The role of artificial intelligence in healthcare has been a huge talking point in recent months and theres no sign of the adoption of this technology slowing down, well, ever really.

AI in healthcare has huge and wide reaching potential with everything from mobile coaching solutions to drug discovery falling under the umbrella of what can be achieved with machine learning.

That being said, many healthcare executives are still too shy when it comes to experimenting with AI due to privacy concerns, data integrity concerns or the unfortunate presence of various organizational silos making data sharing next to impossible. Weve covered the main barriers to adopting AI in healthcare here.

However, the future of healthcare & the future of machine learning and artificial intelligence are deeply interconnected.

Following our comprehensive guides on Artificial Intelligence in Pharma and Blockchain in Healthcare, weve decided to take a closer look at how the healthcare industry is positively impacted by the rise in popularity of artificial intelligence.

But first, a definition:

Artificial intelligence in healthcare refers to the use of complex algorithms designed to perform certain tasks in an automated fashion. When researchers, doctors and scientists inject data into computers, the newly built algorithms can review, interpret and even suggest solutions to complex medical problems.

Applications of Artificial Intelligence in healthcare are endless. That much we know.

We also know that weve only scratched the surface of what AI can do for healthcare. Which is both amazing and frightening at the same time.

At the highest level, here are some of the current technological applications of AI in healthcare you should know about (some will be explored further in the article while some use cases have gotten their own standalone articles on HealthcareWeekly already).

Medical diagnostics: the use of Artificial Intelligence to diagnose patients with specific diseases. Check out our roundup report from industry experts here. Also, a report AI platform was announced in March 2019 which is expected to help identify and anticipate cancer development.

Drug discovery: There are dozens of health and pharma companies currently leveraging Artificial Intelligence to help with drug discovery and improve the lengthy timelines and processes tied to discovering and taking drugs all the way to market. If this is something youre interested in, check our report titled Pharma Industry in the Age of Artificial Intelligence: The Future is Bright.

Clinical Trials: Clinical Trials are, unfortunately, a real mess. Most clinical trials are managed offline with no integrated solutions that can track progress, data gathering and drug tria outcomes. Read about how Artificial Intelligence is reshaping clinical trials here. Also, you may also be interested in the Healthcare Weekly podcast episode with Robert Chu, CEO @ Embleema where we talk about how Embleema is using AI and blockchain to revolutionize clinical trials. If Blockchain in healthcare is your thing, you may also be interested in our Global Blockchain in Healthcare Report: the 2019 ultimate guide for every executive.

Pain management: This is still an emergent focus area in healthcare. As it turns out, by leveraging virtual reality combined with artificial intelligence, we can create simulated realities that can distract patients from the current source of their pain and even help with the opioid crisis. You can read more about how this works here. Another great example of where AI and VR meet is the Johnson and Johnson Reality Program which weve covered at length here. In short, J&J has created a simulated environment which used rules-based algorithms to train physicians in a simulated environment to get better at their job.

Improving patient outcomes: Patients outcomes can be improved through a wide variety of strategies and outcomes driven by artificial intelligence. To begin with, check our report on 10 ways Amazons Alexa is revolutionizing healthcare and our Healthcare Weekly Podcast with Helpsys CEO Sangeeta Agarwal. Helpsy has developed the first Artificial Intelligence nurse in the form of a chatbot which assists patients at every stage of the way in their battle with cancer.

These are just a few examples and theyre only meant to quickly give you a flavor of what artificial intelligence in healthcare is all about. Lets dig into more specific examples that every healthcare executive should be aware of in 2019.

Artificial intelligence in the medical field relies on the analysis and interpretation of huge amounts of data sets in order to help doctors make better decisions, manage patient data information effectively, create personalized medicine plans from complex data sets and discover new drugs.

Lets look at each of these amazing use-cases in more details.

AI in healthcare can prove useful within clinical decision support to help doctors make better decisions faster with pattern recognition of health complications that are registered far more accurately than by the human brain.

The time saved and the conditions diagnosed are vital in an industry where the time taken and decisions made can be life-altering for patients.

AI in healthcare is a great addition to the information management for both physician and patient. With patients getting to doctors faster, or not at all when telemedicine is employed, valuable time and money are saved, taking the strain off of healthcare professionals and increasing comfort of patients.

Doctors can also further their learning and increase their abilities within the job through AI-driven educational modules, further showing the information management capabilities of AI in healthcare.

Around $5bn was invested into AI companies in 2016 and its no surprise that healthcare is up there with one of the fastest growing sectors. The healthcare industry is expected to get more than $6.6bn in investments by 2021.

There are 4 main machine learning initiatives within the top 5 pharmaceutical and biotechnology companies ranging from mobile coaching solutions and telemedicine to drug discovery and acquisitions.

Mobile coaching solutions come in the form of advising patients and improving treatment outcomes using real-time data collection. Theres a huge push in telemedicine in recent years too with companies employing AI for minor diagnosis within smartphone apps.

The ability to analyze large amounts of patient data to identify treatment options. The technology is able to identify treatment options through cloud-based systems able to process natural language.

Acquisitions continue to feed to innovation needs of both large and old biotech firms and with the development of AI, theres plenty to offer up when it comes to company control.

With startups combining the world of AI and healthcare, theres more choice for older and larger companies to acquire information, systems and even the people responsible for leaps and bounds in technology.

Drug discovery is another great place for AI to slip in with pharma companies able to include cutting-edge technology into the expensive and lengthy process of drug discovery.

The benefits of AI are instantly apparent with the focus on time-saving and pattern recognition upon testing and identification of new drugs.

In early-stage drug discovery, start-ups such as BenevolentAI or Verge Genomics are known to adopt algorithms which comb through portions of data for patterns too complex for humans to identify, saving both time and innovating in a way that we otherwise may not have been able to.

Insilico, another company with a heavy AI focus, has taken a different approach by using AI to design treatments not yet found in either nature of chemical libraries. An approach of using AI to simulate clinical trials before human trials have also been seen, leaving plenty of scope available for what AI can create.

For more information regarding how AI used in pharma, click here.

Growth opportunities may be hard to come by without significant investment from companies, but a major opportunity exists in the self-running engine for growth within the artificial intelligence sector of healthcare.

AI applications within the healthcare industry have the potential to create $150 billion in savings annually for the United States, a recent Accenture study estimates, by 2026. With AI in healthcare funding reading historic highs of $600m in equity funding (Q218) there are huge projected equity funding deals and equity deals as the years continue.

We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10 Bill Gates

Saliently, AI represents a significant opportunity for bottom line growth with the introduction of AI into the healthcare sector, with a combined expected 2026 value of $150bn:

The growth, however, is not unexpected and with the needs of the healthcare industry of which AI fits the gap its a match made in heaven.

With the predicted 2026 value of robot-assisted surgery, virtual nursing assistants and administrative workflow assistance are expected to be valued at $40bn, $20bn and $18bn respectively, its the numbers that come with claims that are the most impressive.

Although AI in healthcare has huge potential, as with most developments in the technological space, there are a number of known current limitation.

Experiencing teething problems with the introduction of any new technology is not rare, but must be overcome for large scale adoption of AI to occur in the healthcare market.

Ultimately, the adoption of AI will attract stakeholders who will invest in AI and successful case studies need to be highlighted and presented for future encouragement. These case studies will require some early adopters of healthcare companies to kickstart the process.

Privacy within healthcare is, by nature, extremely sensitive and thus confidential.

For utmost confidence in the technology, systems should be put in place to ensure data privacy and protection from hackers. Unfortunately, data breaches continue to be a common occurrence as reported before when UW Medicine exposed 1 million patient records or with Missouri Medicaid.

But privacy concerns should not be a deterrent from adopting artificial intelligence in the healthcare space. In fact, last year we did a story on how Artificial Intelligence can actually help healthcare data security.

HIPAA and a number of other patient data laws are subject to the approval of governing organizations e.g. FDA to ensure that federal standards are maintained.

The sharing of data among a variety of databases poses challenges to the HIPAA compliance and care must be taken around these areas if future developments are to succeed. As companies developing software, therefore AI, are also required to comply with Hitrust rules, current rules are regulations are definitely known to be a barrier to AI adoption.

Deep learning, AI and machine learning do not have the ability to ask the question why?. As a result, the logic behind decisions is not justified, meaning mostly guesswork is required to how the decision was made.

How and why the decision has been made is key to the information within the treatment plan. With a lack of reasoning can come a lack of confidence within the decision, potentially rendering the technology as unreliable or untrustworthy by both patients and professionals.

When it comes to the stakeholders within the adoption of AI in healthcare, everyone, including patients, insurance companies, pharma companies, healthcare workers etc. are key.

Resistance to pursue the technology at any of the aforementioned levels would result in issues and potential failure to the incorporation of the technology in the macro. Stakeholdering is one of the top ten reasons why the healthcare industry as a whole is not innovating enough in 2019.

Diagnostic errors account for 60% of all medical errors and an estimated 40,000 to 80,000 deaths each year. As a result, artificial intelligence has been employed in a variety of different areas in a bid to reduce the toll and number of errors made by human judgement.

That said, there continues to be significant pushback when it comes to AI adoption in the clinical decision support process as scientists and medical personnel continue to approach the topic of AI with incredible caution.

With minimal operator training needed and design with common output formats that directly interface with other medical software and health record systems, the system is incredibly easy to use and simple to implement.

A clear output from the system allows 60 seconds to identify whether the exam quality was of sufficient quality, the patient is negative for referable DR or the patient has signs of referable DR. Following signs of referable DR, further action in the form of a human grader over-reading, teleconsultation and/or referral to an ophthalmologist may be suggested.

Despite some setbacks and limitations, Artificial Intelligence in healthcare are virtually announced every day. In this section, we will cover some of the most remarkable and revolutionary uses of AI in healthcare with an understanding that this list is by no means complete and definitely a work in progress.

With the launch of the Apple Watch Series 4 and the new electrodes found within the gadget, its now possible for users to take an ECG directly from the wrist.

The Apple Watch Series 4 is the very first direct-to-consumer product that enables users to get a electrocardiogram directly from their wrist. The app that permits the readings provides vital data to physicians that may otherwise be missed. Rapid and skipped heartbeats are clocked and users will now receive a notification if an irregular heart rhythm (atrial fibrillation) is detected.

The number of accessories and add ons that technology companies are releasing for the Apple Watch is also beginning to crossover into the health industry. Companies, such as AliveCor have released custom straps that allow a clinical grade wearable ECG that replaces the original Apple Watch band. Although the strap may be rendered useless with the Series 4, for any of the earlier watches, the strap may prove a useful attachment to identify AF.

In addition, earlier this year, Omron Healthcare made the news when they deployed a new smart watch, called Omron HeartGuide. The watch can take a users blood pressure on the go while interpreting blood pressure data to provide actionable insights to users on a daily basis.

Last year, Fitbit released their signature Charge 3 wristband which uses Artificial Intelligence to detect sleep apnea.

What all these examples have in common is how wearable technologies are slowly being repurposed or augmented to improve medical outcomes. And in all these examples, artificial intelligence is leveraged, under the hood, to collect, analyze and interpret massive amounts of data which can improve the quality of life of patients everywhere.

Late 2018 marked the announcement from Aidoc that it had been granted its U.S. FDA clearance of its first AI-based workflow solution, the diagnosis of bleeds on the brain.

The systems created work with radiologists to flag acute intracranial haemorrhage (ICH), or bleeds on the brain, in CT scans. With over 75 percent of all patient care involving cardiovascular diseases, the workload on radiologists is massive.

Integration into the health industry is simple and wont require significant IT time and with additional hardware not required, its a simple resource that can be set up and maintained remotely. With a solution to assist workflow optimizations and increase the number of correct and high-quality scans, the demand for this AI-enabled technology is expected to be huge.

IDxhas developed an AI diagnostic system, IDx-DR, that autonomously analyzes images of the retina for signs of diabetic retinopathy. The software has received FDA approval to be used in the US.

1. Using a fundus camera, a trained operator captures two-color, 45 FOV images per eye

2. The images are transferred to the IDx-DR client on a local computer

3. The images are then submitted to the IDx-DR analysis system

4. Inside 60 seconds, IDx-DR provides an image quality or disease output and follow-up care instructions

5. If negative for mtmDR, the patient can be rescreened at a later date. If positive for mtmDR, refers the patient to eye care.

iCAD announced the launch of iReveal back in 2015 with the goal to monitor breast density via mammography to support accurate decisions in breast cancer screening.

With an estimated 40% of women in the US having dense breast tissue that can block the mammography from viewing potential cancerous tissue, the issue is huge and a solution was imperative.

The technology uses AI to assess breast density in order to identify patients that may experience reduced sensitivity to digital mammography due to dense breast tissue.

Ken Ferry, CEO of iCAD stated that With iReveal, radiologists may be better able to identify women with dense breasts who experience decreased sensitivity to cancer detection with mammography.

Mr. also Ferry added that The increasing support for the reporting of breast density across the US, there is a significant opportunity to drive adoption of iReveal by existing users of the PowerLook AMP platform and with new customers, which represents an incremental $100 million market opportunity over the next few years. Longer-term, we plan to integrate the iReveal technology into our Tomosynthesis CAD product, which is the next large growth opportunity for our Cancer Detection business.

Ultimately, the system remains at the forefront of breast cancer identification in women in the U.S. and with so many lives expected to be saved, I think everyone can agree what a fantastic use of AI it is.

QuantX is the first MRI workstation to provide a true computer-aided diagnosis, delivering an AI-based set of tools to help radiologists in assessment and characterization of breast abnormalities.

Using MR image data, QuantX uses a deep database of known outcomes and combines this with advanced machine learning and quantitative image analysis for real-time analytics during scans. A fast comprehensive display is seen with all processing on-demand in real-time with rapid display and reformatting of MPR, full MIPs, thin MIPs and subtractions.

A QI Score, a clinical metric correlated to the likelihood of malignancy is calculated with the images and regions of interest during scans. This is paired with a similar case compare, a tool which allows up to 45 similar cases from a reference library to be displayed for each analyzed lesion.

This information is passed on to radiologists to make accurate clinical decisions, decreasing the number of incorrect diagnoses in high-risk environments.

Coronary calcium scoring is a biomarker of coronary artery disease and quantification of this coronary calcification is a very strong predictor for cardiovascular events, including heart attacks or strokes.

A conventional coronary calcium scoring requires dedicated cardiac, ECG gated CT performed with and without contrast.

However, in recent times, a reliable derivation of coronary calcium score has been found algorithmically with the use of AI from low dose chest CT data. Zebra Medicals scoring algorithm uses these standard, non-contrast Chest CTs and automatically calculates the Coronary Calcium Scores.

The tool is vital for the early detection of people at high risk of severe cardiovascular events that otherwise would not be aware of the risk without extensive testing.

San Francisco-based privately held company Bay Labs gained FDA approval in June 2018 for the fully automated and AI-based selection of the left ventricular ejection fraction (EF). Note that Healthcare Weekly has included Bay Labs is our list of the most promising healthcare startups to watch for in 2019.

With EF noted as the single most widely used metric of cardiac function, used as the basis for numerous clinical decisions, Bay Labs AI based EchoMD and AutoEF algorithms work to reduce the errors and minimise workflow that surrounds the industry. The algorithms eliminate the need to manually select views, choose the best clips, and manipulate them for quantification, which is often noted as a particularly time-consuming and highly variable process.

The algorithms automatically review all relevant information and digital clips from a patients echocardiography study and proceeds to rate accordingly with image quality as the focus criteria. What may be most impressive about Bay Labs artificial intelligence solution is the method that the system learned clip selection in which over 4 million images were used to maximise algorithm success.

Ultimately, EchoMD and AutoEF will strive to maximise workflow efficiency while reducing the error in clinical decision making by helping physicians make correct choices.

Neural Analytics, a medical device company tackling brain health, announced a device for paramedic stroke diagnosis back in 2017, revolutionising the way that paramedics diagnose stroke victims.

Neural Analytic Lucid M1 Transcranial Doppler Ultrasound System tackles the issues of expensive and time-consuming stroke diagnosis for patients that suffer blood flow disorders.

This ultrasound system is designed for measuring cerebral blood flow velocities. This is no joke. Is successful, this technology will change how early doctors can detect stroke and could drastically improve patient outcomes.

The use of Transcranial Doppler (TCD), a type of ultrasound, allows for AI to assess the brains blood vessels from outside the body, preventing the need for more invasive tests. The AI software helps physicians detect stroke and other brain disorders caused by blood flow issues, increasing the capability of correct clinical decisions.

Icometrix is a company with the mission to transform patient care through imaging AI. With MRI brain interpretation used to decrease error in clinical diagnosis, the company is well on the way to changing the way that abnormalities are discovered within the brain.

The system developed objectively quantifies brain white matter abnormalities in patients, decreasing the amount of time taken, increasing the accuracy and improving patient care for those with brain issues. Changes in the brain are confidently evaluated with a focus on the structure with utmost accuracy. The system allows an increased sensitivity and augmented detection, ultimately leading to improved healthcare.

With quantification of clinically relevant brain structures for individual patients and a range of identifiable neurological disorders, theres plenty that AI had to offer in the space.

The OsteoDetect software is an AI based detection/diagnostic software that utilises intelligent algorithms that analyze two-dimensional X-rays.

The software searches for damage in the bone, specifically a common wrist fracture called the distal radius fracture. The software utilises the machine learning techniques to identify these problem areas and mark the location of the fracture on the image, assisting the physician with identification of a break.

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Artificial Intelligence in Healthcare: the future is amazing ...

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions.

Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

]]>[Master Deep Learning and build a career in AI, with this highly sought after course from Coursera.]

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What is Artificial Intelligence (AI)? - Definition from ...

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In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his "mother", Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001

Gross USA: $78,616,689

Cumulative Worldwide Gross: $235,926,552

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) - IMDb

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

AI can be categorized as either weak or strong. Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple's Siri, are a form of weak AI. Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities. When presented with an unfamiliar task, a strong AI system is able to find a solution without human intervention.

Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings, as well as access to Artificial Intelligence as a Service (AIaaS) platforms. AI as a Service allows individuals and companies to experiment with AI for various business purposes and sample multiple platforms before making a commitment. Popular AI cloud offerings include Amazon AI services, IBM Watson Assistant, Microsoft Cognitive Services and Google AI services.

While AI tools present a range of new functionality for businesses ,the use of artificial intelligence raises ethical questions. This is because deep learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human selects what data should be used for training an AI program, the potential for human bias is inherent and must be monitored closely.

Some industry experts believe that the term artificial intelligence is too closely linked to popular culture, causing the general public to have unrealistic fears about artificial intelligence and improbable expectations about how it will change the workplace and life in general. Researchers and marketers hope the label augmented intelligence, which has a more neutral connotation, will help people understand that AI will simply improve products and services, not replace the humans that use them.

Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

AI is incorporated into a variety of different types of technology. Here are seven examples.

Artificial intelligence has made its way into a number of areas. Here are six examples.

The application of AI in the realm of self-driving cars raises security as well as ethical concerns. Cars can be hacked, and when an autonomous vehicle is involved in an accident, liability is unclear. Autonomous vehicles may also be put in a position where an accident is unavoidable, forcing the programming to make an ethical decision about how to minimize damage.

Another major concern is the potential for abuse of AI tools. Hackers are starting to use sophisticated machine learning tools to gain access to sensitive systems, complicating the issue of security beyond its current state.

Deep learning-based video and audio generation tools also present bad actors with the tools necessary to create so-called deepfakes , convincingly fabricated videos of public figures saying or doing things that never took place .

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque. Europe's GDPR puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications.

In 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered. Since that time the issue has received little attention from lawmakers.

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What is AI (artificial intelligence)? - Definition from ...

2010 doesnt seem all that long ago. Facebook was already a giant, time-consuming leviathan; smartphones and the iPad were a daily part of peoples lives; The Walking Dead was a big hit on televisions across America; and the most talked-about popular musical artists were the likes of Taylor Swift and Justin Bieber. So pretty much like life as we enter 2020, then? Perhaps in some ways.

One place that things most definitely have moved on in leaps and bounds, however, is on the artificial intelligence front. Over the past decade, A.I. has made some huge advances, both technically and in the public consciousness, that mark this out as one of the most important ten year stretches in the fields history. What have been the biggest advances? Funny you should ask; Ive just written a list on exactly that topic.

To most people, few things say A.I. is here quite like seeing an artificial intelligence defeat two champion Jeopardy! players on prime time television. Thats exactly what happened in 2011, when IBMs Watson computer trounced Brad Rutter and Ken Jennings, the two highest-earning American game show contestants of all time at the popular quiz show.

Its easy to dismiss attention-grabbing public displays of machine intelligence as being more about hype-driven spectacles than serious, objective demonstrations. What IBM had developed was seriously impressive, though. Unlike a game such as chess, which features rigid rules and a limited board, Jeopardy! is less easily predictable. Questions can be about anything and often involve complex wordplay, such as puns.

I had been in A.I. classes and knew that the kind of technology that could beat a human at Jeopardy! was still decades away, Jennings told me when I was writing my book Thinking Machines. Or at least I thought that it was. At the end of the game, Jennings scribbled a sentence on his answer board and held it up for the cameras. It read: I for one welcome our new robot overlords.

October 2011 is most widely remembered by Apple fans as the month in which company co-founder and CEO Steve Jobs passed away at the age of 56. However, it was also the month in which Apple unveiled its A.I. assistant Siri with the iPhone 4s.

The concept of an A.I. you could communicate with via spoken words had been dreamed about for decades. Former Apple CEO had, remarkably, predicted a Siri-style assistant back in the 1980s; getting the date of Siri right almost down to the month. But Siri was still a remarkable achievement. True, its initial implementation had some glaring weaknesses, and Apple arguably has never managed to offer a flawless smart assistant.Nonetheless, it introduced a new type of technology that was quickly pounced on for everything from Google Assistant to Microsofts Cortana to Samsungs Bixby.

Of all the tech giant, Amazon has arguably done the most to advance the A.I. assistant in the years since. Its Alexa-powered Echo speakers have not only shown the potential of these A.I. assistants; theyve demonstrated that theyre compelling enough to exist as standalone pieces of hardware. Today, voice-based assistants are so commonplace they barely even register. Ten years ago most people had never used one.

Deep learning neural networks are not wholly an invention of the 2010s. The basis for todays artificial neural networks traces back to a 1943 paper by researchers Warren McCulloch and Walter Pitts. A lot of the theoretical work underpinning neural nets, such as the breakthrough backpropagation algorithm, were pioneered in the 1980s. Some of the advances that lead directly to modern deep learning were carried out in the first years if the 2000s with work like Geoff Hintons advances in unsupervised learning.

But the 2010s are the decade the technology went mainstream. In 2010,researchers George Dahl and Abdel-rahman Mohamed demonstrated that deep learning speech recognition tools could beat what were then the state-of-the-art industry approaches. After that, the floodgates were opened.From image recognition (example: Jeff Dean and Andrew Ngs famous paper on identifying cats) to machine translation, barely a week went by when the world wasnt reminded just how powerful deep learning could be.

It wasnt just a good PR campaign either, the way an unknown artist might finally stumble across fame and fortune after doing the same way in obscurity for decades. The 2010s are the decade in which the quantity of data exploded, making it possible to leverage deep learning in a way that simply wouldnt have been possible at any previous point in history.

Of all the companies doing amazing AI work, DeepMind deserves its own entry on this list. Founded in September 2010, most people hadnt heard of deep learning company DeepMind until it was bought by Google for what seemed like a bonkers $500 million in January 2014. DeepMind has made up for it in the years since, though.

Much of DeepMinds most public-facing work has involved the development of game-playing AIs, capable of mastering computer games ranging from classic Atari titles like Breakout and Space Invaders (with the help of some handy reinforcement learning algorithms) to, more recently, attempts at StarCraft II and Quake III Arena.

Demonstrating the core tenet of machine learning, these game-playing A.I.s got better the more they played. In the process, they were able to form new strategies that, in some cases, even their human creators werent familiar with. All of this work helped set the stage for DeepMinds biggest success of all

As this list has already shown, there are no shortage of examples when it comes to A.I. beating human players at a variety of games. But Go, a Chinese board game in which the aim is to surround more territory than your opponent, was different. Unlike other games in which players could be beaten simply by number crunching faster than humans are capable of, in Go the total number of allowable board positions is mind-bogglingly staggering: far more than the total number of atoms in the universe. That makes brute force attempts to calculate answers virtually impossible, even using a supercomputer.

Nonetheless, DeepMind managed it. In October 2015, AlphaGo became the first computer Go program to beat a human professional Go player without handicap on a full-sized 1919 board. The next year, 60 million people tuned in live to see the worlds greatest Go player, Lee Sedol, lose to AlphaGo. By the end of the series AlphaGo had beaten Sedol four games to one.

In November 2019, Sedol announced his intentions to retire as a professional Go player. He cited A.I. as the reason.Even if I become the number one, there is an entity that cannot be defeated, he said.Imagine if Lebron James announced he was quitting basketball because a robot was better at shooting hoops that he was. Thats the equivalent!

In the first years of the twenty-first century, the idea of an autonomous car seemed like it would never move beyond science fiction. In MIT and Harvard economists Frank Levy and Richard Murnanes 2004 book The New Division of Labor, driving a vehicle was described as a task too complex for machines to carry out. Executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a drivers behavior, they wrote.

In 2010, Google officially unveiled its autonomous car program, now called Waymo. Over the decade that followed, dozens of other companies (including tech heavy hitters like Apple) have started to develop their own self-driving vehicles. Collectively these cars have driven thousands of miles on public roads; apparently proving less accident-prone than humans in the process.

Foolproof full autonomy is still a work-in-progress, but this was nonetheless one of the most visible demonstrations of A.I. in action during the 2010s.

The dirty secret of much of todays A.I. is that its core algorithms, the technologies that make it tick, were actually developed several decades ago. Whats changed is the processing power available to run these algorithms and the massive amounts of data they have to train on. Hearing about a wholly original approach to building A.I. tools is therefore surprisingly rare.

Generative adversarial networks certainly qualify. Often abbreviated to GANs, this class of machine learning system was invented by Ian Goodfellow and colleagues in 2014. No less an authority than A.I. expert Yann LeCun has described it as the coolest idea in machine learning in the last twenty years.

At least conceptually, the theory behind GANs is pretty straightforward: take two cutting edge artificial neural networks and pit them against one another. One network creates something, such as a generated image. The other network then attempts to work out which images are computer-generated and which are not. Over time, the generative adversarial process allows the generator network to become sufficiently good at creating images that they can successfully fool the discriminator network every time.

The power of Generative Adversarial Networks were seen most widely when a collective of artists used them to create original paintings developed by A.I. The result sold for a shockingly large amount of money at a Christies auction in 2018.

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Revisiting the rise of AI: How far has artificial intelligence come since 2010? - Digital Trends

We Counter Hate

One of the most fascinating examples of social innovation Ive been tracking recently was the We Counter Hate platform, by Seattle-based agency POSSIBLE (now part of Wunderman Thompson Seattle) that sought to reduce hate speech on Twitter by turning retweets of these hateful messages into donations for a good cause.

Heres how it worked: Using machine learning, it first identified hateful speech on the platform. A human moderator then selected the most offensive and most dangerous tweets and attached an undeletable reply, which informed recipients that if they retweet the message, a donation will be committed to an anti-hate group. In a beautiful twist this non-profit wasLife After Hate, a group that helps members of extremist groups leave and transition to mainstream life.

Unfortunately (and ironically) on the very day I reached out to the team, Twitter decided to allow users to hide replies in their feeds in an effort to empower people faced with bullying and harassment, eliminating the reply function which was the main mechanism that gave #WeCounterHate its power and led to it being able to remove more than 20M potentialhatespeech impressions.

Undeterred, I caught up with some members of the core teamShawn Herron, Jason Carmel and Matt Gilmoreto find out more about their journey.

(From left to right)Shawn Herron, Experience Technology Director @ Wunderman ThompsonMatt ... [+] Gilmore, Creative Director @ Wunderman ThompsonJason Carmel, Chief Data Officer @ Wunderman Thompson

Afdhel Aziz: Gentlemen, welcome. How did the idea for WeCounterHate come about?

Shawn Herron: It started when we caught wind of what the citizens of the town of Wunsiedel, Germany were doing to combat the annual extremists that were descending on their town every year to hold rally and march through the streets. The towns people had devised a peaceful way to upend the extremists efforts by turning their hateful march into an involuntary walk-a-thon that benefitted EXIT Deutschland, an organization that helps people escape extremist groups. For every meter the neo Nazis marched 10 euro would be donated to Exit Deutschland. The question became, how can we scale something like that so anyone, anywhere, could have the ability to fight against hate in a meaningful way?

Jason Carmel: We knew that, to create scale, it had to be digital in nature and Twitter seemed like the perfect problem in need of a solution. We figured if we could reduce hate on a platform of that magnitude, even a small percentage, it could have a big impact. We started by developing an innovative machine-learning and natural-language processing technology that could identify and classify hate speech.

Matt Gilmore: But we still needed the mechanic, a catch 22, that would present those looking to spread hate on the platform with a no-win decision to make. Thats when we stumbled onto the fact that Twitter didnt allow people to delete comments on their tweets. The only way to remove a comment was to delete the post entirely. That mechanic is what gave us a way put a permanent marker, in the form of an image and message, on tweets containing hate speech. Its that permanent marker that let those looking to retweet, and spread hate, know that doing so would benefit an organization theyre opposed to, Life After Hate. No matter what they chose to do, love wins.

Aziz: Fascinating. So, what led you to the partnership with Life After Hate and how did that work?

Carmel: Staffed and founded by former hate group members and violent extremists, Life After Hate is a non-profit that helps people in extremist groups break from that hate-filled lifestyle. They offer a welcoming way out thats free of judgement.We collaborated with them in training the AI thats used to identify hate speech in near real time on Twitter. With the benefit of their knowledge our AI can even find hidden forms of hate speech (coded language, secret emoji combinations) in a vast sea of tweets. Their expertise was crucial to align the language we used when countering hate, making it more compassionate and matter-of-fact, rather than confrontational.

Herron: Additionally, their partnership just made perfect sense on a conceptual level as the beneficiary of the effort. If youre one of those people looking to spread hate on Twitter, youre much less likely to hit retweet knowing that youll be benefiting an organization youre opposed to.

Aziz: Was it hard to wade through that much hate speech? What surprised you?

Herron: Being exposed to all the hate filled tweets was easily the most difficult part of the whole thing. The human brain is not wired to read and see the kinds of messages we encountered for long periods of time. At the end of the countering process, after the AI identified hate, we always relied on a human moderator to validate it before countering/tagging it. We broke up the shifts between many volunteers, but it was always quite difficult when it was your shift.

Carmel: We learned that the identification of hate speech was much easier than categorizing it. Or initial understanding of hate speech, especially before Life After Hate helped us, was really just the movie version of hate speech and missed a lot of hidden context. We were also surprised at how much the language would evolve relative to current events. It was definitely something we had to stay on top of.

We were surprised by how broad a spectrum of people the hate was coming from. We went in thinking wed just encounter a bunch of thugs, but many of these people held themselves out as academics, comedians, or historians. The brands of hate some of them shared were nuanced and, in an insidious way, very compelling.

We were caught off guard by the amount of time and effort those who disliked our platform would take to slam or discredit it. A lot of these people are quite savvy and would go to great lengths to attempt to undermine our efforts. Outside of the things we dealt with in Twitter, one YouTube hate-fluencer made a video, close to an hour long, that wove all sorts of intricate theories and conspiracies about our platform.

Gilmore: We were also surprised by how wrong our instincts were. When we first started, the things we were seeing made us angry and frustrated. We wanted to come after these hateful people in an aggressive way. We wanted to fight back. Life After Hate was essential in helping course-correct our tone and message. They helped us understand (and wed like more people to know) the power of empathy combined with education, and its ability to remove walls rather than build them between people. It can be difficult to take this approach, but it ultimately gets everyone to a better place.

Aziz: I love that idea - empathy with education.What were the results of the work youve done so far? How did you measure success?

Carmel: The WeCounterHate platform radically outperformed expectations of identifying hate speech (91% success) relative to a human moderator, as we continued to improve the model over the course of the project.

When @WeCounterHatereplied to a tweet containing hate, it reduces the spread of that hate by an average of 54%. Furthermore, 19% of the "hatefluencers" deleted their original tweet outright once it had been countered.

By our estimates, the Hate Tweets we countered were shared roughly 20 million fewer times compared to similar Hate Tweets by the same authors that werent countered.

Matt: It was a pretty mind-bending exercise for people working in an ad agency, that have spent our entire careers trying to gain exposure for the work do on behalf of clients, to suddenly be trying to reduce impressions. We even began referring to WCH as the worlds first reverse-media plan, designed to reduce impressions by stopping retweets.

Aziz: So now that the project has ended, how do you hope to take this idea forward in an open source way?

Herron: Our hope was to counter hate speech online, while collecting insightful data about how hate speech online propagates. Going forward, hopefully this data will allow experts in the field to address the hate speech problem at a more systemic level. Our goal is to publicly open source archived data that has been gathered, hopefully next quarter (Q1 2020)

I love this idea on so many different levels. The ingenuity of finding a way to counteract hate speech without resorting to censorship. The partnership with Life After Hate to improve the sophistication of the detection. And the potential for this same model to be applied to so many different problems in the world (*anyone want to build a version for climate change deniers?). It proves that the creativity of the advertising world can truly be turned into a force for good, and for that I salute the team at Possible for showing whats, well, possible.

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The Power Of Purpose: How We Counter Hate Used Artificial Intelligence To Battle Hate Speech Online - Forbes

Artificial Intelligence (AI) has always been a topic of discussion- is it good enough for us? Getting more and more into this high technology world will give us a better future or not? According to recent research, almost everyone has a different requirement for automation. And most of the work of humans is done by the latest high intelligence computers. You all must be familiar with the fact of how Artificial Intelligence is changing industries, like Medicine, Automobiles, and Manufacturing. Well, what about Architecture?

The main issue is about the fact that these high tech robots will actually replace the creator? Although these high tech computers are not good enough at some ideas and you have to rely on Human Intelligence for that. However, these can be used to save a lot of time by doing some time-consuming tasks, and we can utilize that time in creating some other designs.

Artificial Intelligence is a high technology mechanical system that can perform any task but needs a few human efforts like visual interpretation or design-making etc. AI works and gives the best results possible by analyzing tons of data, and thats how it can excel in architecture.

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While creating new designs, architects usually go through past designs and the data prepared throughout the making of the building. Instead of investing a lot of time and energy to create something new, it is alleged that a computer will be able to analyze the data in a short time period and will give recommendations accordingly. With this, an architect will be able to do testing and research simultaneously and sometimes even without pen and paper. It seems like it will lead to the organizations or the clients to revert to computers for masterplans and construction.

However, the value of architects and human efforts of analyzing a problem and finding the perfect solutions will always remain unchallenged.

Read More: How Automating Procurement is Like Self-Driving Cars

Parametric architecture is a hidden weapon that allows an architect to change specific parameters to create various types of output designs and create such structures that would not have been imagined earlier. It is like an architects programming language.

It allows an architect to consider a building and reframe it to fit into some other requirements. A process like this allows Artificial Intelligence to reduce the effort of an architect so that the architect can freely think about different ideas and create something new.

Constructing a building is not a one-day task as it needs a lot of pre-planning. However, this pre-planning is not enough sometimes, and you need a little bit of more effort to get an architects opinion to life. Artificial Intelligence will make an architects work significantly easier by analyzing the whole data and creating models that can save a lot of time and energy of the architect.

All in all, AI can be called an estimation tool for various aspects while constructing a building. However, when it comes to the construction part, AI can help so that human efforts become negligible.

The countless hours of research at the starting of any new project is where AI steps in and makes it easy for the architect by analyzing the aggregate data in millisecond and recommending some models so that the architect can think about the conceptual design without even using the pen or paper.

Just like while building a home for a family, if you have the whole information about the requirements of the family, you can simply pull all zoning data using AI and generate design variations in a short time period.

This era of modernization demands everything to be smartly designed. Just like smart cities, todays high technology society demands smart homes. However, now architects do not have to bother about how to use AI to create the designs of home only, but they should worry about making the users experience worth paying.

Change is something that should never change. The way your city looks today will be very different in the coming time. The most challenging task for an architect is city planning that needs a lot of precision planning. However, the primary task is to analyze all the possible aspects, and understand how a city will flow, how the population is going to be in the coming years.

All these factors are indicating one thing only, i.e., the future architects will give fewer efforts in the business of drawing and more into satisfying all the requirements of the user with the help of Artificial Intelligence.

Read More: How AI and Automation Are Joining Forces to Transform ITSM

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How is Artificial Intelligence (AI) Changing the Future of Architecture? - AiThority

tech2 News StaffDec 26, 2019 17:21:10 IST

Samsung has been teasing Neon for quite a while on social media. It appears to be an artificial intelligence (AI) project by its research arm and the company will be announcing more details about it during CES 2020 in January.

Samsung Neon AI project. Image: Neon

Neon hasnt really revealed any details. Its being developed under Samsung Technology & Advanced Research Labs (STAR Labs). STAR Labs could be a reference to the Scientific and Technological Advanced Research Laboratories (STAR Labs) from DC Comics, but we cant confirm that. Samsungs research division is led by Pranav Mistry who earlier worked on the Samsung Galaxy Gear and is now the President and CEO of STAR Labs.

The company has set up a website with a landing page that doesnt really mention any details. It only has a message saying, Have you ever met an Artificial? It has been continuously posting images on Twitter and Instagram, including a couple of videos. These images contain the same message in different languages as well, indicating that the AI has multilingual functionality. Mistry has also been teasing Neon on his own Twitter account.

This wont be Samsungs first venture into AI since it already has the Bixby digital assistant. However, it never really took off. CES 2020 begins on 7 January and well get to know more about Neon during the expo.

Find latest and upcoming tech gadgets online on Tech2 Gadgets. Get technology news, gadgets reviews & ratings. Popular gadgets including laptop, tablet and mobile specifications, features, prices, comparison.

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Samsung to announce its Neon artificial intelligence project at CES 2020 - Firstpost

The US is far ahead of everyone else but China is keen on taking the lead, soon. Source: Shutterstock

IN THE digital age, countries all around the world are racing to excel with artificial intelligent (AI) technology.

The phenomenon is not a surprise considering that that AI is undeniably a powerful solution with elaborate enterprise use across industries from medical algorithms to autonomous vehicles.

For a while now, the US has been dominating the global race in AI development and capabilities, but according to the Global AI Index, it seems like China will be dominating the field in the near future.

As the first runner up, it is expected that China will overtake the US in about 5 to 10 years, based on the countrys impressive growth records.

Based on 7 key indicators such as research, infrastructure, talent, development, operating environment, commercial ventures, and government strategy measured over the course of 12 months it looks like China is promoting growth unlike any other.

Although the US is prominently in the lead by a great margin, China has already materialized efforts to establish a bigger influence based on the countrys Next Generation Artificial Intelligence Development Plan which it launched in 2017.

Not only that, it is reported that China alone has promised to spend up to US$22 billion a mammoth figure compared to the global governmental AI spending estimated at US$35 billion throughout the next decade or so.

Nevertheless, China must recognize some areas that it needs to improve in order to successfully lead with AI.

Recording a 58.3 percent on the index, China seems to lack in terms of talent, commercial ventures, research quality, and private funding.

However, the country has still shown significant growth in various other areas. especially in the contribution of AI code. According to the worlds biggest open-source development platform, Github, China developers have contributed 13,000 AI codes to date.

This is a big jump compared to the initial count of 150 in 2015. The US, however, is still in the lead with a record of 42,000 contributions.

The need to dominate the AI market seems to be the motivation for countries around the world as the technology is a defining asset that can shift the dynamics of the global economy.

Other prominent countries to watch out for are the UK, Canada, and Germany, ranking 3rd, 4th, and 5th place consecutively.

Another Asian country making a mark in the 7th spot is Singapore, promoting a high score in talent but room for improvement in terms of its operative environment.

Despite the quick progress, experts hope that all countries looking to excel in AI will do so with ethical considerations and strategic leadership in mind.

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Who will really dominate artificial intelligence capabilities in the future? - Tech Wire Asia

With a view to promoting enterprises working on artificial intelligence solutions and taking leadership in this emerging technology space, the Telangana government has decided to observe 2020 as the Year of AI.

Telangana IT Minister KT Rama Rao will formally make the announcement on January 2 here, declaring 2020, the Year of AI, and release a calendar of events for the next 12 months.

The event will see signing of memorandum of agreements between the government and AI start-ups.

The Information and Technology Ministry is in the process of preparing a document with strategy framework to offer incentives exclusive to the AI initiatives.

We have come up with such documents for Blockchain and drones. With new technologies such as AI and Big Data Analytics expected to generate 8 lakh jobs in the country in the next two years, we will launch a dedicated programme for AI in 2020, Jayesh Ranjan, Principal Secretary, IT and Industries, Government of Telangana, has said.

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For Telangana, 2020 will be year of artificial intelligence - BusinessLine