AI News, 10 Best Ai artificial intelligence images in 2019

Artificial Intelligence (AI) Health Outcomes Challenge

Launch Stage: CMS announced 25 Participants to advance to Stage 1 on October 30, 2019.

The 25 Participants, titles of proposed solutions and geographic locations are listed below: Participant: Accenture Federal Services Proposed Solution: Accenture Federal Services AI ChallengeGeographic Location: Arlington, Virginia Participant: Ann Arbor Algorithms Inc.

Proposed Solution: Actionable AI to Prevent Unplanned Admissions and Adverse EventsGeographic Location: Kenilworth, New Jersey Participant: North Carolina State University (NCSU) Proposed Solution: Multi-Layered Feature Selection and Dynamic Personalized ScoringGeographic Location: Raleigh, North Carolina Participant: Northrop Grumman Systems Corporation (NGSC) Proposed Solution: Reducing Patient Risk through Actionable Artificial Intelligence: AI Risk Avoidance System (ARAS)Geographic Location: Herndon, Virginia Participant: Northwestern Medicine Proposed Solution: A human-machine solution to enhance delivery of relationship-oriented careGeographic Location: Chicago, Illinois Participant: Observational Health Data Sciences and Informatics (OHDSI) Proposed Solution: OHDSI SubmissionGeographic Location: New York, New York Participant: University of Virginia Health System Proposed Solution: Actionable AIGeographic Location: Charlottesville, Virginia More information about Stage 1 submission requirements and evaluation criteria will be provided at a later date.

Artificial general intelligence

cognitive science, computational intelligence and decision making) tend to emphasise the need to consider additional traits such as imagination (taken as the ability to form mental images and concepts that were not programmed in)[13]

see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent), but not yet at human levels.

The most difficult problems for computers are informally known as 'AI-complete' or 'AI-hard', implying that solving them is equivalent to the general aptitude of human intelligence, or strong AI, beyond the capabilities of a purpose-specific algorithm.[19]

AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.[20]

In the 1990s and early 21st century, mainstream AI has achieved far greater commercial success and academic respectability by focusing on specific sub-problems where they can produce verifiable results and commercial applications, such as artificial neural networks, computer vision or data mining.[33]

Most mainstream AI researchers hope that strong AI can be developed by combining the programs that solve various sub-problems using an integrated agent architecture, cognitive architecture or subsumption architecture.

Hans Moravec wrote in 1988: 'I am confident that this bottom-up route to artificial intelligence will one day meet the traditional top-down route more than half way, ready to provide the real world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs.

for example, Stevan Harnad of Princeton concluded his 1990 paper on the Symbol Grounding Hypothesis by stating: 'The expectation has often been voiced that 'top-down' (symbolic) approaches to modeling cognition will somehow meet 'bottom-up' (sensory) approaches somewhere in between.

A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer).'[36]

Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10 years to over a century, but the consensus in the AGI research community seems to be that the timeline discussed by Ray Kurzweil in The Singularity is Near[41]

A 2017 survey of AGI categorized forty-five known 'active R&D projects' that explicitly or implicitly (through published research) research AGI, with the largest three being DeepMind, the Human Brain Project, and OpenAI based article.[8]

A low-level brain model is built by scanning and mapping a biological brain in detail and copying its state into a computer system or another computational device.

The computer runs a simulation model so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.[48]

An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS).[51]

In 1997 Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps).[52]

(For comparison, if a 'computation' was equivalent to one 'floating point operation' – a measure used to rate current supercomputers – then 1016 'computations' would be equivalent to 10 petaFLOPS, achieved in 2011).

He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

The artificial neuron model assumed by Kurzweil and used in many current artificial neural network implementations is simple compared with biological neurons.

The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate.

In addition the estimates do not account for glial cells, which are at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.[53]

The Blue Brain project used one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 108 synapses in 2006.[55]

A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: 'It is not impossible to build a human brain and we can do it in 10 years,' Henry Markram, director of the Blue Brain Project said in 2009 at the TED conference in Oxford.[56]

Hans Moravec addressed the above arguments ('brains are more complicated', 'neurons have to be modeled in more detail') in his 1997 paper 'When will computer hardware match the human brain?'.[58]

The actual complexity of modeling biological neurons has been explored in OpenWorm project that was aimed on complete simulation of a worm that has only 302 neurons in its neural network (among about 1000 cells in total).

fundamental criticism of the simulated brain approach derives from embodied cognition where human embodiment is taken as an essential aspect of human intelligence.

The first one is called 'the strong AI hypothesis' and the second is 'the weak AI hypothesis' because the first one makes the stronger statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test.

Since the launch of AI research in 1956, the growth of this field has slowed down over time and has stalled the aims of creating machines skilled with intelligent action at the human level.[72]

Furthermore, AI researchers have been able to create computers that can perform jobs that are complicated for people to do, but conversely they have struggled to develop a computer that is capable of carrying out tasks that are simple for humans to do (Moravec's paradox)[example needed].[72]

The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers from emulating the function of the human brain into a computer hardware.[75]

Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously can people then overlook solutions to problematic questions.[42]

possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance.[75]

There are other aspects of the human mind besides intelligence that are relevant to the concept of strong AI which play a major role in science fiction and the ethics of artificial intelligence:

It is also possible that some of these properties, such as sentience, naturally emerge from a fully intelligent machine, or that it becomes natural to ascribe these properties to machines once they begin to act in a way that is clearly intelligent.

Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century because it would require 'unforeseeable and fundamentally unpredictable breakthroughs' and a 'scientifically deep understanding of cognition'.[81]

Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level artificial intelligence is as wide as the gulf between current space flight and practical faster-than-light spaceflight.[82]

Four polls conducted in 2012 and 2013 suggested that the median guess among experts for when they'd be 50% confident AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081.

A growing population of intelligent robots could conceivably out-compete inferior humans in job markets, in business, in science, in politics (pursuing robot rights), and technologically, sociologically (by acting as one), and militarily.

For example, robots for homes, health care, hotels, and restaurants have automated many parts of our lives: virtual bots turn customer service into self-service, big data AI applications are used to replace portfolio managers, and social robots such as Pepper are used to replace human greeters for customer service purpose.[86]

UTD CS Ranks 5th in Top 40 Best U.S. Colleges for Artificial Intelligence

Recently, the website Great Value Colleges ranked the top 40 best colleges in the U.S. for Artificial Intelligence placing UT Dallas Computer Science Department as the fifth best college for undergraduate studies in Artificial Intelligence (AI).

Artificial Intelligence is the theory behind or development of computer systems capable of performing “intelligent” tasks such as decision-making, learning from examples, and translating languages that would otherwise require a human brain.

The CS Department at UT Dallas has done well in research-based rankings such as, where it ranks 7th in the area of natural language processing, 11th in artificial intelligence, 5th in the area of software engineering, and 6th in real-time systems (2009-2019 period).

Because their focus is affordability, Great Value Colleges used the National Center for Education Statistics’ College Navigator Database to determine the net cost, percentage of students receiving financial aid, student-to-faculty ratio, and first-time student retention rate (an indicator of student satisfaction) for each university.

They then developed a point system to account for each of those factors, as well as the school’s location, course offerings, research breadth, faculty achievements and additional pros related to a positive AI learning environment.

During the writing of this article, UT Dallas announced that it reached a qualifying benchmark to receive funding from the National Research University Fund, an exclusive source of research support available to the state’s “emerging research universities.” The University’s annual restricted research expenditures, high-achieving freshman class and high-quality faculty are a few of the requirements it met.

With The University of Texas at Dallas’ unique history of starting as a graduate institution first, the CS Department is built on a legacy of valuing innovative research and providing advanced training for software engineers and computer scientists.

Can Artificial Intelligence “Think”?

Sci-fi and science can’t seem to agree on the way we should think about artificial intelligence.

Sci-fi wants to portray artificial intelligence agents as thinking machines, while businesses today use artificial intelligence for more mundane tasks like filling out forms with robotic process automation or driving your car.

When interacting with these artificial intelligence interfaces at our current level of AI technology, our human inclination is to treat them like vending machines, rather than to treat them like a person.

Today’s AI is very narrow, and so straying across the invisible line between what these systems can and can’t do leads to generic responses like “I don’t understand that” or “I can’t do that yet”.

In the book “Thinking Fast and Slow”, Nobel laureate Daniel Kahneman talks about the two systems in our brains that do thinking: A fast automated thinking system (System 1), and a slow more deliberative thinking system (System 2).

Just like we have a left and right brain stuck in our one head, we also have these two types of thinking systems baked into our heads, talking to each other and forming the way we see the world.

Today's AI systems learn to think fast and automatically (like System 1), but artificial intelligence as a science doesn’t yet have a good handle on how to do the thinking slow approach we get from System 2.

In the future, thinking algorithms that teach themselves may themselves represent most of the value in an AI system, but for now, you still need data to make an AI system, and the data is the most valuable part of the project.

A colleague of mine has a funny story from her undergraduate math degree at a respected university, where the students would play a game called “stats chicken”, where they delay taking their statistics course until the fourth year, hoping every year that the requirement to take the course will be dropped from the program.

When we see a really relevant movie or product recommendation, we feel impressed by this amazing recommendation magic trick, but don’t get to see the way the magic trick is performed.

In fact, there are some accusations even in respected academic circles (slide 24, here) that the basic theory of artificial intelligence as a field of science is not yet rigerously defined.

Engineers don’t tend to ask questions like “is it thinking?”, and instead ask questions like “is it broken?” and “what is the test score?” Supervised learning is a very popular type of artificial intelligence that makes fast predictions in some narrow domain.

Explicit models like decision trees are a common approach to developing an interpretable AI system, where a set of rules is learned that defines your path from observation to prediction, like a choose your own adventure story where each piece of data follows a path from the beginning of the book to the conclusion.

Another type of artificial intelligence called reinforcement learning involves learning the transition from one decision to the next based on what’s going on in the environment and what happened in the past.

We know that without much better “environment” models of the world, these approaches are going to learn super slowly, to do even the most basic tasks.

In a game playing simulator an AI model can play against itself very quickly to get smart, but in human-related applications the slow pace of data collection gums up the speed of the project.

CES showcases more than 4,500 exhibiting companies, including manufacturers, developers and suppliers of consumer technology hardware, content, technology delivery systems and more.

Photographer: David Paul Morris/Bloomberg Regardless of the underlying technical machinery, when you interact with a trained artificial intelligence model in the vast majority of real-life applications today, the model is pre-trained and is not learning on the fly.

It is useful to think about more general mathematical models like rainfall estimation and sovereign credit risk modeling to think about how mathematical models are carefully designed by humans, encoding huge amounts of careful and deliberative human thinking.

I asked Kurt a lot of technology questions, leading up to the question “Does the system think like people do?” AstraLaunch is a pretty advanced product involving both supervised and unsupervised learning for matching technologies with company needs on a very technical basis.