Category Archives: Artificial Intelligence

By the times that A.I. comes back and tells you that, then we have reached artificial general intelligence, and you should be very scared or very excited, depending on your point of view, Dr. Tegmark said. The reason Im working on this, honestly, is because what I find most menacing is, if we build super-powerful A.I. and have no clue how it works right?

Dr. Thaler, who directs the new institute at M.I.T., said he was once a skeptic about artificial intelligence but now was an evangelist. He realized that as a physicist he could encode some of his knowledge into the machine, which would then give answers that he could interpret more easily.

That becomes a dialogue between human and machine in a way that becomes more exciting, he said, rather than just having a black box you dont understand making decisions for you.

He added, I dont particularly like calling these techniques artificial intelligence, since that language masks the fact that many A.I. techniques have rigorous underpinnings in mathematics, statistics and computer science.

Yes, he noted, the machine can find much better solutions than he can despite all of his training: But ultimately I still get to decide what concrete goals are worth accomplishing, and I can aim at ever more ambitious targets knowing that, if I can rigorously define my goals in a language the computer understands, then A.I. can deliver powerful solutions.

Recently, Dr. Thaler and his colleagues fed their neural network a trove of data from the Large Hadron Collider, which smashes together protons in search of new particles and forces. Protons, the building blocks of atomic matter, are themselves bags of smaller entities called quarks and gluons. When protons collide, these smaller particles squirt out in jets, along with whatever other exotic particles have coalesced out of the energy of the collision. To better understand this process, he and his team asked the system to distinguish between the quarks and the gluons in the collider data.

We said, Im not going to tell you anything about quantum field theory; Im not going to tell you what a quark or gluon is at a fundamental level, he said. Im just going to say, Heres a mess of data, please separate it into basically two categories. And it can do it.

View original post here:

Can a Computer Devise a Theory of Everything? - The New York Times

Security and food safety

Many factories are introducing smart machinery, taking advantage of the benefits that robots on the production line, connected devices and predicative maintenance offer. However, smart devices require an internet connection and anything with an internet connection can be hacked, potentially leading to data loss or comprising the safety of the final product.

Who bears contractual responsibility in this event? You? The supplier? The AI itself? There is no clear answer to this, but in our view the responsibility for the actions of a smart device will likely lie with the operator.

We eventually see the law automatically placing liability on the supplier in certain circumstances, such as where it fails to ensure that its algorithms are free from bias or discrimination.

Intellectual property and data

The optimisations AI can lead towards are the product of the data that the machine has learnt from. For the AI tool to tell your business how to optimise its production process or reduce its wastage, first you too must hand over valuable information.

The AIs learnings can now be applied to your business but the AI tool now has another data point: yours. And there is nothing to stop its owner going to a competitor of yours and teaching it efficiencies based on its newly expanded (thanks to your business) pool of data.

How can we stop data spreading to competitors?! How do you share the gains fairly between the two organisations? These are issues that must be documented carefully in your contracts as the current law does not yet provide a clear answer.

As AI gets more advanced, it may begin to create new ideas recipes or methods of production requiring less and less human input to do so. Who would own the intellectual property rights in new inventions created solely by AI?

The legal community is still carefully considering the ownership of IP developed by non-humans, but early-adopters of these technologies should be contemplating the ownership question and documenting it in their contracts now.

In the UK, the regulatory issues surrounding AI are still being debated and different bodies have different views. Some believe regulation is urgently needed, whilst others consider that the technology needs to be more widely deployed before rules dictating its use can be drafted. What is clear, though, is the need to take a holistic approach.

The legal implications of AI cannot be looked at in silos for instance only from a data protection perspective or only from an antitrust perspective any regulation of AI must be reviewed as a whole and the risks and benefits carefully weighed.

This is particularly true for the use of AI technologies in the food manufacturing industry where consumer safety is at stake. It may be too early to build laws controlling AI tools used to manufacture consumer goods, but the consequences of AI getting it wrong could be highly damaging and result in the industry rejecting AI completely, despite its many benefits.

Until the law does catch up, make sure to read the small print on security policies, adhere to best practice information management processes and document agreed terms clearly with suppliers.

Excerpt from:

Reaping the benefits of artificial intelligence - FoodManufacture.co.uk

BOSTON and PARIS, Nov. 24, 2020 /PRNewswire/ --Shift Technology, a provider of AI-native fraud detection and claims automation solutions for the global insurance industry today announced its fraud detection technology has been selected by digital native neo-insurance company Luko.

Since its launch in May 2018, Luko has been forging a new path in the world of homeowners insurance. This pioneering new insurance company employs patented technology which predicts which claims may be filed (water damage, fire, etc.) and convinces policyholders to adopt best practices in terms of prevention. In cases where claims cannot be avoided, Luko relies on technology to shorten the claims process and provide its customers with an exemplary customer experience.

Ensuring that claims are legitimate is a critical component of ensuring a fast, efficient, and accurate claims process. However, Luko's market success and rapid growth exposed that the existing procedures used to detect potential fraudulent claims simply could not keep up. As a result, Luko turned to Shift Technology and its award-winning AI-based insurance fraud detection solution.

"The insurance sector is the target of numerous attempts at fraud, whether opportunistic or resulting from organized crime networks," explained Raphal Vullierme, co-founder of Luko. "It was therefore essential that we continue to reinforce our processes and technologies in terms of fraud detection, so as to quickly identify potentially illegitimate claims."

In addition to the fraud detection technology offered by Shift, Luko is supported by the deep insurance industry experience and experience of its data science teams. This strong combination of people and technology help to ensure Luko is always staying abreast of the latest fraud trends and schemes.

"We have always considered the fight against fraud to be a critical topic for insurers," stated Jeremy Jawish, CEO and co-founder, Shift Technology. "Not only does effective fraud fighting reduce undeserved indemnity pay-outs and dismantle fraud networks, but also supports the digital transformation of the customer journey."

About Shift TechnologyShift Technology delivers the only AI-native fraud detection and claims automation solutions built specifically for the global insurance industry. Our SaaS solutions identify individual and network fraud with double the accuracy of competing offerings, and provide contextual guidance to help insurers achieve faster, more accurate claim resolutions. Shift has analyzed hundreds of millions of claims to date and was presented Frost & Sullivan's 2020 Global Claims Solutions for Insurance Market Leadership Award. For more information please visit http://www.shift-technology.com.

About LukoLuko is reinventing home insurance, placing social responsibility and technology at the heart of its priorities. The company is now the first neo-insurance firm in France, with more than 100,000 policyholders, and the Insurtech with the strongest growth in Europe. More than a simple insurance contract, Luko's ambition is for insurance to change from a model activated as a reaction to a model based on prevention, using internally-developed technology. The co-founders Raphal Vullierme, a serial entrepreneur, and Benoit Bourdel, have pooled their expertise to create a company with a specific, positive impact, recognized by Bcorp certification in July 2019.

Contacts:Rob MortonCorporate CommunicationsShift Technology+1.617.416.9216[emailprotected]

SOURCE Shift Technology

https://www.shift-technology.com

Continue reading here:

Luko and Shift Technology Apply Artificial Intelligence to the Fight Against Fraud - PRNewswire

POTOMAC, Md., Nov. 23, 2020 /PRNewswire/ --Z Advanced Computing, Inc. (ZAC), the pioneer Explainable-AI (Artificial Intelligence) software startup, was included by Forrester Research, Inc. in their prestigious report: "New Tech: Responsible AI Solutions, Q4 2020" (November 23, 2020, at https://go.Forrester.com/). Forrester Research is a leading global research and advisory firm, performing syndicated research on technology and business, advising major corporations, governments, investors, and financial sectors. ZAC is the first to demonstrate Cognition-based Explainable-AI (XAI), where various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle. "With our superior algorithms, complex 3D objects can be recognized from any direction, using only a small number of training samples," said Dr. Saied Tadayon, CTO of ZAC. "You cannot do this with the other techniques, such as Deep Convolutional Neural Networks (CNN), even with an extremely large number of training samples. That's basically hitting the limitations of CNNs, which others are using now," continued Dr. Bijan Tadayon, CEO of ZAC. "For complex tasks, such as detailed 3D image recognition, you need ZAC Cognitive algorithms. ZAC also requires less CPU/ GPU and electrical power to run, which is great for mobile or edge computing," emphasized Dr. Saied Tadayon.

Read the original:

Explainable-AI (Artificial Intelligence - XAI) Image Recognition Startup Included in Responsible AI (RAI) Solutions Report, by a Leading Global...

In August, the young artificial intelligence process automation company Intelenz, Inc. announced its first U.S. patent, an AI-enabled software-as-a-service application for automating repetitive activities, improving process execution, and reducing operating costs. For company co-founder Renzo Zagni, the patent is a powerful testament to the value of his MIT educational experience.

Over the course of his two-decade career at Oracle, Zagni worked his way from database administrator to vice president of Enterprise Applications-IT. After spending seven years in his final role, he was ready to take on a new challenge by starting his own company.

From employee to entrepreneur

Zagni launched Intelenz in 2017 with a goal of keeping his company on the cutting edge. Doing so required that he stay up to date on the latest machine learning knowledge and techniques. At first, that meant exploring new concepts on his own. But to get to the next level, he realized he needed a little more formal education. Thats when he turned to MIT.

When I discovered that I could take courses at MIT, I thought, What better place to learn about artificial intelligence and machine learning? he says. Access to MIT faculty was something that I simply couldnt pass up.

Zagnienrolled in MIT Professional Educations Professional Certificate Program in Machine Learning and Artificial Intelligence, traveling from California to Cambridge, Massachusetts, to attend accelerated courses on the MIT campus.

As he continued to build his startup, one key to demystifying machine learning came from MIT Professor Regina Barzilay, a Delta Electronics professor in the Department of Electrical Engineering and Computer Science and a member of MITs Computer Science and Artificial Intelligence Laboratory. Professor Barzilay used real-life examples in a way that helped us quickly understand very complex concepts behind machine learning and AI, Zagni says. And her passion and vision to use thepower of machine learning to help win the fight against cancer was commendable and inspired us all.

The insights Zagni gained from Barzilay and other machine learning/AI faculty members helped him shape Intelenz early products and continue to influence his companys product development today most recently, in his patented technology, the "Service Tickets Early Warning System. The technology is an important representation of Intelenz ability to develop AI models aimed at automating and improving business processes at the enterprise level.

We had a problem we wanted to solve and knew that artificial intelligence and machine learning could possibly address it. And MIT gave me the tools and the methodologies to translate these needs into a machine learning model that ended up becoming a patent, Zagni says.

Driving machine learning with innovation

As an entrepreneur looking to push the boundaries of information technology,Zagni wasnt content to simply use existing solutions; innovation became a key goal very early in the process.

For professionals like me who work in information technology, innovation and artificial intelligence go hand-in-hand, Zagni says.

While completing machine learning courses at MIT, Zagni simultaneously enrolled in MIT Professional Educations Professional Certificate Program in Innovation and Technology. Combining his new AI knowledge with the latest approaches in innovation was a game-changer.

During my first year with MIT, I was putting together the Intelenz team, hiring developers, and completing designs. What I learned in the innovation courses helped us a lot, Zagni says. For instance, Blake Kotellys Mastering Innovation and Design Thinking course made a huge difference in how we develop our solutions and engage our customers. And our customers love the design-thinking approach.

Looking forward

While his progress at Intelenz is exciting, Zagni is anything but done. As he continues to develop his organization and its AI-enabled offerings, hes looking ahead to additional opportunities for growth.

Were already looking for the next technology that is going to allow us to disrupt the market, Zagni says. Were hearing a lot about quantum computing and other technology innovations. Its very important for us to stay on top of them if we want to remain competitive.

He remains committed to lifelong learning, and says he will definitely be looking to future MIT courses and he recommends other professionals in his field do the same.

Being part of the MIT ecosystem has really put me ahead of the curve by providing access to the latest information, tools, and methodologies, Zagni says. And on top of that, the faculty are very helpful and truly want to see participants succeed.

See the original post here:

Staying ahead of the artificial intelligence curve with help from MIT - MIT News

November10, 20204 min read

Artificial intelligence is revolutionizing various industries, markets, and services. However, the creative industries and the art world have not yet been able to use the full potential of this technology. However, two Chilean entrepreneurs devised a platform to go further.

Using the latest technology, they allow creators, amateur filmmakers, visual artists, even the film and music industry to use artificial intelligence algorithms in their work. This is Runway , a platform that integrates machine learning and artificial intelligence to the world of art and creativity.

Founded at the end of 2018 by Cristbal Valenzuela, Alejandro Matamala and Anastasis Germanidis, Runway started as a thesis project that they developed at New York University (NYU) - where they met - while developing a postgraduate degree.

Its creators define the platform as part of the new generation of creative tools. If Photoshop and Adobe revolutionized the creative market a few decades ago, Runway is looking to do so for years to come.

In this case, the bet of this startup is that with their software in the cloud, they can develop "synthetic content", that is, automatically generate, modify, and edit audiovisual content with artificial intelligence algorithms.

Cristobal, Alejandro and Anastasis, founders of Runway. Courtesy photo

"We continue to create audiovisual content in the same way that we have done for decades and that makes the process unnecessarily slow, expensive and difficult. With AI algorithms anyone can create hyper-realistic animations in seconds and edit them automatically. Something that only Hollywood or large production companies and special effects have been able to do so far, "explains Valenzuela.

At the same time, Runway makes it possible to shorten development times, in addition to democratizing access to this technology for as many creators as possible. These technologies are radically changing the way we create content because algorithms are already capable of generating images, text, video and sound in an ultra-realistic way, explains Cristbal; to which Alejandro adds "if we put these tools in the hands of people who have never accessed them before, they will start to think of new ways of producing art, generating content and telling stories".

The impact of the platform started from when they launched a tweet asking how many would use a tool like the one they had in mind. In less than 48 hours, they already had responses from engineers from Facebook, Google, universities and even the media, indicating that they found the possibility of a creative tool to occupy artificial intelligence algorithms incredible. Immediately after this, they created the company and have not looked back.

The path they have already traveled has been fast. As a result of their work, they have already generated interest from different investment funds. In the same year that they created Runway, they completed a $ 2 million investment round with US funds specialized in technology research startups: Lux Capital, Amplify Partners Compound Ventures.

But also, on a practical level, they have already carried out important projects, such as a collaboration with New Balance for the design of a shoe; be the software with which the rock band YACHT created part of the audiovisual content of their latest album - being nominated for a Grammy Award; be working on the creation of short films generated by IA, and even already collaborating with visual artists and the seventh art.

Along with this, the response of cloud software has also come from the academic world, which has led them to close alliances with various universities in the United States, such as NYU, MIT and UCLA; while in Chile the software is already being used at the Universidad Adolfo Ibez, Universidad de las Amricas, and the Pontificia Universidad Catlica.

At the moment, each step that Runway takes is a path towards the future and that is precisely the bet that its founders are making, by developing residencies or internships in the company for artists and researchers, so that they can deepen the uses and applications of the technology they develop. This was a practice that they had implemented before the outbreak of the coronavirus pandemic and that they will resume in a few more weeks, from their offices located in Brooklyn in New York City.

Read more:

Machine Learning and Artificial Intelligence to Revolutionize the World of Art and Creativity - Entrepreneur

Description:

The Barcelona Institute for Global Health (ISGlobal) is a cutting-edge institute addressing global public health challenges through research, translation to policy and education. ISGlobal has a broad portfolio in communicable and non-communicable diseases including environmental and climate determinants, and applies a multidisciplinary scientific approach ranging from the molecular to the population level. Research is organized in three main areas, Malaria and other Infectious Diseases, Child and Maternal Health, and Urban Health, Climate & Non-Communicable Diseases. ISGlobal is the first global public health centre to have received the Severo Ochoa distinction, a seal of excellence of the Spanish Science Ministry.

What we are looking for:

We are seeking a postdoctoral researcher to join the new Biomedical Data Science Research Group of Dr. Paula Petrone at ISGlobal, created in the framework of the Severo Ochoa Program, with the vision of leveraging data analytics and artificial intelligence to improve health care outcomes. This is a unique opportunity for a motivated data scientist to contribute to the digital transformation of biomedicine and carry out research that has social impact and responds to unmet medical needs.

The postdoctoral researcher will develop novel research that applies advanced analytics and machine learning approaches to biomedical data and real-world evidence, IoT and digital therapeutics, bioinformatics and health informatics. An ambition of this team is to implement predictive modelling as well as explainable AI methods to understand disease drivers leading to early disease diagnosis.

Previous experience in the biomedical field is not a requirement but highly desirable. Interest is additionally expressed for candidates with demonstrated experience in at least one of the following topics: bioinformatics, health informatics, medical imaging, computer vision, machine learning, natural language processing, deep learning and explainable artificial intelligence.

Main duties:Development of data science models to predict and to understand human health and disease, to augment clinical decision-making and guide the development of new therapies and diagnosticsCarry out high-quality research independentlyHelp develop robust model training and data infrastructure to support collaborative data science projects at ISGlobalPublish research results in scientific journals and in national and international conferences.

Competences:

Candidates are expected to:

Conditions:

See the article here:

Postdoctoral researcher in Biomedical Data Science and Artificial Intelligence Female candidates are encouraged to apply - ReliefWeb

The ever-increasing adoption of AI-powered systems in all areas of the economy could either lead to potential discrimination against women or open up new career prospects. Lets find out what needs to be done to achieve the second scenario.

For years, there have been debates that artificial intelligence will change the labor market. Nowadays the focus has shifted from the inevitability of the future to an assessment of how and when the world will change and what it means for all of us. There have been several attempts to create AI, but Artificial General Intelligence the one portrayed in films and books is still a long way from being built. What do we have now?

There are many narrow smart algorithms that solve niche tasks and can find patterns in large amounts of data.They help to make decisions on, for example, whether a suspicious transaction should be stopped or allowed, where cargo should be sent, or whether a loan should be granted.The probability of a mistake in these systems is lower comparing to a human beings performance.Further development of algorithms capable of supplementing or replacing human decision-making will reduce the number of roles requiring routine decision-making, which are often taken on by women. It is already difficult to imagine a bright future of tour operators, taxi dispatchers, clerks, and some other professions. According to a forecast made by IMFresearchers, 11% of jobs held by women will be automated within the next two decades. The focus will also shift into designing these algorithms.

More and more decisions are made automatically and, sadly, not in womens favor. For example, two years ago Amazon, the largest online retailer in the world, was forced to dismiss an automated CV-reviewing system because it discriminated against female developers.This situation occurred because the data used to train machine learning models represented a hiring history for 10 years. At that time, the majority of candidates were male, but this does not prove that men are better at technical work.Considering that algorithms are becoming in charge of making an ever-increasing number of decisions, similar side effects can be an issue.

It is widely believed that algorithms just find patterns within an existing data set. However, focusing on data, it is easy to forget two aspects of this problem: the limitations of existing algorithms and, more importantly, the role of people who train them. Most algorithms just catch the correlation within the data, without understanding anything about it. Even the best data is meaningless as long as people who can solve problems and ask the right questions are not involved, so the algorithms will simply reflect our own biases.

Developers of AI systems must carefully monitor the way datasets are formed and track any biases that might occur.Mistakes made by the algorithm should be tracked: sometimes the percentage of errors is quite low, but they could be related to one group of people.For example, a scoring model systematically refuses to give loans to residents of Chinatown. Such behavior is particularly dangerous because we are shifting more and more responsibility for decision-making onto the system. Some advanced algorithms which are currently in use cannot even be interpreted; i.e. we are unable to understand why a certain decision has been made at all, or which factors influenced it.

Its interesting to note that the rise of computer science is strongly connected with women:Ada Lovelace created programming, Betty Holberton designed the first general-purpose computer, and Margaret Hamilton developed the software for the Apollo project.Today, only15% of specialists in the field of artificial intelligence around the world are women, which is disappointing for many advocates of gender equality.Some people worry that the future will be created by men for men.The presence of female analysts eases the situation since they can spot the problems with products that are not easy to discover if you dont face discrimination on a daily basis.

Women already spend a lot of energy ensuring that they are treated honestly and fairly. Perhaps a legal base is needed. Some regulatory bodies have already taken up this issue. For example, the EU General Data Protection Regulation (GDPR) requires companies to explain the reasons behind decisions made by AI systems and to monitor them to prevent any discrimination. Currently, only large companies and governments can afford the software and hardware required to launch the most advanced AI models. This barrier could be used to help set up some basic rules before the technology becomes widely used.

Many futurists are optimistic about womens chances of success. Why? The most valuable skills in this brave new world are those that algorithms cannot master: the use of soft skills and emotional intelligence. As such, 83% of organizations surveyed byCapgemini believe that emotional intelligence will be a prerequisite for success in the coming years. The new labor market will value compassion, multi-tasking, cooperation, and empathy traits that are traditionally associated with women, meaning that women will have greater chances of being hired. However, there is a nuance here. To succeed in an AI-ruled world, a great work of retraining and adaptation is necessary. After all, it will be not the strongest and intelligent that survives, it is the one that is most adaptable to change.

Extra risks for women will be caused by an inability to adapt.The new challenges of automation are added to conventional difficulties, creating barriers to gender equality. Nowadays there is a need for mobility and flexibility among employees as it is now easier to change profession, employer, industry, and even country than ever before. Women are often less mobile than men, because of their second job at home. Whats more, they are oftenexcluded from networking, which allows men to improve their skills, find mentors, and new employment opportunities.

On the other hand, companies will be more motivated to push their employees to develop skills that cannot be automated. If women are proactive and able to adapt, they will have more employment opportunities.Bear in mind that being good at something no one needs is the biggest waste of time. In the future, two categories of skills will be most important: the ability to negotiate and standing your ground, and the ability to see trends and build strategies.

Organizations need employees who can talk to both machines and people. Recently, technologies have been significantly democratised, meaning that you do not need a PhD to work with AI.Today is the best time to gain insight on the foundations of a technology which at first seems complicated, even if your profession is not connected with data analysis at all.

It would be useful for executives to at least make themselves comfortable with the methods of machine learning for analyzing different types of data. Such analysis will make it possible to assess use cases for AI implementation in a specific area and to build an effective strategy for digital transformation. A deep dive into the topic and acquisition of practical skills, such as programming and creating machine learning models, will be useful for those who spend a lot of time running routine analysis and who want to automate the decision-making process.

Implemented projects from industries that are more mature when it comes to AI adoption, such as IT companies, banks, and retailers, can be good sources of inspiration. Even such a conservative industry as manufacturing has started digital transformation programs for production optimization, for example, to forecast machinery breakdowns.

According toIDC forecasts, by 2021 AI systems will be implemented in one form or another at 75% of enterprises. So, the relevant skills will be required in almost all industries. Allocate an hour and watch a video on the principles of machine learning. Only by having a clear understanding of how it works, it is possible to ask the right questions and set goals. Knowledge of a few principles is more important than understanding detailed implementation ways for all algorithms in a software package.

Once you understand the basic principles, its important to understand which tasks are the most relevant for applying AI and to try delegating some responsibilities to the machine to increase the quality of work and performance. By studying successful scenarios of AI application, many people realize that this technology can be used for a wide range of processes and tasks that involve working with large amounts of data.Women should learn how to find prospective use cases for new technologies, have sufficient motivation to look for answers, and take under control their education and career.

Data analysis skills are relevant to a huge number of professions, from marketing experts to mechanics working with a CNC machine. For example, AI can identify anomalies in a technological process. Currently, there is a lack of personnel in data science and artificial intelligence space. The problem is so urgent that large companies are offering free platformsfor studying. After the training, you may not be able to perform as good as an experienced data scientist, but you will be able to translate a task from a business domain for the researcher, for example, regarding an analysis of CV-collection or even writing music.

According to the Institute of Electrical and Electronics Engineers, an international non-profit association of specialists in the field of computer science, The need for specialists in artificial intelligence has revealed itself in almost every area of life. Experts are urging for AI training for specialists in areas such as healthcare, agriculture, and logistics.

Conventional methods of working are rapidly becoming obsolete. Humanity has to choose once again: to lament about fate and talk about the rise of the machines or to get ready for the future and acquire the skills that are in demand. The Luddites have lost in their fight against the machines, simply because machines are economically efficient. And, as we know now, after all the number of jobs created thanks to the adoption of machines has been far greater than the number of jobs gone.

The most insightful employees were not afraid of industrialization, and made use of and reaped the benefits of new technologies. So, why cant women do the same?

Read more:

Artificial Intelligence and Discrimination: To Be or Not To Be? - IoT For All

Today, artificial intelligence (AI) applications are reshaping large and small companies products, services, and business models. Decision makers need to make critical hardware and software choices to achieve AI strategy success. As smaller firms to global enterprises assess their business cases and work flows the need for highly-scaled servers and cloud platforms are no longer the only option to build a successful AI infrastructure.

The study results summarized in this white paper, Using Workstations To Reshape Your Artificial Intelligence Infrastructure, show that firms are already using workstations to lower the cost, increase the security, and speed up their AI infrastructure. The addition of workstations into a firms AI workflow allows servers and cloud platforms to be tasked with business cases that require more robust computing while workstations take on tasks with longer time frames and smaller budgets.

Download this white paper, Using Workstations To Reshape Your Artificial Intelligence Infrastructure, to read more about how highly-scaled servers and cloud platforms are necessary to run applications that must be run at top speed and where cost is not a barrier. However, workstations provide excellent support for applications where data security is a priority but where timelines are more flexible or cost is a major consideration.

See the original post here:

Using Workstations To Reshape Your Artificial Intelligence Infrastructure - insideHPC

Automation during the industrial revolution led to a profound change in working practices across the 18th and 19th centuries. Currently, we are in the midst of a fourth industrial revolution, with globalisation and automation affecting every aspect of our working lives and leisure activities. The COVID-19 pandemic has provided a further driver for change, altering how we work and interact with remote working and reduction of human interaction at the centre of global initiatives to try and reduce the spread of the virus.

Healthcare systems generate large quantities of complex datasets pertaining to patients. Artificial Intelligence (AI) can offer solutions to medical problems by attempting to replicate decisions that would otherwise require human intelligence. Specific algorithms can be created in order to make associations between data and predict future outcomes. The COVID-19 pandemic has accelerated change within healthcare systems and driven interest in automated algorithms capable of assisting hospitals in diagnostics, decision making and repetitive clerical tasks thus reducing the potential footfall of staff on site. Automation and intelligent algorithms that learn and improve with further iterative cycles require data and the ethics behind large personal data sets, the challenges of anonymisation remain in their infancy.

AI applications within healthcare can be broadly categorised into diagnosis, research, management and system analysis. Ultimately in the time of COVID, workforce adaptations have led healthcare providers to use their workforce wisely, reducing the staff requirements on site and moving towards remote working.

Healthcare records in most countries have moved from paper-based records and notes to digital media. The electronic health record (EHR) allows the capture of large quantities of data across patient groups. Future patient care aims to develop tailored treatments for patients in a cost-effective manner.

The use of large datasets requires an element of data curation. Data needs to be retrieved from multiple disparate data sources. Data needs to then be cleaned to remove anomalies and harmonised to ensure similar data sets are compared across patient records. An element of these processes needs human oversight to ensure the correct data is fed into the algorithms.

Administrative applications are resource-heavy and repetitive. These applications include workflow management such as uploading referral letters from primary care, setting up referral assessment services and booking patients to the correct service provider in secondary care. Robotic process automation (RPA) consists of computer programmes that obey rules for these manual, resource-heavy tasks.

Additionally, process management systems (PMS), while embedded within commercial businesses, are still in their infancy within a healthcare application. Patients are individuals and tailoring care to them necessitates being able to react to changing physiological parameters within the confines of the organisation. In order to standardise care, clinical pathways have been developed to manage patient care from referral, to ordering diagnostic tests and eventual treatment pathway. While standardisation across patient groups allows for automation of some of these processes (templates for referral, standard test orders), allowing a more tailored approach which is patient-specific is the ultimate goal; thus generating a conflict to resolve between standardisation and a tailored patient approach specific to their individual needs.

Flexibility within a process management system requires technological skills to allow tasks to be postponed or reorganised. However, healthcare professionals lack the technical skillset to implement this, requiring a user-friendly interface. Further work in user interface and user experience (UI/UX) is therefore required to ensure a system that allows flexibility without losing the advantages of rapid automation and processing of large numbers of patients within the pathway management systems.

Increasingly call centre staff for websites have been replaced by chatbots that use natural language processing (NLP) to provide callers with information and manage queries. A chatbot is a type of AI programme that can conduct an intelligent conversation via text or auditory methods. It is predicted that by 2025 the global chatbot market will be worth $1.23bn. For hospitals dealing with upwards of 10,000 patient appointments per week, the use of chatbots to handle patient queries regarding appointment queries is still in its infancy when compared to more established sectors such as banking or commerce.

The current COVID-19 pandemic has highlighted the need for rapid screening and testing of patients to improve treatment pathways and also reduce the risk of cross infection. Clinical testing requires taking biological samples from patients which can be resource heavy and incorporates a time lag before results are available from real-time polymerase chain reaction testing (RT-PCR). The use of AI within this environment accessing electronic health records (EHR) of routinely ordered tests and vital signs can produce an effective tool to screen patients in emergency departments and hospital admission units.

Predictive analytics utilise AI algorithms to analyse healthcare data from EHRs to predict future outcomes thus aiming to improve outcomes and the patient experience as well as reducing costs. Data collected from EHRs can be supplemented with data from wearable technology and medical devices. Risk prediction models utilising AI would improve with successive data collection cycles aiming to supplement decision making by clinicians. Applications include management of chronic diseases such as chronic renal failure, diabetes and cardiovascular disease. Specific patient populations can vary across geographical healthcare providers and the ability of a predictive model to learn from its local population provides an advantage over established static modelling. Scalability across healthcare providers can therefore be challenging due to differences in socio-economic factors and populations based on geographical location. The ethical implications in terms of health insurance and risk stratification are in their infancy; and issues around data governance and data sharing may have a significant impact that is yet to be fully regulated.

Diagnostic applications of AI technological advances have exploded over the past ten years with multiple applications. Imaging studies such as breast mammograms or histological analysis rely on skilled scientists or clinicians performing repetitive tasks to manually identify abnormalities. An inaccurate diagnosis can have serious consequences for patient care. AI programmes can be trained to perform these tasks and have shown an accuracy in correctly diagnosing abnormalities which in some studies has been shown to be as accurate as a trained clinician. Further future applications in diagnostic imaging include the field of radiomics which extracts nuanced features peculiar to imaging modalities such as wavelength, texture and shape. This additional information can provide further data for diagnosis and prognostic indicators specific to patients.

With the potential application of AI within the healthcare setting, the question remains how will this impact the workforce? The fourth industrial revolution has 50% of companies to predict that by 2022, automation will decrease their numbers of full-time staff and that by 2030 robots will replace 800 million workers across the world (McKinsey Global Institute reports). Automation of clerical processes and care pathways could potentially impact on the non-clinical workforce within a healthcare setting. Specialties such as radiology where imaging reports can be automated and produced by AI algorithms may soon be the reality. The ethics of data sharing and the implications for patients and their insurers is a further area of controversy. We enter a brave new world.

Caroline B HingYasmin AntoniouAI for Goodwww.aiforgood.co.uk

This article is from issue 15 of Health Europa. Clickhere to get your free subscription today.

Read the original post:

Artificial Intelligence in healthcare and clinical practice in the COVID era - Health Europa