with 121 layers31.

All pre-trained models were TensorFlow implementations initialized using ImageNet weights32.Training was performed end-to-end with no freezing of layers. All models performed a final classification step via a dense layer with one node. Sigmoid activation was used at this final dense layer, with binary cross entropy for the models loss function.

For both classification tasks, the final dataset was randomly shuffled and split into training and validation sets in a 4:1 ratio, where 80% images were used for fivefold cross-validation and 20% unseen images were used for evaluating model performance. The best model from each fold were combined and used as the final model for prediction on the test set.

Hyperparameters were fine-tuned using Grid Search, where the search space included the following parameters: optimizers: Adam, Stochastic Gradient Descent (SGD), learning rate: 0.01, 0.001, 0.0001; momentum (for SGD): 0, 0.5, 0.9, 0.99. For all models, training phases consisted of 20 epochs with batch size of 32.

Models were evaluated using accuracy, recall, precision, and F1-scores. As a binary classification problem, confusion matrices and ROC curves were used to visualize model performance.

To provide visual explanation of what the models are learning, we chose the Gradient-weighted Class Activation Mapping (Grad-CAM) technique33. Grad-CAM produces a heatmap for each model output, showing which part(s) of the image the model is using to make predictions (produces the strongest activation). The heatmap is a course localization map produced by using gradient information flowing into the last convolutional neural network layer, to assign importance values to each neuron.

We also had an experienced gastroenterologist (D.C.B.) annotate and highlight the regions of interest in representative images to provide a comparison with the regions of interest generated by the heatmaps.

Model building was performed and figures created was done using TensorFlow and Keras packages32 in Python 3.6.9, run on Google Colab (https://research.google.com/colaboratory/) notebook.

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Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images | Scientific Reports - Nature.com

Artificial Intelligence

Human-Centered Artificial Intelligence (HCAI) is a concept that seems to put human usage and access of AI technology at the forefront. To me, it seems in opposition to the data driven vision of some pundits, though there is the ability to differentiate between goals and development. Human-Centered AI, by Ben Shneiderman, is an excellent introduction to the concepts of HCAI. Be aware, though, that this isnt a breezy, short, book aimed at quick review. This is a business school textbook. For management interested in governance and control, focus on part four of the book, discussed towards the end of this article. The section should be a must-read, even if you skim the rest.

That point is important so as not to surprise people. The books audience should be business personnel and students wanting a strong introduction to the issues of HCAI showing concepts that should then be drilled down into practice. It is for upper- and middle-management in the CIO, CTO, R&D and other more technical realms of an organization. The text is 376 pages in a font smaller than the usual business book. Give that content, this review will remain at a higher level than many of the book reviews in this column.

Theres an important thread running through the book. The author differentiates two different research lenses that can be used, that of science and innovation. The science approach is focused on what is possible from a technical view. Why it is being done doesnt matter. On the other hand, Ben Shneiderman points to the innovation view, that of understanding how a technology can provide innovation in the real world. HCAI is driven from the innovation perspective.

As much as I like this book, it isnt perfect. The big problem early is in chapter four, and that chapter should be skimmed or skipped. In it, the author presents another academic technologists view that the AI revolution is similar to the industrial revolution and makes the same mistake many do in claiming jobs wont be lost. The industrial revolution took people from farms and crafts into simple shop floors. Over generations, those shop floors became more complex, but it was a stepwise advancement. Artificial intelligence isnt that. It will take over jobs with no similar positions to fill. The gap between many of the disappearing jobs and the remaining ones are much larger than during the industrial revolution.

He also states that automation lowers cost and improves quality. The first, yes. The second is very arguable. That, however, is a discussion for another day.

Back to what I like. Chapter eight focuses on the authors two dimensional view of human and automation controls, how they will overlap. There are some excellent examples.

Chapter 12 is a key for understanding the science v innovation views mentioned above. While the discussion runs through the book, this chapter focuses on it in a clear way. It also discusses why the innovation view requires understanding and explainability of AI systems.

Social robots are described and discussed in detail in chapter 16. While it is a good survey of options, I do think the author misses one critical point. He points out that surveys over the years show people interested in anthropomorphic robots, with the feedback implying those robots arent yet good enough. Then he states, in softer words, the opinion that they will never be good enough. Too many opinions over the years have stated because AI hasnt yet reached point X, theyll never reach point X. Thats a stretch.

The same chapter points to what the author describes as supertools, functional devices as the alternative. They avoid the anthropomorphic trap to create usable devices with responses that are accepted. They are useful, and clearly a segment of devices that will remain; but chatbot research has also shown improving technology, creating more acceptance as long as people know they are talking with a chatbot.

For business managers and government employees who wish to better understand the organizational impact of AI at multiple levels, part four is the meat of the matter. The author defines four levels of governance:

Software development

Corporate policy

Industry & trade standards

Governmental regulations

While the entire book is a good overview of HCAI, much of it is aimed at a mid-tier management, and even development manager, level of focus. The explanation of the four levels is something that is important to all levels in companies, industry and government. The types of governance arent independent, and people must be aware of how they integrate. For instance, if software developers arent paying attention to social demands, they wont be prepared for government that could lay waste to expenditures of time and money. In the opposite direction, there is not much use of creating industry standards and government regulations to arent directly applicable to the technology.

That means government officials hiring people to better educate them in whats possible. Long before the recent hearings on social media, and even before the famous statement by Senator Ted Stevens about the internet being as series of tubes, legislators have wanted to help citizens but not understood the implications of the technology and how best to address it.

It also means that corporate policy is critical, as it must be the bridge between development and the real world. Human-centered AI is an important concept. This book is a heavy introduction, and many parts of it will be useful to different audiences. Students, in academia and business, can read it all, but it is still valuable to management who need to both understand how to better direct AI development and to require appropriate AI to solve market and social challenges.

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Human-Centered AI, An In-Depth Study Of The Current State Of The Artificial Intelligence Concept - Forbes

Artificial intelligence is a transformative technology that impacts every aspect of businesses globally in key business decisions. Most US businesses are faced with the daunting challenge of understanding AI and its adoption. As a result, AI and analytics professionals are in ever-increasing demand as the technology is integrated into business systems.

To understand why AI is becoming so popular, the impact of AI and the field of AI and data science as a career choice, the University of Louisville College of Business is organising a webinar on 23rd of February 2022 from 7:30 to 8:30 PM to discuss ways in which one can manage their career in the age of AI.

Jeff Guan is a Professor of Computer Information Systems in the College of Business, University of Louisville. He holds a doctoral degree in computer engineering and is the creator and director of the Master of Science in Business Analytics program at UofL. His research interests include accounting information systems, machine learning, knowledge management, and information systems implementation and adoption.

He has published and presented about 100 papers in the above areas and teaches courses in database, artificial intelligence, data warehousing, and information systems analysis and designboth at the undergraduate and graduate levels. He also has extensive consulting experience with private and government organisations, such as Brown-Forman, GE Appliances, Yum!, Department of Defense, and Kentucky State Government.

Swan is an experienced international education professional with demonstrated leadership in international student services, international programming, campus internationalisation, and international student recruitment. He will walk the participants through the graduate admission process, discuss internship opportunities, and offer answers to any questions they might have.

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Managing your career in the age of artificial intelligence: Register for this webinar - Analytics India Magazine

Unusual applications of Artificial Intelligence are less commonly discussed but still has significance

Artificial Intelligence is leading humans towards digital transformation. From weather predictions, stock market crashes, drug discovery, to understanding customer data, better filling systems, artificial intelligence is ruling every industry. Artificial Intelligence is capable of achieving enormous feats in the modern world, but it does sound weird when you hear about an AI system that can test beer or AI perfumers and Chef AI. This article features the top 10 unusual applications of artificial intelligence that are in use today.

With the integration of Artificial Intelligence and a wide array of technological advancements, one can use the power of AI to uncover hidden truths about the Universe, which would otherwise be deemed as impossible. To discuss some of the unusual applications of Artificial Intelligence in exploring the Universe, it was found that the Generative Adversarial Networks can create mappings of the Universe extremely similar to the realistic expectations of what humans have hoped for in their theoretical visions.

IBM research is collaborated with a German Fragrance House Symrise to introduce AI perfumers in the perfume industry. In June, an AI perfume created by a computer system was launched on the Brazilian market. Artificial intelligence involved combined ingredients in a manner unthinkable to most humans. It is one of the best unusual applications of artificial intelligence that are in use today.

San Franciscos Creator uses a combination of AI and robotics for every step of the burger-making process, i.e., grinding beef, frying patties, toasting buns, dispensing condiments, adding tomatoes, onions, and pickles, and even assembling burgers. Meanwhile, MIT students and researchers have built an AI system that generates new pizza recipes. Chef AI is the future of the food industry and also one of the best unusual applications of artificial intelligence that are in use today.

Scientists worked to develop an artificial bee with an incorporated AI system that mimics. The bee drones come with a GPS to locate a specific location and a High-resolution Camera working similar to the eye of a honeybee.

AI helps in better-removing germs and protecting teeth against bacteria that even stay after regular brushing. The scrubbers on AI-Brush are trained for several brushing styles to support thousands of people. Besides, the AI system also comes with a deep learning algorithm that remembers the brushing behavior of a particular user and adopts its nature. After learning, it provides the user with the same brushing behavior based on his usage habit.

Fashion retailers have turned to AI to make their businesses more efficient, replace photoshoots and predict what people will want to buy and wear in the future. AI can map clothes onto peoples bodies these can either be models or potential customers who upload their own photos to an app.

Dont be surprised to know that the hit song you are enjoying will be written with the help of AI. In fact, its already been done: In 2017, Alex Da Kids single, Not Easy, performed with Elle King, X Ambassadors, and Wiz Khalifa, made both the Billboard and iTunes charts. AI for lyrics writing is one of the unusual applications of Artificial Intelligence that are in use today.

Artificial Intelligence can create a video game, music, screenplays, novel and poetry and the next a movie custom-built to the individual viewer. Machine learning and artificial intelligence algorithms can be utilized to create new scripts or write synopsis and characters for movies. It is one of the unusual applications of Artificial Intelligence that are in use today.

Whether you are a firm believer of Astrology, Numerology, or any other similar field, or you dont really care about the existence of such fields, it is fascinating to explore what impact Artificial Intelligence would have on these subjects matters. Alexander Reben, an artist and MIT-trained roboticist, built a system that generates one-line predictions by training a neural network on the messages found in thousands of fortune cookies and thousands of inspirational expressions he scraped off the Internet.

Beauty AI is the new judge of the beauty contest that will perceive you on how they see you and does not judge on how they feel about you. Beauty.AI is an artificial intelligence solution that can perceive human beauty by how healthy someone is neither by age nor by nationality. It is one of the unusual applications of Artificial Intelligence that are in use today.

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Top 10 Unusual Applications of Artificial Intelligence in Use - Analytics Insight

Our world is increasingly technology centric, offering unlimited opportunities with just the click of a button. As a result, artificial intelligence (AI), which aims to create technology with human-like problem-solving and decision-making capabilities, is becoming part of our daily lives, be it through voice controlled personal assistants, smart cars, or automated investing platforms. The use of AI gives rise to an array of societal, economic, and legal issues causing the European Commission to include AI in its digital strategy and propose the first legal framework on AI in an attempt to encourage the uptake of AI and address the risks associated with its uses.

In April 2021 the EU Commission published its draft proposal for a Regulation laying down harmonised rules on Artificial Intelligence (the Proposal). The objectives of the Proposal are to:

(a) facilitate the development of a single market for lawful, safe and trustworthy AI applications and prevent market fragmentation.

(b) guarantee legal certainty to facilitate investment and innovation in AI;

(c) enhance governance and effective enforcement of existing law on fundamental rights and safety requirements applicable to AI systems;

(d) safeguard and respect existing law on fundamental rights and European values;

The Proposal defines AI systems as software that is developed with machine learning, logic-and knowledge-based approaches, and/ or statistical approaches and can, for a given set of human defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with. The Proposal puts forward rules for the development, placement on the market and use of AI systems following a human-centric and risk-based approach. Risk is measured in relation to the purpose of the AI system similar to product safety legislation.

SCOPE

The scope of application of the Proposal is broad and will have extraterritorial effect. The Proposal is intended to apply to (i) providers placing on the market or putting into service AI systems in the EU, irrespective of whether they are established within the EU or in a third country; (ii) users of AI systems located within the EU; and (iii) providers and users of AI systems located outside of the EU, where the output produced by the system is used in the EU.

AI systems used exclusively for military purposes are expressly excluded from the scope of the Proposal together with those used by public authorities or international organisations in a third country to the extent such use is in the framework of international agreements for law enforcement and judicial cooperation with the EU or individual Member States.

PROHIBITED ARTIFICIAL INTELLIGENCE PRACTICES

The Proposal lists several AI system types which are considered to create an unacceptable level of risk and are prohibited. These include:

(a) AI systems that deploy subliminal techniques beyond a persons consciousness to materially distort a persons behaviour in a manner that causes or is likely to cause physical or psychological harm;

(b) AI systems that exploit any of the vulnerabilities of a specific group of persons in order to materially distort the behaviour of a person pertaining to that group in a manner that causes or is likely to cause physical or psychological harm;

(c) AI systems placed on the market or put into service by public authorities or on their behalf for the evaluation or classification of the trustworthiness of natural persons over a certain period of time based on their social behaviour or known or predicted personal or personality characteristics, with the social score leading to detrimental or unfavourable treatment of persons either in social contexts which are unrelated to the contexts in which the data was originally generated or collected and/or disproportionate to their social behaviour or its gravity;

(d) the use of real-time remote biometric identification systems in publicly accessible spaces for the purpose of law enforcement, unless and in as far as such use is strictly necessary for reasons such as targeted search for specific potential victims of crime, prevention of specific, substantial and imminent threat of a terrorist attack, detection, identification and prosecution of a perpetrator or suspect of a criminal offence in relation to whom a European arrest warrant is in place.

HIGH-RISK SYSTEMS

AI systems identified as high-risk are only permitted subject to compliance with mandatory requirements and a conformity assessment. High-risk AI systems include:

(a) AI systems intended to be used for the real-time and post remote biometric identification of natural persons;

(b) AI systems intended to be used for the purpose of determining access or assigning natural persons to educational and vocational training institutions;

(c) AI systems intended to be used for recruitment or selection of natural persons, notably for advertising vacancies, screening or filtering applications, evaluating candidates in the course of interviews or tests;

(d) AI systems intended to be used by public authorities or on behalf of public authorities to evaluate the eligibility of natural persons for public assistance benefits and services, as well as to grant, reduce, revoke, or reclaim such benefits and services;

(e) AI systems intended to be used by law enforcement authorities as polygraphs and similar tools or to detect the emotional state of a natural person;

(f) AI systems intended to assist a judicial authority in researching and interpreting facts and the law and in applying the law to a concrete set of facts.

High-risk AI systems will give rise to obligations relating to data quality and governance, documentation and record keeping, conformity assessment, transparency and provision of information to users, human oversight, robustness, accuracy and security.

The Proposal envisages an EU-wide database for stand-alone high risk AI systems with fundamental rights implications to be operated by the European Commission and provided with data by the providers of the AI systems prior to placing them on the market or otherwise putting them into service.

Providers of high risk AI systems will be required to establish and document a post-market monitoring system and to collect, document and analyse data on the performance of the AI system and report serious incidents and malfunctioning to the market surveillance authorities.

LOW RISK AND MINIMAL RISK SYSTEMS

AI systems which do not qualify as prohibited or high-risk systems are not subject to AI specific requirements other than disclosing the use of AI to the users. However, they could choose to adopt a code of conduct with a view to increasing their AI systems trustworthiness.

ENFORCEMENT AND SANCTIONS

The Proposal provides for a two-tier AI governance system at European Union and national level. At Union level, it is envisaged that an Artificial Intelligence Board composed of representatives of the Member States and the Commission will be created to facilitate harmonised implementation and cooperation. At national level, each Member State will be expected to designate one or more national competent authorities and a national supervisory authority.

It is envisaged that non-compliance of an AI system with requirements or obligations provided for in the Proposal will result in, among other sanctions, administrative fines. The Proposal sets out three sets of maximum thresholds for administrative fines that may be imposed for relevant infringements ranging from the higher of 30m or 6% of the total worldwide annual turnover of the offender to the higher of 10m or 2% of the worldwide annual turnover of the offender.

CONCLUSION

The Proposal is still at the early stages of the European legislative process. Its human-centric, risk-based approach and obligations relating to record keeping, transparency and reporting as well as the introduction of significant administrative fines evoke parallels with the European General Data Protection Regulation. The definition of an AI system as well as the approach to determining the category of risk of an AI system have already been criticised as requiring extensive analysis and having the potential to add significant compliance costs for users and providers. It remains to be seen whether the Proposal will meet with the support of the European Parliament and of the Council and whether it will be successful in setting a new global standard for AI.

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Proposed EU Framework on Artificial Intelligence - everything you need to know - Lexology

Artificial intelligence can be used to identify the secrets of the universe and understand the big bang

Artificial intelligence has entirely transformed many areas of our daily lives, both in the professional and personal aspects. Starting from healthcare to transport, several industrial tasks are now being carried out by computers or advanced robots in a swifter and more efficient fashion. These machines can carry out more dangerous and possibly hazardous jobs that are there in the industry, with minimum errors. The robots can enter unbreathable environments, such as areas that require deep-sea diving, making ceratin processes much safer and faster. All in all, AI has successfully improved the way we perceive our professional and personal environments. Recently, AI researchers have also discovered that integrating machine learning and neural networks can help uncover and clear up several secrets of the big bang theory, the deep mysteries of the universe.

In recent years, Artificial Intelligence has become much more accessible than what humans perceive. With the development of powerful tools like deep learning, it has become quite easier and seamless to teach computers to perform tasks without being explicitly programmed. Deep learning has transformed fields such as speech recognition, NLP, computer vision, and other technologies. Researchers and scientists can now use this technology to study the universe, and develop algorithms from the data gathered through space telescopes to better understand the formation of galaxies.

Big bang can be efficiently studied with the help of high-performance computers and highly complex computer simulations whose results are quite difficult to evaluate. Integrating neural networks can tie the mathematical prospects of space physics with logic and unravel several secrets. Neural networks are used especially for image recognition. Besides, with the help of such neural networks, it becomes possible to make predictions about the systems. These deep learning models will help astronomers see the concepts and technologies behind the incidents taking place in the universe. Furthermore, a hybrid convolutional neural network model, based on deep residual networks is presented. With the help of Artificial Intelligence, scientists can also explore the mapping of the universe and identify distant objects in it.

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Can Artificial Intelligence Unravel the Mysteries of the Big Bang Theory? - Analytics Insight

GAO's managing director Cathleen Berrick: 'Business as usual for DoD is really a losing proposition'

WASHINGTON The U.S. Government Accountability Office in a new report says the Defense Department has to be better prepared to respond to Chinas advances in space, cyberwarfare and artificial intelligence.

Successful preparation for strategic competition with China will depend on continuing efforts to increase U.S. combat credibility and enhance conventional deterrence that can help prevent conflict, protect U.S. interestsand assure allies, GAO said in the report titled Challenges Facing DoD in Strategic Competition with China.

The three-page summary GAO published Feb. 15 is the unclassified version of a much more extensive report that is classified.

Going forward, said GAO, the U.S. defense Department should be preparedto maintain supply chains, gather intelligence, and responsibly leverage emerging space, cyber, and AI technologies in response to potential threats.

The watchdog agency suggested that Congress will need to pay close attention to DoDs efforts in these areas and whether DoD takes timely actions.

Cathleen Berrick, GAOs managing director of defense capabilities and management, said DoD has taken some steps to invest and innovate in response to Chinas leaps in cybersecurity, space and the use of artificial intelligence. But more could be done, she said.

With regard to space, GAO in the report listed a number of recommendations it has issued in the last several years such as the need for DoD to revamp its satellite-based communications architecture and ground-based systems for the command and control of satellites. These are actions that may better position DoD to address the challenges with China but DOD has not yet implemented.

Recent reports that a Chinese satellite actually grabbed another Chinese satellite and pulled it out of its orbit really demonstrates a significant leap in anti-satellite capabilities, said Berrick.

Space is very important because DoD, of course, relies on its space based capabilities for communications, for navigation and targeting, and also for intelligence collection. And China knows this and is actively developing systems that could counter these capabilities.

I think its important for Congress and DoD to continue to have this China pacing threat reality at the forefront of their thinking because business as usual for DoD is really a losing proposition, Berrick said. This means theyre going to have to figure out how to adapt everything the department does away from the current industrial age approach to something more suitable for the information age.

Berrick said it would not be an understatement to say that strategic competition with China is unlike any other challenge DoD has faced.

Read more from the original source:

GAO: DoD has to step up efforts in space, cyber and artificial intelligence to compete with China - SpaceNews

A new opportunity for WPI students to bolster their understanding of diversity, equity, and inclusion in Artificial Intelligence (AI) is coming to WPI. Through AI4ALL, a national program supported by Melinda Gates Pivotal Ventures, students will be able to take a series of classes on the ethics of AI, collaborate with industry partners, and gain access to AI4ALLs alumni network, to pursue internships and jobs. Students will also receive a certificate upon completion of the program.

Associate Professor of Computer Science Rodica Neamtu spearheaded the effort to bring AI4ALL to WPI and will lead the program. AI4ALL seeks to help students become different kinds of leaders, says Neamtu. Leaders who are not just knowledgeable scientists invested in the work, but also have a deep understanding of the social and ethical implications of their work.

Applications for the program open in late March, with the first module scheduled to begin in fall 2022. Information sessions will be held leading up to the application deadline.

Read more:

Nationally Recognized Diversity Equity and Inclusion in Artificial Intelligence Program Coming to WPI - WPI News

Illustrative photo/rawpixel.com

Sonantic startup engineers taught artificial intelligence incredible speech realism thanks to the introduction of very important changes in the program. The technology is already being used in games.

The technology that imitates the human voice was shown on February 14, teaching how to confess one's love. It is simply impossible to distinguish it from a real person.

Demonstration of the work of artificial intelligence: video

The artificial voice in the video sounds quite natural and very realistic, especially the sigh and laughter that fit perfectly into her speech. That's why it's so surprising when she suddenly confesses: I'm not real. I was never born. And I'll never die. Because I don't exist.

Remind. Startup Sonantic announced the creation of a neural network to generate speech with imitation of human emotions in 2020. For two years, the developers have made great progress. In the first commercials, IS's voice did not sound very natural, and the speech was accompanied by distortion. The company is already cooperating with game studios. Her work was used by Obsidian Entertainment to generate the voices of some minor characters in The Outer Worlds. In August 2021, a startup used technology to replicate the voice of actor Val Kilmer, who lost the ability to speak as a result of laryngeal cancer.

Excerpt from:

The realism of artificial intelligence has reached a new level - The Times Hub