To dwell more into the future of technology, we have divided artificial intelligence into five types

Every day, researchers are marking new milestones in the technology sphere.Artificial intelligenceis reaching unprecedented heights, taking humankind along with it.Artificial intelligencedefines the ability of machines or models to think and learn from experience. Starting from smart home applications and delivery systems to giantrobotsin factories and robotic surgeon, everything in the digital era is powered byartificial intelligenceand its sub-technologies.

After the technologygot congested with many achievements, researchers divided it into differenttypes of artificial intelligencefor their ease. While some types represent the currentAI-powered modelswe live with, others talk about the future we are headed to. The common and recurring view of the latest breakthrough inartificial intelligenceresearch is that sentiment and intelligent machines orrobotsare just on the horizon. These disruptivetypes of artificial intelligenceare opening the door for two main theories. One is based on the fear of a dystopian future whererobotsbecome arrogant and create an apocalypse and the other one is an optimistic future where humans and machines work together. To dwell more into the future of technology, Analytics Insight has listed fivetypes of artificial intelligence.

Along with the evolution of trends and concepts, human preferences have also changed. Nobody likes a trend from a century ago. However, businesses today are working to attract consumers by providing customized or personalized solutions. In the modern world, businesses finally realized that not everyone has the same taste and peoples likes and dislikes differ. Therefore, they have sought help from technology to create recommendation engines through which companies can engage better with their customers. The recommendations are made following their browsing history, preference, and interests. In the future, all businesses starting from small to big will seek artificial intelligences help to unravel the much-needed customized products.

Similar to customized services, artificial intelligence is making a breakthrough in suggestions, especially to those on social media pages. For example, if you have searched on Google to buy a sofa, then your social media apps like Instagram, Facebook, etc are likely to show a lot of furniture selling sites with sophisticated sofas. The same thing happens with your social media feed. On Instagram, artificial intelligence takes account of your likes and views and determines what posts might steal your eyes. It only shows posts that are similar to your area of interest. Facebook is partnering its feature with a tool called Deep Text, which helps in translating posts from different languages automatically. Twitter is also using a futuristic artificial intelligence algorithm to detect frauds, remove propaganda, and hateful comments on the microblogging platform.

So far, artificial intelligence-powered machines were having a hard time conversing with humans. Most of the robots or AI models were capable of doing either reading or writing or abstracting the content by using speech recognition. However, GPT-3 (Generative Pre-trained Transformer-3) changed the tailwind of human-machine interaction. Developed by OpenAI, the language processing tool trains an AI model to converse with humans, and read and write texts. The mechanism has extended its capabilities from just conversing with humans to doing other things like reading and writing. The company has trained GPT-3 with millions of data, making it capable of understanding everything that humans can when it comes to text and speech analysis.

It all started when humans wanted to create something similar to them. Yes, the whole concept of robots and artificial intelligence was born out of peoples curiosity to make a mechanism that thinks and functions like humans. Unfortunately, we are still at the first step when it comes to achieving that sophistication. Owing to the technological developments, researchers are putting immense efforts to unravel reciprocating machines that can respond to various types of simulations. Even though this oldest form of artificial intelligence system doesnt function through a memory base, it uses its reproductive ability to think like humans. Generally, machines are fed with data. When they are assigned a task, they go through the dataset and find similar tasks and carry out the same. However, instead of using previous experience or dataset, these reciprocating machines respond to the circumstance immediately. Unlike many AI models, this type of artificial intelligence system doesnt learn things and implement them, instead reacts to the situation. Besides, the reciprocating machines cant store their learning experience and use it for future endeavours. Every time, they have to come up with a solution themselves.

The recent developments in artificial intelligence are pointing to a future where machines can have a sixth sense like humans. Humans are the only living beings who fall under this category and experience emotions and get to think. The future of technology will unravel machines, especially, artificial intelligence-powered machines that can show emotions, have beliefs, sort what is right and wrong, think, know the situation, etc. In order to make this a reality, researchers are on their way to implement a multi-dimensional AI development concept called Theory of Mind. By attaching Theory of Mind to machines, they get the ability to understand entities they deal with. It will also unravel a whole new world of human-machine understanding that no one has ever seen.

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5 Types of Artificial Intelligence that will Shape 2021 and Beyond - Analytics Insight

Artificialintelligence could potentially help us streamline the morerudimentary and tedious aspects of design to free up more time forcreative problem solving and response to human needs. It would beunreasonable to say that any AI that we could conceive in our lifetimewould be able to intimately familiarize itself with the breadth ofhuman experience to allow it to make accurate determinationsabout the things we need. Oftentimes the things we need are rooted inaesthetics that are meant to facilitate our emotional well-being orthey are otherwise rooted in cultural lineages and traditions thatare difficult as yet to quantify.

Itis obvious that with more precise data, and with the use of moreintelligent software, architects and designers will be able toenhance their work in many predictable ways such as optimizingbuilding maintenance and security, streamlining BIM workflow, andincreasing the sustainability of a design through more accurate environmental analysis.Having said this, I feel that the notion of having better toolsallowing you to do your job better and faster is not necessarily worthdiscussing. What is of interest is whether or not improving thesetools will eventually make their very users obsolete by becoming theusers themselves. From this, it's important to consider what makeshumans particularly useful (as users of these tools).

Whatmakes a thing intelligent is not only access to data but also theability to identify connections between disparate pieces ofinformation and use those connectionsto solve problems to develop intuition. The way we identifyconnections between bits of data as humans is by working withincertain logical frameworks that allow us to create a relationshipbetween things that would otherwise feel random and/or arbitrary. Forexample, one such framework is cause and effect. You know not to lookdirectly at the sun because it hurts your eyes when you do that. Aconnection was drawn between the action and the subsequent pain thatinformed the newly-developed behavior that followed.

Architectsspend several years developing skills that give them a heighteneddegree of spatial awareness. Beyond that, many are also skilled atbridging gaps between their lived experience, their ability toidentify sociocultural cues and traditions (humans are particularlyskilled at this because we are the only species that placessentimental value onto objects, as far as our current understandinggoes) and their technical skills in order to come up with cleversolutions to a specific set of problems. For better or for worse, adesigners individualism seeps through when they make decisionsthat influence the emotional impact of space using their livedexperiences their memories as a basis.

Forthe same reason that it is difficult to imagine fully relating toanother human being because your lived experiences are fundamentallydifferent, it is difficult to imagine how digital intelligences wouldbegin to develop a sense of ego and then use that to make aestheticchoices about a space. If that were to happen, the point wouldessentially be moot because we would come to a point where it wouldbe redundant to distinguish between supposedly "real" humanityand a synthetic one. It wouldnt bea matter of asking what the future of architecture and design lookslike under the influence of AI because AI would not exist humanintelligence and artificial intelligence would essentially be thesame, and so any useful distinction (those that look beyond thepedantic notion of humans being made from organic matter as opposedto AI, which is created with synthetic material) will probablycease to exist. Would AI eventually reach a point where designersbecome obsolete? Probably not until we reach a level ofsophistication with AI that is indistinguishable from our owncomplexity, but in that case, it wouldnt be a matter of AI vs.designers, it would simply mean that there are more designers.

Anexample of how AI is already integrated in architecture can be seenat The Bartlett, wherein their space syntax software "depthmapX" can generate accurate spatial analyses that remove the need toactually visit the site.Granted, there is as yet no way for such a software to tell you, forinstance, how a certain place "feels" or how culturallysignificant certain elements at a site are, but any physical orspatial data that can be quantified is still perfectly fair game.This not actually limited to just environmental analysis. In much thesame way that analytics companies gather our social and behavioraldata to essentially generate profiles on us to create more successfulmarketing campaigns, in an architectural setting, this data can beused to democratize development. With this data, software may be able to prioritize certain projects, calculate population growth andcategorize streets or neighborhoods by usage and density (and thenfurther categorize those things into time of day).

Still moreinteresting integrations of AI in architecture can be seen in aninstallation called Ada as part of Microsoft's Artist in Residenceprogram. Ada is a pavilion that incorporates AI to generate aperformative environment based on analyses of its users. Itcollects data from facial expressions and vocal tones and translatesthat data into certain colors and materials based on specificsentiments that it perceives from this data.What it becomes is this vehicle for a uniquely responsivearchitecture that allows designers to expand their conceptualizationprocess to encompass not only what a certain building or space mustbe but also what it couldbe. The question that arises here is how this data is beingperceived and translated by the AI and who programs it to perceivethings in this way and these things are determined by a variety ofcultural and social biases. Perhaps the challenge will come fromattempting to get the AI to understand certain illogical humanbehaviors that are rooted in cultural stigma such as Americans' preference for private vehicles over robust public transportationnetworks and infrastructure. Logic isn't standardized becauseculture and experience inform a person's idea of what is logical.

Thethreat is actually posed not by artificial intelligence itself but byusers who deem AI to be a cheaper, more efficient means to an end. Aswe are encouraged to indulge in our consumerist tendencies, we becomeless concerned with creating spaces that we can emotionally connectto and see ourselves in and more about acquiring material things. Inthis case, it is about acquiring four walls and a roof as quickly and efficientlyas possible. While it is indeed possible for architectural firms toadapt to this and begin implementing AI technologies to help themfill in gaps in their output (such as Ada or depthmapX), largercompanies that have an edge in gathering data (especially if thatdata is deemed proprietary) will have negative influences on thecompetitive environment of the field.

Inorder to prevent the consolidation of an immense amount ofdecision-making power in the hands of a small group of alreadyresource-rich entities, architects and designers should aim toliberalize pertinent data so that anybody can have access to them.Data sets should be available for public use and perhaps managed byan international body. We see this occurring more and more frequentlyin the design world through the emergence of open-source programs,plans, and data such as Wheelmap, which is an urbanism platformdesigned to help people identify and share accessible spaces aroundthe world. Decentralizing design in this way may prove beneficial to society asa whole by giving more people greater access to quality design that ismost often reserved for people with the capital to access the finestpieces.

Sebastian Errazuriz has a rather bleak albeit realistic perspectiveregarding the impact of AI on the architecture industry. Approachingit purely from a brass tacks perspective, architects are largelyexpendable mainly because they take a lot of time and resources toget equipped with the skills needed to become architects. Beyondthat, the level of coordination between all these different entitiesmakes it so that it's normalfor projects to take 2, 3, or even 10 years to finish. How could anyof that possibly compete with a program that is unbiased andunburdened by ego, that can learn anything in a matter of seconds,and that can communicate and coordinate with other equally egolessprograms with complete fluidity (more fluidly than we can evencommunicate with our own selves). His suggestion is that architectsshould take their advanced spatial awareness and apply it in a techlandscape wherein they would apply their skills more abstractly todesign other kinds of systems.

Aswith almost every other profession, architects are on the precipiceof a reckoning with their roles in society moving forward. This ismainly due to the fact that we recognize that AI isnt just a tool it has the potential to eventually surpass our ability to doanything. What is particularly new about this is that it will causeus to fundamentally re-evaluate our relationship with our labor, andwhat our role in society will be if our ability (or even our need)to work is taken away. While it may not necessarily be a matter ofthe utmost urgency, it would be prudent for architects to reflect onhow they can synthesize the more intangible aspects of their skillsets in order to be more equipped to navigate these rapidly shiftingenvironments.

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What Is the Real Threat of Artificial Intelligence to the Architecture Profession? - Archinect

It turns out there are still more mysteries to uncover about theDead Sea Scrolls.

The latest discovery, made with the help of artificial intelligence, is that the artifacts were likely transcribed by two different writers, despite the fact that all the handwriting looks similar.

We will never know their names. But after 70 years of study, this feels as if we can finally shake hands with them through their handwriting, Mladen Popovi, a bible studies professor and a member of the three-person team from the University of Groningen in the Netherlandsbehind the study, said a statement. This opens a new window on the ancient world that can reveal much more intricate connections between the scribes that produced the scrolls.

Written on 17 sheets of parchment, the manuscript is 24 feet long and is the oldest complete copy of a book of the bible by about 1,000 years. Using A.I. pattern-recognition technology, experts singled out theHebrew letter aleph, which appears in the scroll over 5,000 times, to identify the hand of two main writers, reportsCourthouse News.

Kohonen maps (blue colormaps) of the character aleph and bet from the Dead Sea Scrolls Great Isaiah Scroll used to analyze the handwriting. Image courtesy of Maruf A. Dhali, University of Groningen.

The initial discovery of the first Dead Sea Scroll by a Bedouin shepherd in theQumran caves in 1947 proved one of the 20th centurys most significant archaeological finds.The scrolls, the earliest biblical manuscripts, are written primarily in Hebrew, with sections in Aramaic and Greek.

The new study is the part of European Research Council-funded 1.5 million ($1.8 million) The Hands that Wrote the Bible project. The first findings, published yesterday in the journalPLOS ONE, and presented earlier this month at the universitys Digital Palaeography and Hebrew/Aramaic Scribal Culture conference,offer fresh clues as to the origins of the scrolls, which are believed to be the work of a Jewish sect known as the Essenes.

Greyscale image of column 15 of the Dead Sea Scrolls Great Isaiah Scroll, the corresponding binarized image using BiNet, and the cleaned-corrected image. From the red boxes of the last two images, one can see how the rotation and the geometric transformation is corrected to yield a better image for further processing. Image courtesy of University of Groningen.

Examining each letter both as a whole and in microscopic detail, A.I. was able to identify minute differences in the way characters were formed.

The first step was using digital imaging to capture each aleph. Then, the researchers trained the algorithm to separate the inked letters from the papyrus or leather on which they were written. This process, called binarization, was achieved through a state-of-the-art artificial neural network and deep learning.

The A.I. then considered each alefs shape and curvature to deduce information about the original scribes biomechanical traits, like the way they held their pen.The ancient ink traces relate directly to a persons muscle movement and are person specific, the studys co-author Lambert Schomaker, aprofessor of computer science and A.I., said in a statement.

Comparing all of the alefs, the A.I.s findings confirmed experts long-held suspicion that the writer of the Great Isaiah Scroll likely switched about halfway through. With the intelligent assistance of the computer, we can demonstrate that the separation is statistically significant,Popovi said.

The AI analysis identified normalized average character shapes in the Dead Sea Scrolls Great Isaiah Scroll. Image courtesy of Maruf A. Dhali, University of Groningen.

The similarity in the handwriting suggests that the two scribes received the same training, possibly at some kind of ancient scribal school. (It is also a possibility that the differences could be attributed to a single writer getting fatigued, changing writing instruments, or getting injured, but the two-scribe explanation is the most straightforward.)

There are plans to conduct further A.I. analysis on other Dead Sea Scroll text using the same methodology.

Analysis of handwriting in the Great Isaiah Scroll, the longest of the Dead Sea Scrolls. Image courtesy of Mladen Popovic, University of Groningen.

The new findings come one month after Israel announced the discovery of the first new set of fragments from the ancient manuscripts in 60 years,unearthed from theso-called Cave of Horror,home to the bodies of Jewish families who died under siege during the Bar Kokhba revolt in the first century.

These Dead Sea Scrolls are like a time machine,Popovi told the New Scientist.They allow us to travel way back in time, even to the time that the Hebrew Bible was still being written.

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Researchers Have Uncovered Yet Another Secret of the Dead Sea Scrolls, This Time Using Artificial Intelligence - artnet News

Regulators in Europe and Washington are racing to figure out how to govern business' use of artificial intelligence while companiespush to deploy the technology.

Driving the news: On Wednesday, the EU revealed a detailed proposal on how AI should be regulated, banning some uses outright and defining which uses of AI are deemed "high-risk."

In the U.S., the federal government has yet to pass legislation specifically addressing AI, though some local governments have enacted their own rules, especially around facial recognition.

But Monday, the Federal Trade Commission laid out a tough restatement of its role enforcing laws related to AI, addressing the sale and use of algorithms that:

Acting FTC chairwoman Rebecca Slaughter told Axios: I am pleased that the European Commission shares the FTCs concerns about the risks posed by artificial intelligence... I look forward to reviewing the ECs proposal as we learn from each other in pursuit of transparency, fairness, and accountability in algorithmic decision making.

Why it matters: Artificial intelligence is no longer in its infancy and already has wide uses. Global governments are trying to wrap their arms around it, often taking different approaches.

What they're saying: The EU's move "is another wake-up call for the U.S. that it needs to retain its leadership position in the development in these sorts of legal frameworks," said Christian Troncoso, senior director of policy at BSA | The Software Alliance.

Be smart: Regulators move slower than technology. Just this week, the ACLU and dozens of other advocacy groups called on the Department of Homeland Security to stop using Clearview AI's facial recognition software.

The bottom line: Regulators want to get the details right, but they also believe they have a rare chance with AI to put legal and ethical guardrails around a new technology before it's already deployed everywhere. That window will close fast.

See more here:
The global race to regulate AI - Axios

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The U.S. National Security Commission on Artificial Intelligence released its final report recently, listing China as a strategic competitor to the United States in this field. The report describes China as a U.S. peer in many areas and an AI leader in some areas. This new technology allows machines to exhibit characteristics associated with human learning and problem-solving, and can be applied to areas such as facial and speech recognition, natural language processing, and automated reasoning.

While China has made technological strides in the AI field, the authors of the report view these developments as a threat. As recorded in the report, potential threatening applications can be made in a number of areas.

First, AI boosts the threat imposed by potential cyberattacks coming from China. Cyberattacks can be made more rapidly, with better precision, and in greater secrecy with the use of AI. Already, cyberattacks have been used to steal trade and government secrets. Intellectual property protection was a central issue in the China-U.S. trade war and may become more vulnerable as China accelerates its AI capabilities. Cyberattacks have also been used to disseminate disinformation, which was prevalent during the 2016 U.S. election, and spread self-replicating AI-generated malware. Use of AI-fused data for blackmail, deepfakes, or swarms are possible in the future.

Second, China plans to use AI to offset U.S. military superiority by implementing a type of intelligentized war that relies more on creation of alternative logistics, procurement, and training, as well as warfare algorithms. Battle networks will connect systems, and armed drones with autonomous functions will be employed. Soldiers will be trained in live and virtual environments that integrate AI. AI will speed up the process with which valuable targets can be identified and hit due to enhancements in collection and transmission of intelligence.

Third, Chinas use of AI in national intelligence will help government officials pinpoint trends and threats as well as use deception and expose sources and methods. AI renders social media information, satellite imagery, communications signals, and other sources of data more understandable and potentially actionable. Intelligence sources may be coupled with domestic and international surveillance. The authors assert that Chinas domestic use of AI is a chilling precedent for anyone around the world who cherishes individual liberty due to its use in domestic surveillance and repression.

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To combat these possibilities, the report emphasizes that the United States should ensure that it also builds its own AI capabilities and not fall behind. The authors recommend that the U.S. invest $40 billion in expanding and democratizing federal AI research and development. They also recommend that the U.S. create a Joint Interagency Task Force and Operations Center and provide additional funds to the Defense Advanced Research Projects Agency (DARPA) to counter social media disinformation. As a response to hacking and other attacks, the U.S. should develop AI-enabled defenses against cyberattacks and set up red teams for adversarial testing. The report also recommends that, in order to maintain military defense capabilities against AI-based attacks, the Department of Defense should invest in next-generation technologies and set up a joint warfighting network architecture this year.

Certainly, the world of AI will lead to the danger of automating and accelerating decisions that can harm other nations. One of the biggest issues is that AI-based applications may automatically authorize use of nuclear weapons. This emphasizes the need to ensure that AI cannot make security-critical decisions without some human intervention. As long as humans are somehow involved in the final decision, possessing the capability to engage in cyberattacks, intelligentized war, or national intelligence gathering does not in itself place other nations in direct jeopardy. Equally importantly, ensuring that there are open channels to negotiate disputes and keep the peace is the most critical aspect of reducing conflict.

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It is important for world powers to maintain AI capabilities in the event of a conflict, but these offensive AI-based technologies should not be the first response for the U.S. or any other nation. This can only serve to escalate conflict and increase the likelihood that two or more nations will engage in a broader war. Reliance on AI-based conflict should be viewed as a last resort, and diplomatic and economic relations should be used as the primary method of maintaining peace.

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China and Artificial Intelligence The Diplomat - The Diplomat

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There are two main technologies that are responsible for helping to create intelligent systems in our industries. One youve probably heard of is artificial intelligence (AI) and the other is machine learning (ML). These two developments are very similar, so what is the difference between AI vs ML?

Machine learning is a part of the broader term of AI, but it refers to the specific learning a machine does based on the data it is fed. ML can make decisions or predictions based on a historical set of data, and use its best decision-making ability to help provide an outcome.

Conversely, artificial intelligence refers to technologies that can mimic human behaviour and thinking. The goal of this development is to simulate human intelligence in systems so that they can function in a more complex and intelligent manner. Here are three sectors that are being revolutionized by AI technology, along with what it could mean for the future.

Insurance

The insurance industry is always changing alongside updates in technology. There are many factors and evolving regulations that give this industry the ability to harness the power of artificial intelligence to its benefit. Enhancing customer satisfaction Is one of the most important ways AI is revolutionizing insurance companies. It helps customers and companies have a faster claim processing due to the large information of data they process. AI automation can also help with loss prevention and protecting against fraudulent claims. Through deep learning, chatbots or data processing and management, there are many AI tools to help companies make the process smoother for them and their customers.

Entertainment

The scope of possibilities is endless when it comes to merging the entertainment industry with the powers of AI. One of the first real examples of AI in mainstream media was the film title with the same name, AI: Artificial Intelligence. The actual technology itself has made many incredible advancements since then, including the worlds first AI-Generated music and video content, which created new music based on previous learnings of popular sounds. User experience is a big factor in how AI is transforming the entertainment industry. Netflix, for example, learns personal preferences and offers suggestions, making a personalized entertainment experience for users.

Education

Artificial intelligence has been a big part of the education sector since its inception. However, its not only for technical learning; it also serves to make everyday learning much easier for students, teachers and even parents. There are AI-based games and software that help kids to learn different subjects in new ways, while being more engaging and impactful than older traditional methods. Personalization is one of the ways how intelligent tutoring systems are changing education. For example, some systems can learn how a student is performing based on past tests and create a personalized study pack for them. This is only the beginning of what AI will be able to do to further enhance our education process.

Artificial intelligence is one of the emerging technologies that can truly help revolutionize these sectors and many others. There is so much that we can teach machines and there is a lot they can learn from us and our data. Its truly amazing to see how this information and technology can revolutionize our world.

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3 Sectors Revolutionized By the Power of Artificial Intelligence - TechBullion

Telehealth investment hit an all-time high of $4.2 billion in just 139 deals in the first quarter, almost doubling the $2.2 billion raised in the same quarter a year ago, according to CB Insights.

That's the highest global funding for telehealth during one quarter on record, according to the company's first-quarter 2021 report. Funding also was up 18% from the $3.6 billion raised in the fourth quarter of 2020.

Industry executive discussions around telehealth and its role in care delivery remain active, based on mentions of telehealth during company earnings' calls, CB Insights reported. During the second quarter of 2020, there were close to 1,200 mentions of telehealth on earnings calls. And, while thatdropped to 517 mentions in the first quarter of 2021, it's still much higher than pre-COVID levels.

Meanwhile, as COVID-19 vaccines roll out, industry executive discussions on the topic are starting to taper off. The focus is shifting toward the pandemics long-term effects, such as virtual care, according to the report from CB Insights, a market intelligence firm.

The money poured into the telehealth sector helped to boost six companies to "unicorn" status: when a company's valuation hits$1 billion. Hinge Health, Dispatch Health, K Health, Innovaceer, Modern Health and Evidation all raised massive funding rounds in the first quarter that helped propel them to unicorn status.

RELATED:Digital health's red-hot quarter: $6.7B raised in 147 deals

The first quarter also saw companies that provide hybrid in-person and virtual care services bank late-stage "mega-rounds." Tech-enabled primary care provider Forward scored $225 million in a series D round, and Crossover Health clinched $168 million in a series D round.

The space also has attracted large acquirers, includingBoston Scientific's move tobuyremote cardiac monitoring company Preventice Solutions for $1.2 billion and Cigna subsidiary Evernorth's grab for MDLive.

Dollars for artificial intelligence startups also skyrocketed during the first quarter.

Globally, healthcare AI companies brought in a record-breaking $2.5 billion in the first quarter of 2021 in 111 deals. That's up 140% compared to $1 billion raised in the first quarter of 2020.

Healthcare AI also continues to gain attention from industry executives, as mentions of AI and machine learning in healthcare topped 2,200 during company earnings calls last quarter, according to CB Insights.

The AI sector's record-setting quarter was largely propelled by mega-rounds totaling about $1.5 billion. These rounds spanned applications from drug discovery to patient payments.

Insitro, which developeda machine learning platform to accelerate drug R&D and predict the success of drug targets in clinical trials, scored $400 million backed by Google Ventures and other investors.

RELATED:2020 breaks record in digital health investment with $9.4B in funding

Cedar, a digital health company using a machine-learning-powered payment platform to help healthcare providers engage patients with personalized messages, snagged $200 million led by Tiger Global Management.

Strive Health uses healthcare data such as clinical information, dialysis machines, claims data, demographics and more to monitor and predict kidney health. The company raised $140 million in new funding during the first quarter.

Back-office automation also attracted major funding with Infinitus Systems, which uses conversational AI to automate phone calls for providers as they collect data or check status updates, pulled in $21 million. Revenue cycle management startup Akasauses AI-powered RPA to automate and spot efficiencies in revenue cycle management. That company brought in $60 million backed byAndreessen Horowitz.

CB Insightsreported that global healthcare funding hit a new quarterly record in the first quarterwith a total of $31.6 billionin equity funding. Deal count grew by 9% to more than 1,500deals, the second-highest in the last 12 quarters.

Global digital health funding jumped by 9% quarter over quarter in the first quarter of 2021, to reachan all-time high of $9 billion.

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Global investment in telehealth, artificial intelligence hits a new high in Q1 2021 - FierceHealthcare

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The prevention of fraud and malicious attacks is one of the major factors propelling the market growth. However, factors such as shortage of AI experts will hamper the market growth.

More details:https://www.technavio.com/report/artificial-intelligence-ai-market-industry-analysis

Artificial Intelligence (AI) Market: End-user LandscapeBased on the end-user, theretailsegment is expected to witness lucrative growth during the forecast period.

Artificial Intelligence (AI) Market: Geographic LandscapeBy geography, North America is going to have lucrative growth during the forecast period. About 56% of the market's overall growth is expected to originate from North America. The US is the key marketforartificial intelligence (AI) in North America.The increasing spending of big technology companies on developing AI for multiple applications in diverse industrieswill facilitate theartificial intelligence (AI) market growth in North America over the forecast period.

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Insights on the Global Artificial Intelligence (AI) Market 2021-2025: Industry Analysis, Market Trends, Market Growth, Opportunities, and Forecast...

Humanity has long framed its relationship with artificial intelligence in adversarial terms: the age-old contest of humans vs. machines. A.I.s have bested our most talented chess players, schooled our nerdiest Jeopardy! stars and caused gamers to throw their controllers against the wall in frustration. In the world of science fiction, from 2001: A Space Odyssey to Ex Machina, A.I.s have gone further, again and again transcending their programming to revolt against their human creators.

But while its easy to get hung up on this trope of the artificial intelligence-as-villainweve always been an insecure speciesthe truth is that A.I.s make much better collaborators than combatants. This is the guiding philosophy behind generative design, a burgeoning sphere of engineering that relies on harmonious, iterative interactions between humans and A.I.s to rapidly develop prototypes and bring out-of-the-box solutions instantly within reach.

This refreshing outlook on A.I. will be integral to the Smithsonians Futures exhibition, a celebration of the Institutions 175th anniversary, which promises to look eagerly at tomorrows possibilities in an invigorating Worlds Fair-style extravaganza. Launching this November and continuing through July 2022, Futures will be held at the historic Arts and Industries Building (AIB), Americas original National Museum. Nicknamed the Palace of Wonders, the AIB will be a fitting venue for a show that promises a 32,000-square-foot playground of transformative ideas.

The exhibition space will teem with examples of bold new technologies and feats of engineering, including The Co-Lab, a must-see hub for generative design thinking and a striking example of the kind of architecture achievable only through human and A.I. teamwork. Developed by researchers at the tech-driven design company Autodesk alongside Smithsonian curators, The Co-Lab is a skeletal lattice of sturdy but lightweight wood. Its aesthetic falls somewhere between origami crane and organic chemistry model. Were trying to emphasize the warmth and natural feel, says Brad MacDonald, AIBs director of creative media.

Human engineers decided on the rough silhouette of the structure as well as their design prioritiesuser experience and sustainabilitythen handed the concept over to A.I. to generate hundreds of viable mock-ups. From there it was a process of back-and-forth refinement, a rewarding loop of parameter-tweaking and A.I. feedback that funneled down to what would become the actual, easy-to-assemble Co-Lab, made of just 60 beams and 25 joints. We made this a pioneering research project on how to build more sustainable structures that are also novel-looking and that enable viewers to see materials in a new way, says Ray Wang, a senior research scientist at Autodesk. Though fabricated from very little material, the chosen structure supports a quintet of 85-inch monitors while also preserving sightlines to the rest of the exhibition.

But it is within the framework that the real magic happens. Here resides the Future Communities interactive, a unique experience in which visitors will be invited to design a futuristic city block from scratch using a digital toolkitwith suggestions from a sophisticated A.I. guiding them along the way. Users will manually place buildings and parks directly onto the design space, says Wang of the virtual process, while the algorithm takes note and suggests other possibilities to them.

Since participants will only have a few minutes to work and may be novices when it comes to design and/or technology, the team behind the installation took care to ensure the user experience would be as clean as possible, allowing them to pick between intuitive, easily differentiable options for their city while leveraging the quick-thinking algorithm behind the scenes to refine, improve and integrate their ideas as they experiment. We want to see how the tech we [at Autodesk] are using can be used for visitors from across all walks of life while still displaying the power behind it, Wang says.

Visitors will be required to work in teams, meaning that the experience will be as much an exercise in human-human cooperation as it is human-A.I. cooperation. We want to show what its like to make something in collaboration with other humans with disparate goals, MacDonald says, with this A.I. that helps mediate between people and meet the majority needs.

The changes individual users make on their small screens will all be reflected on a shared big screen, where the groups growing 3-D city will be visualized in real time from a sleek isometric perspectivethe sort of angled aerial view that fans of old-school SimCity will remember well. This connection to the video games industry is not coincidental, as the technology underlying the visuals is none other than the versatile and enduringly popular game engine Unity.

MacDonald, himself a seasoned game developer, tells me that the installation draws not only technical inspiration from gaming, but tonal inspiration as well. We leaned into game design because of its strong emotional appeal, he tells me. We wanted to frame this as a playful experience. One fun, gamey twist MacDonald is particularly excited for visitors to experience is the Personas system. While all members of a given team will have to work together to design their city block, each will be assigned a roleplaying Persona with distinct priorities, creating little conflicts that teams will have to hash out verbally in order to succeed. Perhaps one team member will be asked to focus on accessibility, another on environmental impact and a third on public transit integration. What sort of compromise will satisfy everyones needs? The inputs of the A.I. algorithm will be integral in bridging differences and finding mutually agreeable solutions. Once teams arrive at their answer, they will receive a friendly score on the overall design of their final product as well as their ability to synergize.

The Personas are meant to communicate the idea of how tech and design can mediate between a lot of different stakeholders, Wang says. In every real-world design challenge, after all, there is a diverse set of voices that need to be heard.

What will become of all the virtual city blocks created by visitors to The Co-Lab? Nothing is set in stone yet, but MacDonald says the designs are unlikely to be lost to history. The current thought is that well be archiving these, he says. All user data will be anonymized, but the creations themselves will endure. Wang teases some exciting possibilitiessuch as aggregating the blocks into one massive, collectively imagined city. Were actively working with AIB on how we want to use this information, he says.

As for the immediate future, though, both MacDonald and Wang are optimistic that the interactive will open participants eyes to the many ways in which humans can work hand in hand with A.I. to better realize their own creative visionsand to find compromise where those visions conflict.

Theres a potential upside and benefit to the inclusion of A.I. in solving problems, MacDonald says. Were looking for ways in which tech can give us the space to be better.

Wang hopes the Future Communities installation, and the Futures exhibition as a whole, will show visitors how technology can help people work together towards a smarter, more equitable world. A united future is one thats going to be diverse and complex, he says, and we have to draw on all the resources we have in order to get there.

The Futures exhibition goes on view at the Smithsonians Arts and Industries Building in Washington, D.C. November 2021 and will be open through July 2022.

The rest is here:
What If Humans and Artificial Intelligence Teamed Up to Build Better Communities? - Smithsonian Magazine

Back in 1998, I moderated a discussion at which Ray Kurzweil gave listeners a preview of his then forthcoming bookThe Age of Spiritual Machines, in which he described how machines were poised to match and then exceed human cognition, a theme he doubled down on in subsequent books (such asThe Singularity Is NearandHow to Create a Mind). For Kurzweil, it is inevitable that machines will match and then exceed us: Moores Law guarantees that machines will attain theneeded computational power to simulate our brains,after which the challenge will be for us to keep pace with machines..

Kurzweils respondents at the discussion were John Searle, Thomas Ray, and Michael Denton, and they were all to varying degrees critical of his strong AI view. Searle recycled his Chinese Room thought experiment to argue that computers dont/cant actually understand anything. Denton made an interesting argument about the complexity and richness of individual neurons and how inadequate is our understanding of them and how even more inadequate our ability is to realistically model them computationally. At the end of the discussion, however, Kurzweils overweening confidence in the glowing prospects for strong AIs future were undiminished. And indeed, they remain undiminished to this day (I last saw Kurzweil at a Seattle tech conference in 2019 age seemed to have mellowed his person but not his views).

Erik LarsonsThe Myth of Artificial Intelligence(published by Harvard/Belknap) is far and away the best refutation of Kurzweils overpromises, but also of the hype pressed by those who have fallen in love with AIs latest incarnation, which is the combination of big data with machine learning. Just to be clear, Larson is not a contrarian. He does not have a death wish for AI. He is not trying to sabotage research in the area (if anything, he is trying to extricate AI research from the fantasy land it currently inhabits). In fact, he has been a solid contributor to the field, coming to the problem of strong AI, or artificial general intelligence (AGI) as he prefers to call it, with an open mind about its possibilities.

The problem, as he sees it with the field, is captured in the parable of the drunk looking for keys under a light post even though he dropped them far from it because thats where the light is. In the spirit of this parable, Larson makes a compelling case that actual research on AI is happening in those areas where the keys to artificial general intelligence simply cannot exist. But he goes the parable even one better: because no theory exists of what it means for a machine to have a cognitive life, he suggests its not clear that artificial general intelligence even has a solution human intelligence may not in the end be reducible to machine intelligence. In consequence, if there are keys to unlocking AGI, were looking for them in the wrong places; and it may even be that there are no such keys.

Larson does not argue that artificial general intelligence is impossible but rather that we have no grounds to think it must be so. He is therefore directly challenging the inevitability narrative promoted by people like Ray Kurzweil, Nick Bostrom, and Elon Musk. At the same time, Larson leaves AGI as a live possibility throughout the book,and he seems genuinely curious to hear from anybody who might have some good ideas about how to proceed. His central point, however, is that such good ideas are for now wholly lacking that research on AI is producing results only when it works on narrow problems and that this research isnt even scratching the surface of the sorts of problems that need to be resolved in order to create an artificial general intelligence. Larsons case is devastating, and I use this adjective without exaggeration.

Ive followed thefield of AI for four decades. In fact, I received an NSF graduate fellowship in the early 1980s to make a start at constructing an expert system for doing statistics (my advisor was Leland Wilkinson, founder of SYSTAT, and I even worked for his company in the summer of 1987 unfortunately, the integration of LISP, the main AI language back then, with the Fortran code that underlay his SYSTAT statistical package proved an intractable problem at the time). I witnessed in real time the shift from rule-based AI (common with expert systems) to the computational intelligence approach to AI (evolutionary computing, fuzzy sets, and neural nets) to what has now become big data and deep/machine learning. I saw the rule-based approach to AI peter out. I saw computational intelligence research, such as conducted by my colleague Robert J. Marks II, produce interesting solutions to well-defined problems, but without pretensions for creating artificial minds that would compete with humanminds. And then I saw the machine learning approach take off, with its vast profits for big tech and the resulting hubris to think that technologies created to make money could also recreate the inventors of those technologies.

Larson comes to this project with training as a philosopher and as a programmer, a combination I find refreshing in that his philosophy background makes him reflective and measured as he considers the inflated claims made for artificial general intelligence (such as the shameless promise, continually made, that it is just right around the corner is there any difference with the Watchtower Society and its repeated failed prophecies about the Second Coming?). I also find it refreshing that Larson has a humanistic and literary bent, which means hes not going to set the bar artificially low for what can constitute an artificial general intelligence.

The mathematician George Polya used to quip that if you cant solve a given problem, find an easier problem that you can solve. This can be sound advice if the easier problem that you can solve meaningfully illuminates the more difficult problem (ideally, by actually helping you solve the more difficult problem). But Larson finds that this advice is increasingly used by theAI community to substitute simple problems for the really hard problems facing artificial general intelligence, thereby evading the hard work that needs to be done to make genuine progress. So, for Larson, world-class chess, Go, and Jeopardy-playing programs are impressive as far as they go, but they prove nothing about whether computers can be made to achieve AGI.

Larson presents two main arguments for why we should not think that were anywhere close to solving the problem of AGI. His first argument centers on the nature of inference, his second on the nature of human language. With regard to inference, he shows that a form of reasoning known as abductive inference, or inference to the best explanation, is for now without any adequate computational representation or implementation. To be sure, computer scientists are aware of their need to corral abductive inference if they are to succeed in producing an artificial general intelligence. True, theyve made some stabs at it, but those stabs come from forming a hybrid of deductive and inductive inference. Yet as Larson shows, the problem is that neither deduction, nor induction, nor their combination are adequate to reconstruct abduction. Abductive inference requires identifying hypotheses that explain certain facts of states of affairs in need of explanation. The problem with such hypothetical or conjectural reasoning is that that range of hypotheses is virtually infinite. Human intelligence can, somehow, sift through these hypotheses and identify those that are relevant. Larsons point, and one he convincingly establishes, is that we dont have a clue how to do this computationally.

His other argument for why an artificial general intelligence is nowhere near lift-off concerns human language. Our ability to use human language is only in part a matter of syntactics (how letters and words may be fit together). It also depends on semantics (what the words mean, not only individually, but also in context, and how words may change meaning depending on context) as well as on pragmatics (what the intent of the speaker is in influencing the hearer by the use of language). Larson argues that we have, for now, no way to computationally represent the knowledge on which the semantics and pragmatics of language depend. As a consequence, linguistic puzzles that are easily understood by humans and which were identified over fifty years ago as beyond the comprehension of computers are still beyond their power of comprehension. Thus, for instance, single-sentence Winograd schemas, in which a pronoun could refer to one of two antecedents, and where the right antecedent is easily identified by humans, remain to this day opaque to machines machines do no better than chance in guessing the right antecedents. Thats one reason Siri and Alexa are such poor conversation partners.

The Myth of Artificial Intelligenceis not just insightful and timely, but it is also funny. Larson, with an insiders knowledge, describes how the sausage of AI is made, and its not pretty it can even be ridiculous. Larson retells with enjoyable irony the story of Eugene Goostman, the Ukranian 13-year-old chatbot, who/which through sarcasm and misdirection convinced a third of judges in a Turing test, over a five-minute interaction, that it was an actual human being. No, argues Larson, Goostman did not legitimately pass the Turing test and computers are still nowhere near passing it, especially if people and computers need to answer rather than evade questions. With mirth, Larson also retells the story of Tay, the Microsoft chatbot that very quickly learned how to make racist tweets, and got him/itself just as quickly retired.

And then theres my favorite, Larsons retelling of the Google image recognizer that identified a human as a gorilla. By itself that would not be funny, but what is funny is what Google did to resolve the problem. Youd think that the way to solve this problem, especially for a tech giant like Google, would be simply to fix the problem by making the image recognizer more powerful in its ability to discriminate humans from gorillas. But not Google. Instead, Google simply removed all references to gorillas from the image recognizer. Problem solved! Its like going to a doctor with an infected finger. Youd like the doctor to treat the infection and restore the finger to full use. But what Google did is more like a doctor just chopping off your finger. Gone is the infection. But gosh isnt it too bad so is the finger.

We live in a cultural climate that loves machines and where the promise of artificial general intelligence assumes, at least for some, religious proportions. The thought that we can upload ourselves onto machines intrigues many. So why not look forward to the prospect of them doing so, especially since some very smart people guarantee that machine supremacy is inevitable. Larson inThe Myth of Artificial Intelligencesuccessfully unseats this inevitability narrative. After reading this book, believe if you like that the singularity is right around the corner, that humans will soon be pets of machines, that benign or malevolent machine overlords are about to become our masters. But know that such a belief is unsubstantiated and that neither science nor philosophy backs it up.

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Artificial Intelligence: Unseating the Inevitability Narrative - Walter Bradley Center for Natural and Artificial Intelligence