Mediaboss Marketing

A recent study by Gartner indicates that by 2025 the 10% of enterprises that establish Machine Learning (ML) or Artificial Intelligence (AI) engineering best practices will generate at least three times more value from their AI and ML efforts than the 90% of enterprises that don't. With such a high value estimated to be derived only from the adoption of ML/AI practices, it is difficult to not agree that the future of enterprises rests heavily on AI and ML technologies with other digital technologies.The pandemic has unveiled a world that embraced technology at a pace that would have otherwise taken ages to evolve.

Traditional practices that saw monolithic systems, lack of flexibility and manual processes were all blocking innovation.

Also Read:Artificial Intelligence: A Pathway to success for enterprises

However, mass new-age technology acceptance induced by the pandemic has helped enterprises overcome these challenges. Modern technologies like AI and ML are opening a new world of possibilities for organisations.

Seizing the early-mover advantage will particularly benefit organisations in taking important business decisions in a more informed, intuitive way.

The applicability of new-age technologies is growing every day. For example, marketers are starting to use ML-based tools to personalise offers to their customers and further measure their satisfaction levels through the successful implementation of ML algorithms into their operations.

This and there are more examples of how AI/ML algorithms are enabling organisations run their businesses smartly and make them profitable.Additionally, enterprises are recognising the benefits of cloud infrastructure and applications with ML and AI algorithms built in.

They allow companies to spend less time on manual work and management and instead focus on high-value jobs that drive business results. ML can result in efficiencies in workloads of enterprise IT and ultimately reduce IT infrastructure costs.

This stands especially true in India, where consulting firm Accenture estimates in one of its reports that the use of AI could add $957 billion to the Indian economy in 2035 provided the "right investments" are made in new-age technology. India, with its entrepreneurial spirit, abundance of talent and the right sources of education has mega potential to unleash AI's true capabilities - but they need the right partner.

The biggest limitation in using AI is that companies often run into implementation issues which could be anything from scarcity of data science expertise to making the platform perform in real-time.

As a result, there is slight reluctance in accepting AI among organisations, and this, in turn, is leading to inconsistencies and lack of results.

Also Read:Three ways AI can help transform businesses

However, with the right partner, India's true potential can be harnessed. As we move into an AI/ML led world, we need to lead the change by building the requisite skills.

While many companies don't have enough resources to marshal an army of data science PhDs, a more practical alternative is to build smaller and more focused "MLOps" teams - much like DevOps teams in application development.

Such teams could consist of not just data scientists, but also developers and other IT engineers whose mission would be to deploy, maintain, and constantly improve AI/ML models in a production environment. While there is a huge responsibility lying on IT professionals to develop an AI/ML led ecosystem in India, companies must also align resources to help them be successful. In due course, AI/ML will be the competitive advantage that companies will need to adopt in order to stay relevant and sustain businesses.

Forrester predicts that one in five organisations will double down on "AI inside" - which is AI and ML embedded in their systems and operational practices.

AI and ML are powerful technology tools that hold the key to achieving an organization's digital transformation goals.

(The author is Head-Technology Cloud, Oracle India.)

View original post here:
How leveraging AI and machine learning can give companies a competitive edge - Business Today

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it.

Move that computer vision system to a self-driving car and the stakes become much higher for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street.

To overcome these errors, MIT researchers have developed a framework that helps machines see the world more like humans do reports MIT News. Their new artificial intelligence system for analyzing scenes learns to perceive real-world objects from just a few images, and perceives scenes in terms of these learned objects.

The researchers built the framework using probabilistic programming, an AI approach that enables the system to cross-check detected objects against input data, to see if the images recorded from a camera are a likely match to any candidate scene. Probabilistic inference allows the system to infer whether mismatches are likely due to noise or to errors in the scene interpretation that need to be corrected by further processing.

This common-sense safeguard allows the system to detect and correct many errors that plague the deep-learning approaches that have also been used for computer vision. Probabilistic programming also makes it possible to infer probable contact relationships between objects in the scene, and use common-sense reasoning about these contacts to infer more accurate positions for objects.

If you dont know about the contact relationships, then you could say that an object is floating above the table that would be a valid explanation. As humans, it is obvious to us that this is physically unrealistic and the object resting on top of the table is a more likely pose of the object. Because our reasoning system is aware of this sort of knowledge, it can infer more accurate poses. That is a key insight of this work, says lead author Nishad Gothoskar, an electrical engineering and computer science (EECS) PhD student with the Probabilistic Computing Project.

In addition to improving the safety of self-driving cars, this work could enhance the performance of computer perception systems that must interpret complicated arrangements of objects, like a robot tasked with cleaning a cluttered kitchen.

Gothoskars co-authors include recent EECS PhD graduate Marco Cusumano-Towner; research engineer Ben Zinberg; visiting student Matin Ghavamizadeh; Falk Pollok, a software engineer in the MIT-IBM Watson AI Lab; recent EECS masters graduate Austin Garrett; Dan Gutfreund, a principal investigator in the MIT-IBM Watson AI Lab; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences (BCS) and a member of the Computer Science and Artificial Intelligence Laboratory; and senior author Vikash K. Mansinghka, principal research scientist and leader of the Probabilistic Computing Project in BCS. The research is being presented at the Conference on Neural Information Processing Systems in December.

A blast from the past

To develop the system, called 3D Scene Perception via Probabilistic Programming (3DP3), the researchers drew on a concept from the early days of AI research, which is that computer vision can be thought of as the inverse of computer graphics.

Computer graphics focuses on generating images based on the representation of a scene; computer vision can be seen as the inverse of this process.Gothoskar and his collaborators made this technique more learnable and scalable by incorporating it into a framework built using probabilistic programming.

Probabilistic programming allows us to write down our knowledge about some aspects of the world in a way a computer can interpret, but at the same time, it allows us to express what we dont know, the uncertainty. So, the system is able to automatically learn from data and also automatically detect when the rules dont hold, Cusumano-Towner explains.

In this case, the model is encoded with prior knowledge about 3D scenes. For instance, 3DP3 knows that scenes are composed of different objects, and that these objects often lay flat on top of each other but they may not always be in such simple relationships. This enables the model to reason about a scene with more common sense.

Learning shapes and scenes

To analyze an image of a scene, 3DP3 first learns about the objects in that scene. After being shown only five images of an object, each taken from a different angle, 3DP3 learns the objects shape and estimates the volume it would occupy in space.

If I show you an object from five different perspectives, you can build a pretty good representation of that object. Youd understand its color, its shape, and youd be able to recognize that object in many different scenes, Gothoskar says.

Mansinghka adds, This is way less data than deep-learning approaches. For example, the Dense Fusion neural object detection system requires thousands of training examples for each object type. In contrast, 3DP3 only requires a few images per object, and reports uncertainty about the parts of each objects shape that it doesnt know.

The 3DP3 system generates a graph to represent the scene, where each object is a node and the lines that connect the nodes indicate which objects are in contact with one another. This enables 3DP3 to produce a more accurate estimation of how the objects are arranged. (Deep-learning approaches rely on depth images to estimate object poses, but these methods dont produce a graph structure of contact relationships, so their estimations are less accurate.)

Outperforming baseline models

The researchers compared 3DP3 with several deep-learning systems, all tasked with estimating the poses of 3D objects in a scene.

In nearly all instances, 3DP3 generated more accurate poses than other models and performed far better when some objects were partially obstructing others. And 3DP3 only needed to see five images of each object, while each of the baseline models it outperformed needed thousands of images for training.

When used in conjunction with another model, 3DP3 was able to improve its accuracy. For instance, a deep-learning model might predict that a bowl is floating slightly above a table, but because 3DP3 has knowledge of the contact relationships and can see that this is an unlikely configuration, it is able to make a correction by aligning the bowl with the table.

I found it surprising to see how large the errors from deep learning could sometimes be producing scene representations where objects really didnt match with what people would perceive. I also found it surprising that only a little bit of model-based inference in our causal probabilistic program was enough to detect and fix these errors. Of course, there is still a long way to go to make it fast and robust enough for challenging real-time vision systems but for the first time, were seeing probabilistic programming and structured causal models improving robustness over deep learning on hard 3D vision benchmarks, Mansinghka says.

In the future, the researchers would like to push the system further so it can learn about an object from a single image, or a single frame in a movie, and then be able to detect that object robustly in different scenes. They would also like to explore the use of 3DP3 to gather training data for a neural network. It is often difficult for humans to manually label images with 3D geometry, so 3DP3 could be used to generate more complex image labels.

The 3DP3 system combines low-fidelity graphics modeling with common-sense reasoning to correct large scene interpretation errors made by deep learning neural nets. This type of approach could have broad applicability as it addresses important failure modes of deep learning. The MIT researchers accomplishment also shows how probabilistic programming technology previously developed under DARPAs Probabilistic Programming for Advancing Machine Learning (PPAML) program can be applied to solve central problems of common-sense AI under DARPAs current Machine Common Sense (MCS) program, says Matt Turek, DARPA Program Manager for the Machine Common Sense Program, who was not involved in this research, though the program partially funded the study.

Additional funders include the Singapore Defense Science and Technology Agency collaboration with the MIT Schwarzman College of Computing, Intels Probabilistic Computing Center, the MIT-IBM Watson AI Lab, the Aphorism Foundation, and the Siegel Family Foundation.

Republished with permission ofMIT News. Read theoriginal article.

Visit link:
Machines that see the world more like humans do - Big Think

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.

Like Loading...

Related

See the original post:
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