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Technology and healthcare go hand in hand. Many people are asking themselves where theyre going. The industry continues to benefit from massive investment in digital health trends such as telemedicine, IoT devices, and virtual reality surgical training, which has helped improve global health equity.

Here are 12 reasons technology is changing how we think about IT and healthcare:

Nanotechnology promises many things, but it may actually be closer than you think. Researchers from the US and South Korea have created nanorobots capable of delivering drugs to clogged arteries and drilling through them. This technology, which is controlled by an MRI machine and has wide-ranging applications, looks promising. However, there are still some issues that need to be resolved in the lab before they can apply it to humans. Google has established Verily, a Life Sciences division within Alphabet that is partnering with Johnson & Johnson in order to further explore the technology.

It has never been easier to deal with large amounts of data. Analytics, cloud computing, machine learning, and machine learning have allowed us to access more data and allow us to see it in new ways. AI promises to allow us to sift through the mountains of data to gain new insights. This will enable us to identify potential risks and reduce costs. Other promising applications include reducing waste and expediting the drug discovery process.

The biggest source of frustration and confusion in healthcare is billing. It is easy to make mistakes and it can be frustrating to chase down people. Patient Access Solutions makes the whole process simpler and the audit process more efficient.

Augmented reality offers many promising applications in healthcare. It can help us keep our information organized, avoid errors, and improve the quality of our care. Its possible to access patient information during an interaction, making it more personal and powerful.

3D printing promises to revolutionize medical technology, from prosthetics to instrumentation, to implants. It has the potential for a complete revolution in the medical field as we continue to refine and improve our processes.

Shockwave therapy, also known as acoustic shockwave therapy (LiESWT) or low-intensity additional corporeal shockwave treatment (Acoustic Soundwave Therapy), is the best method to solve the problem. It increases the blood flow to the penis permanently. This type of therapy has been used in clinics for over a decade. However, a new shockwave therapy device, the Phoenix, allows men to improve their erections from the privacy and comfort of their own homes.

As our demand to interface quickly with computers and digital information grows, it might make sense to use recent advancements in neural interface technology. Cyborgization is a concept that allows humans and machines to work seamlessly together in many contexts. This will allow us to provide quality care in new ways. The possibilities are limitless, from providers being able precisely to control robotic surgical tools to patients having integrated systems that monitor vital stats and alert of impending trouble.

Electronic prescription filing is growing for many reasons. It reduces errors, speeds up medical reconciliation, and alerts providers to potential adverse interactions or patient allergies.

Digital diagnostic tools are becoming more powerful. Its easier than ever to get a second opinion and confirm a difficult diagnosis with 4K video and high-resolution cameras. There are also more options to consult if you have difficulty solving a case.

While patient history is an important part of quality care, it is often the patient who is the most difficult to access. Patient portals allow you to access all the patients information and medical history from one place.

Providers compliance is centered on health records and personal information (PHI). They are also an important source of anxiety for IT professionals in healthcare who are responsible for security. Blockchain is made up of two components. The first is a public transaction log, which cannot be accessed by anyone else.

Cognitive technology increasingly uses digital records and AI advances to process large quantities of data in new ways. It identifies patterns that can be used to predict disease early and help catch it before it happens. Computer vision, machine learning, and natural language processing are just a few of the other uses.

It protects encrypted data from being altered or changed. It can improve patient care by linking patients to their data rather than to their identities.

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12 Technology Innovations That Will Influence the Future of Healthcare - The Southern Maryland Chronicle

In the first of his four (stunning) Reith lectures on living with artificial intelligence, Prof Stuart Russell, of the University of California at Berkeley, began with an excerpt from a paper written by Alan Turing in 1950. Its title was Computing Machinery and Intelligence and in it Turing introduced many of the core ideas of what became the academic discipline of artificial intelligence (AI), including the sensation du jour of our own time, so-called machine learning.

From this amazing text, Russell pulled one dramatic quote: Once the machine thinking method had started, it would not take long to outstrip our feeble powers. At some stage therefore we should have to expect the machines to take control. This thought was more forcefully articulated by IJ Good, one of Turings colleagues at Bletchley Park: The first ultra-intelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.

Russell was an inspired choice to lecture on AI, because he is simultaneously a world leader in the field (co-author, with Peter Norvig, of its canonical textbook, Artificial Intelligence: A Modern Approach, for example) and someone who believes that the current approach to building intelligent machines is profoundly dangerous. This is because he regards the fields prevailing concept of intelligence the extent that actions can be expected to achieve given objectives as fatally flawed.

AI researchers build machines, give them certain specific objectives and judge them to be more or less intelligent by their success in achieving those objectives. This is probably OK in the laboratory. But, says Russell, when we start moving out of the lab and into the real world, we find that we are unable to specify these objectives completely and correctly. In fact, defining the other objectives of self-driving cars, such as how to balance speed, passenger safety, sheep safety, legality, comfort, politeness, has turned out to be extraordinarily difficult.

Thats putting it politely, but it doesnt seem to bother the giant tech corporations that are driving the development of increasingly capable, remorseless, single-minded machines and their ubiquitous installation at critical points in human society.

This is the dystopian nightmare that Russell fears if his discipline continues on its current path and succeeds in creating super-intelligent machines. Its the scenario implicit in the philosopher Nick Bostroms paperclip apocalypse thought-experiment and entertainingly simulated in the Universal Paperclips computer game. It is also, of course, heartily derided as implausible and alarmist by both the tech industry and AI researchers. One expert in the field famously joked that he worried about super-intelligent machines in the same way that he fretted about overpopulation on Mars.

But for anyone who thinks that living in a world dominated by super-intelligent machines is a not in my lifetime prospect, heres a salutary thought: we already live in such a world! The AIs in question are called corporations. They are definitely super-intelligent, in that the collective IQ of the humans they employ dwarfs that of ordinary people and, indeed, often of governments. They have immense wealth and resources. Their lifespans greatly exceed that of mere humans. And they exist to achieve one overriding objective: to increase and thereby maximise shareholder value. In order to achieve that they will relentlessly do whatever it takes, regardless of ethical considerations, collateral damage to society, democracy or the planet.

One such super-intelligent machine is called Facebook. And here to illustrate that last point is an unambiguous statement of its overriding objective written by one of its most senior executives, Andrew Bosworth, on 18 June 2016: We connect people. Period. Thats why all the work we do in growth is justified. All the questionable contact importing practices. All the subtle language that helps people stay searchable by friends. All of the work we have to do to bring more communication in. The work we will likely have to do in China some day. All of it.

As William Gibson famously observed, the futures already here its just not evenly distributed.

Pick a sideThere Is no Them is an entertaining online rant by Antonio Garca Martnez against the othering of west coast tech billionaires by US east coast elites.

Vote of confidence?Can Big Tech Serve Democracy? is a terrific review essay in the Boston Review by Henry Farrell and Glen Weyl about technology and the fate of democracy.

Following the rulesWhat Parking Tickets Teach Us About Corruption is a lovely post by Tim Harford on his blog.

Read more from the original source:
Worried about super-intelligent machines? They are already here - The Guardian

What was in the past just a figment of the imagination of some of our most famous scientists and writers, machine learning Machine Learning Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesnt figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesnt figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Read this Term and AI have without a doubt taken root in almost everything smart.

AI is now being used to not only solve a wide range of modern and common problems, but also to assist in the wellbeing of the human mind.

Recently, developers have attempted to use AI to make us happier, but can these applications help us?

In the early 1930s, at the height of the Second World War, British cities were taking heavy casualties by constant German air raids. The Germans were so effective with blitzkrieg and with the secretary of their war plans that at one point during the war, they cornered the entire British army at the beaches of a French coastal town called Dunkirk.

Related content

The Germans were always a step ahead in their vital war plans largely because the allies had little intelligence on what their next advance would be. The Germans used a special code generated by a machine they had engineered called the Enigma to send messages secretly within the Wehrmacht and its occupied territories.

The allies biggest challenge was to crack this German code. To undertake this project, the UK Government Code and Cypher School (GC&CS), headquartered in Bletchley Park, appointed scientist Alan Turing as the man for the job.

Turing assembled a team that eventually created the Bombe machine which was used to decipher Enigmas messages. By speeding up the process of breaking the Enigma's encryption settings, staff could decode messages quickly and pass on the intelligence.

The Bombe and Enigma Machines laid the foundations for Machine Learning. They could converse with humans without humans knowing it was a machine. This imitation game is technically what we would label as intelligent.

In 1956, American computer scientist John McCarthy officially adopted the term Artificial Intelligence at the Dartmouth Conference.

Several Research centers were established in the United States aiming to explore the potential of AI. Herbert Simon and Allen Newell were pivotal in promoting AI as a technology that could transform the world.

In 1966, well before the launch of personal computers, Joseph Weizenbaum created Eliza at the MIT Artificial Intelligence Laboratory. This was the first-ever AI bot in the form of a chat-bot which are self-learning bots that are programmed with Natural Language Processing (NLP) and Machine Learning.

Today, AI is integrated in a variety of machines and softwares including AI bots.

However, a more sophisticated type of AI is emerging, labeled as "happiness tech" which assists people in becoming happier by detecting an individual's emotional state of being. But how does it work?

Since 2016, AI researcher Julian Jewel Jeyaraj has been working on the idea of utilizing AI to measure an individual's happiness. Jewel Jeyaraj developed JJAIBOT which is able to analyze the facial expressions of thousands of photos ( a social media profile for example) and forecast the emotional state of individuals within those photos. By analyzing the facial expressions, date, time, and location of those photos, the AI - which is trained in cognitive behavioral therapy methods to learn emotional profiles - is able to even measure the general happiness of an individual, or an entire demographic.

Based on the data it collects, the AI bot has the capabilities to provide personalized "happiness recommendations" to individuals such as meditation and breathing techniques, and other exercises to assist in their mental health.

So far the AI has been tested with more than 10,000 people in different environments.

Julian Jewel says AI bots are like personal assistants who remember our likes, dislikes and never tend to disappoint. Future JJAIBOTs can be assembled through stem cells in a petri dish that can produce living robots that can essentially reproduce. These bots can be programmed to perform useful functions such as finding cancer cells in human bodies or trapping harmful microplastics in the ocean protecting the environment

Utilizing this type of AI technology in the workplace can help businesses, too. Companies would be able to track what's called "psychological capital," and could significantly increase employee productivity for companies.

During lockdown, the world relied on technology to keep us connected to friends, family and our ability to work remotely.

The pandemic also made clear the importance of human connection which was heavily underscored.

We depend on "happiness technologies" to keep us healthy and happy and without applications such as video chats, entertainment, online conferencing, and software such as JJAIBOT, we would live in a world that was much more fragmented and psychologically difficult to bear.

During the pandemic, socialization has been crucial to many people's mental health. Interactive bots have been able to at least partially meet our need for intelligent connection.

A prime example of this is the CozmoBot, a child friendly human-AI interaction robot designed by AnthroTronix. CozmoBot is a robot that recognizes faces, learns names and uses facial expressions to convey different emotions and can be used as part of a play therapy program that promotes rehabilitation and development of disabled children. It has a constantly evolving set of skills and abilities based on human interactions. The CozmoBot system also automatically collects data for therapist evaluation.

Another example is JJAIBOTT which uses Visual & Acoustic Recognition Component (V-ARC) and advanced algorithms to detect images (brain scans, facial expressions, etc.) and text to detect human emotions. JJAIBOT also utilizes Predictive Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making.In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes.Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables.Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements.How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors.In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions.Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed.Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades.Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making.In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes.Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables.Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements.How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors.In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions.Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed.Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades.Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term Engine (PAE), which uses automated machine learning algorithms to data sets to create predictive models.

In these cases, there is no question that AI has the potential to tackle and solve complex problems, even as complex as helping our physiological state.

AI is a valuable tool to help increase a person's happiness by offering deep analysis, calculated solutions, and mimicking human-like connection.

This article was written by Khaled Mazeedi.

What was in the past just a figment of the imagination of some of our most famous scientists and writers, machine learning Machine Learning Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesnt figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesnt figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Read this Term and AI have without a doubt taken root in almost everything smart.

AI is now being used to not only solve a wide range of modern and common problems, but also to assist in the wellbeing of the human mind.

Recently, developers have attempted to use AI to make us happier, but can these applications help us?

In the early 1930s, at the height of the Second World War, British cities were taking heavy casualties by constant German air raids. The Germans were so effective with blitzkrieg and with the secretary of their war plans that at one point during the war, they cornered the entire British army at the beaches of a French coastal town called Dunkirk.

Related content

The Germans were always a step ahead in their vital war plans largely because the allies had little intelligence on what their next advance would be. The Germans used a special code generated by a machine they had engineered called the Enigma to send messages secretly within the Wehrmacht and its occupied territories.

The allies biggest challenge was to crack this German code. To undertake this project, the UK Government Code and Cypher School (GC&CS), headquartered in Bletchley Park, appointed scientist Alan Turing as the man for the job.

Turing assembled a team that eventually created the Bombe machine which was used to decipher Enigmas messages. By speeding up the process of breaking the Enigma's encryption settings, staff could decode messages quickly and pass on the intelligence.

The Bombe and Enigma Machines laid the foundations for Machine Learning. They could converse with humans without humans knowing it was a machine. This imitation game is technically what we would label as intelligent.

In 1956, American computer scientist John McCarthy officially adopted the term Artificial Intelligence at the Dartmouth Conference.

Several Research centers were established in the United States aiming to explore the potential of AI. Herbert Simon and Allen Newell were pivotal in promoting AI as a technology that could transform the world.

In 1966, well before the launch of personal computers, Joseph Weizenbaum created Eliza at the MIT Artificial Intelligence Laboratory. This was the first-ever AI bot in the form of a chat-bot which are self-learning bots that are programmed with Natural Language Processing (NLP) and Machine Learning.

Today, AI is integrated in a variety of machines and softwares including AI bots.

However, a more sophisticated type of AI is emerging, labeled as "happiness tech" which assists people in becoming happier by detecting an individual's emotional state of being. But how does it work?

Since 2016, AI researcher Julian Jewel Jeyaraj has been working on the idea of utilizing AI to measure an individual's happiness. Jewel Jeyaraj developed JJAIBOT which is able to analyze the facial expressions of thousands of photos ( a social media profile for example) and forecast the emotional state of individuals within those photos. By analyzing the facial expressions, date, time, and location of those photos, the AI - which is trained in cognitive behavioral therapy methods to learn emotional profiles - is able to even measure the general happiness of an individual, or an entire demographic.

Based on the data it collects, the AI bot has the capabilities to provide personalized "happiness recommendations" to individuals such as meditation and breathing techniques, and other exercises to assist in their mental health.

So far the AI has been tested with more than 10,000 people in different environments.

Julian Jewel says AI bots are like personal assistants who remember our likes, dislikes and never tend to disappoint. Future JJAIBOTs can be assembled through stem cells in a petri dish that can produce living robots that can essentially reproduce. These bots can be programmed to perform useful functions such as finding cancer cells in human bodies or trapping harmful microplastics in the ocean protecting the environment

Utilizing this type of AI technology in the workplace can help businesses, too. Companies would be able to track what's called "psychological capital," and could significantly increase employee productivity for companies.

During lockdown, the world relied on technology to keep us connected to friends, family and our ability to work remotely.

The pandemic also made clear the importance of human connection which was heavily underscored.

We depend on "happiness technologies" to keep us healthy and happy and without applications such as video chats, entertainment, online conferencing, and software such as JJAIBOT, we would live in a world that was much more fragmented and psychologically difficult to bear.

During the pandemic, socialization has been crucial to many people's mental health. Interactive bots have been able to at least partially meet our need for intelligent connection.

A prime example of this is the CozmoBot, a child friendly human-AI interaction robot designed by AnthroTronix. CozmoBot is a robot that recognizes faces, learns names and uses facial expressions to convey different emotions and can be used as part of a play therapy program that promotes rehabilitation and development of disabled children. It has a constantly evolving set of skills and abilities based on human interactions. The CozmoBot system also automatically collects data for therapist evaluation.

Another example is JJAIBOTT which uses Visual & Acoustic Recognition Component (V-ARC) and advanced algorithms to detect images (brain scans, facial expressions, etc.) and text to detect human emotions. JJAIBOT also utilizes Predictive Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making.In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes.Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables.Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements.How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors.In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions.Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed.Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades.Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making.In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes.Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables.Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements.How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors.In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions.Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed.Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades.Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term Engine (PAE), which uses automated machine learning algorithms to data sets to create predictive models.

In these cases, there is no question that AI has the potential to tackle and solve complex problems, even as complex as helping our physiological state.

AI is a valuable tool to help increase a person's happiness by offering deep analysis, calculated solutions, and mimicking human-like connection.

This article was written by Khaled Mazeedi.

See the original post here:
New AI Software Makes Us Happier by Analyzing Facing Expressions - Finance Magnates

Like many manufacturers, autonomous vehicle (AV) developers suffered through a 2021 that was rocked by semiconductor shortages, global supply chain disruptions, eroding customer confidence, and other challenges.

Heading into 2022, the AV industrys biggest challenges will continue to be a disrupted supply chain, chip shortages, and a skeptical public. AVs rely on AI technology in the form of graphics processor units (GPUs) to handle deep learning and machine learning tasks. Those chips are advancing with Qualcomms SnapDragon being a big one and NVIDIA in the space as well with the TX2/Jetson models, says Chris Mattmann, CTIO at the NASA Jet Propulsion Laboratory. With the supply chain crisis that includes chip manufacturing, getting these chips and many of them per vehicle is even more important in the autonomous vehicle industry than it is in the consumer sector.

Moving into 2022, many AV manufacturers hope to build trust with increasingly skeptical lawmakers and consumers. Phil Koopman, a Carnegie-Mellon University associate professor with appointments in the department of electrical and computer engineering and with the Robotics Institute, believes that Tesla's use of vehicle owners as beta testers is reckless and damaging to the image of the entire autonomous vehicle industry. Reckless, because [drivers] are running stop signs, running red traffic lights, and veering across centerlines on public roads, he explains. Tesla is using civilian drivers who are neither specifically trained in testing safety nor operating according to best practices for road testing safety.

Koopman says that the stance taken by the entire AV industry to push back hard against any requirement to follow safety standards further erodes public trust. He notes that manufacturers face a choice in 2022 and beyond. They can continue to take an adversarial approach with regulators and have a problem when a high-profile crash forces regulators to intervene, or they can take a cooperative approach now while they still have time.

An excellent first step, Koopman says, would be for AV developers to voluntarily agree to follow the SAE J3018 standard for safe road tests. The industry itself wrote that standard based on lessons learned from the Uber ATG testing fatality in Tempe, Arizona, but there is not a single AV company that will publicly pledge to follow that standard.

The biggest challenge for vehicle manufacturers and their technology partners is developing models that can deliver a true autonomous driving experience. Within the AV industry, full autonomy is referred to as Level 5 Advanced Driver Assistance Systems (ADAS). At Level 5, there is no human intervention required and the vehicle is fully capable of driving itself, says Matt Desmond, automotive principal industry analyst at business advisory firm Capgemini Americas.

None of the AVs marketed to be sold in the next few years will incorporate Level 5 ADAS. Delivering a truly autonomous vehiclewithout steering wheel, accelerator, or brakesis a steep technological and safety challenge, and there are many significant hurdles to achieving fully autonomous solutions, Desmond says. In the meantime, leading vehicle manufacturers and technology firms are investing massive sums in developing, testing, and refining AV systems in an effort to mitigate technical issues and deliver a robust technology foundation, he notes.

As things currently stand, Level 5 ADAS vehicles may not reach market for at least several years. The reality is that the core technologies of ADAS needs to mature to a point where virtually any scenario can be identified and addressed safely by the autonomous software, Desmond explains. He notes that vehicle manufacturers and technology providers have already driven AVs for thousands of hours to train the onboard software to learn various driving environments. However, there's still much more work to do, especially in scenarios where there is inclement weather, such as snow, mud, sand, or rain, that can possibly interfere with sensors.

Several key issues need to be resolved before Level 5 ADAS vehicles can become mainstream transportation technology. Besides addressing the core technical challenges presented by code complexity, network latency, and hardware gaps, numerous market- and legal-oriented matters must be settled. Taken as a whole, the industry and the ecosystems of business, law, policy, and culture have a long way to go to provide solutions for the mass market deployment of autonomous vehicles, Desmond says.

In a sense, AV developers are facing a chicken and egg scenario, since many potential ADAS challenges can't be fully vetted and resolved until production AVs are released to market, Desmond says. As real production dates for autonomous vehicles are announced, we believe we will see real traction for resolution of these issues from the car and technology companies developing ADAS products, the insurance industry, and federal, state and local regulatory agencies.

While fully autonomous vehicles won't be generally available in the near-term, autonomous features will continue to be added to conventional vehicles, observed Raj Rajkumar, a professor in Carnegie-Mellon University's department of electrical and computer engineering and co-director of the General Motors-Carnegie Mellon Vehicular Information Technology Collaborative Research Lab. The endpoint of full autonomy will not be an overnight revolution, but the final stop in an evolutionary path of progress with multiple milestones, he says.

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Diabetic retinopathy (DR) is one of the main reasons for avoidable blindness among adults in India, which has one of the worlds biggest diabetic populations.Early detection and treatment are critical to limiting the damage but a shortage of well-trained ophthalmologists, especially in rural areas, remains a challenge.

Artificial intelligence (AI) is coming in handy to bridge the gap. Sankara Eye Foundation India, a non-profit that aims to eliminate preventable and curable blindness, has collaborated with Singapore-based Leben Care to deploy a cloud-based AI software platformNetra.AI.

Built on Intel-powered technologies, Netra, which means eyes in Hindi, can help identify the retinal condition in a short time with the accuracy level of human doctors, using deep learning.It can tell a healthy retina from an unhealthy one.

Not just Intel, several tech have turned to AI to help detect DR. Google helps healthcare workers detect diabetic retinopathy, with possibilities of AI algorithms to assist clinicians in identifying other diseases as well.

Microsoft Seeing AI research project is designed for no and low-vision community. Seeing AI uses computer vision, image and speech recognition, natural language processing and machine learning to describe a persons surroundings, read the text and answer questions. It can also identify Indian currency notes.

These are just some of the examples of AI improving the quality of life in India.Not just in India, AI for social good is almost a movement across the world, aimed at providing quality education, welfare measures and healthcare while taking care of the environment.

Delivering the social goods

AI for social good is an increasingly popular theme. When Google Research India, an AI lab based in Bengaluru, was set up in 2019, the focus was to find ways to help build an artificial intelligence ecosystem.

AI has been a subject of immense interest not just for the technological breakthroughs but also for the wrong turns it could take.

Since its origins in the 1950s, AI has been viewed with suspicion. Will it make humanity redundant? Will humans become subservient to machines?

A lot of water has flown under the bridge since the 1950s and extensive research of the last four decades has led us to AI for social good.

Worries surrounding legal, ethical and safety issues forced the tech ecosystem to look for ways that AI could be shaped to benefit people and society at large.

Be it digital healthcare, advanced education, forecasting floods, wildlife conservation, securing marine life, or predicting wildfires, AI has a role to play.

Powering learning

Shalini Kapoor, IBM Fellow, IBM India Software Labs, says AI has penetrated every aspect of our lives. From transforming businesses to making a mark in societal impact, AI is leading from the front.

In the field of education, we believe that AI can enhance learning environments by unlocking learning potential resulting in improved outcomes and better student experiences. IBM is working with several state governments and educational institutes to ensure AI is part of the curriculum to impart AI skills at an early age, she says.

IBM has partnered with the Ministry of Education and NITI Aayog on an online initiative for higher education.

Aligned with the Skill India mission to provide last-mile connectivity for quality higher education, smShiksha, an AI-driven, personalised learning platform, is being designed for a holistic learning experience.

It has the potential to scale up to become a single-point source for higher education in India by serving as a virtual campus, Kapoor says.

Along with CBSE, we have also developed a curriculum of artificial intelligence as an elective subject for Classes IX to XII. We have covered over 200 schools across 13 states and over 15,000 students have benefitted from the programme, she told Moneycontrol.

The coronavirus pandemic has accelerated digital adoption. Rohini Srivathsa, National Technology Officer, Microsoft India, says India is on the cusp of a digital revolution.

During the pandemic, we worked closely with several governments and public health authorities to enable citizen services with AI. The government of India's Saathi chatbot and Punjab governments COVA app that helped citizens with critical COVID care information are powered by Azure AI, she says.

If you look at healthcare, Apollos 24x7 virtual healthcare platform uses Azure AI to offer last-mile healthcare service delivery across India, says Srivathsa.

Bridging the language divide

AI and data are at the heart of driving economic and societal inclusion for everyone, including the one billion people with disabilities around the world.

AI4Bharat, an AI startup in partnership with Microsoft, is building AI models for recognising Indian sign languages, creating one of the largest datasets on it. This project has the potential to integrate people with disabilities into the workforce, says Srivathsa.

Google is using AI to help out with languages. Many of us prefer consuming content and getting things done in our mother tongue but language continues to be one of the biggest barriers to access information on the web.

Solving for languages, in particular Indian languages, represents many challenges, says Partha Talukdar, a staff research scientist at Google Research India.

From the complexity and variation of scriptsfrom Urdu to Malayalam to Gujaratito the sheer number of languages across the country, it poses an interesting challenge for AI and machine learning.

It is also something we at Google have been focusing on for a long time, from launching Neural Machine Translation for 11 Indian languages back in 2017 to now giving users the option to access high-quality web pages originally written in other languages and see it in their preferred Indian language, says Talukdar.

Scientists at Google Research India have come up with a new language model called Multilingual Representations for Indian Languages (MuRIL).

MuRIL helps in handling challenges like transliterations, spelling variations, mixed languages, and other usages often observed in the Indian context.

MuRIL supports 16 Indian languages as well as English. We have also adapted MuRIL for better query understanding for spoken query understanding, says Googles Talukdar.

Srinivas Lingam, Intels VP, Datacenter & AI Group, says AI is improving English-language proficiency for some of Indias most needy children.

A project weve been involved with is the development of ReadToMe, an AI-enabled software platform, which is supporting more than 25,000 Indian schools. Implemented for students from grades 1 through 12, classes using ReadToMe displayed a 2040 percent improvement in English reading and comprehension skills, he says.

Googles Read Along, earlier known as Bolo, helps kids learn to read. First tested in India in 2018 and rolled out across the world, Read Along is an AI-enabled Android app. The virtual assistant in the app reads a story then listens to the child narrating it back.

Using AI-based speech recognition and machine learning, the app coaches the student on pronunciation. Googles internal analysis shows that after reading 100 minutes on the app, beginner readersthose who read at a speed of less than 45 correct words a minutesee an improvement from 38 percent to 88 percent in reading fluency.

Intelligent farming

Elsewhere, AI is helping Indias agricultural sector. For instance, an AI algorithm can help farmers detect where pests and insects will land in a field by checking the direction of winds, helping them optimise where they plant their crops, says Intels Lingam.

According to Microsofts Srivathsa, AI is removing guesswork involved in agriculture by enabling data-driven farming.

With the help of machine learning algorithms and low-cost sensors, our data and AI give farmers a real-time view of soil conditions and moisture, enabling farmers to increase productivity and lower costs, she says.

Google is working on a range of climate-related efforts such as weather alerts, flood forecasting and air quality in India that can provide timely and important information.

It is also expanding its machine-learning-based flood forecasting it launched in 2018 to help combat the damage by equipping those in harms way with accurate and detailed alerts.

Microsoft is working with non-profit SEEDS (Sustainable Environment and Ecological Development Society) to protect vulnerable populations affected by climatic hazards by leveraging AI capabilities.

With a cloud and AI-based model that predicts cyclonic activity, the project is helping vulnerable communities from cyclone-prone areas reach safe grounds on time.

The AI model called Sunny Lives has received technical and financial support from Microsofts AI for Humanitarian Action grant and was deployed at scale during Cyclone Yaas in Odisha earlier this year.

SEEDS is exploring its use cases across many countries in Southeast Asia to cope with other weather challenges, including heatwaves.

More here:
How AI is improving education, healthcare and farming in India - Moneycontrol.com