AI News, Five Top Artificial intelligence (AI) trends for 2019
When the environment doesn't For example, legislation that denotes how businesses interact with people isn't baked into financial, health, fitness, or social-media, software.
It's something that you need to add to the system and most companies do not have ethics baked into their decision making they push the boundaries of legislation.
2 Fast-Growing AI Stocks to Buy in 2019
From self-driving cars to interactions with virtual assistants and simple searches on the internet, you can be assured that there's an AI algorithm working in the background to come up with the best results.
It is well-known that NVIDIA is driving innovation in self-driving cars and Microsoft in the cloud, but a few smaller companies are using AI as a springboard for their growth and investors could benefit from taking a look.
XLNX data by YCharts NVIDIA has been making waves in AI thanks to its graphics cards, which are considered best suited for training AI models as they can process huge sets of data thanks to the presence of hundreds of cores.
But Xilinx's field-programmable gate arrays (FPGAs) -- chips that can be reprogrammed for specific tasks after manufacturing -- are considered ideal for the real-world deployment of AI models because of their flexibility and low power consumption.
Developers can reprogram Xilinx's FPGAs to deploy or fine-tune AI applications, making these chips ideal for the inferencing phase that requires fewer data center resources as compared to the training phase.
That's one of the reasons the FPGA market is expected to clock robust growth in the coming years, hitting revenue of $12.1 billion by 2024, as compared to $6.9 billion in 2016, according to one estimate.
Xilinx's chips played an important role in Alibaba's 'Singles Day' shopping event in November last year, providing more than 45 billion personalized shopping recommendations to consumers based on their histories.
This is a lucrative market to be in, as demand for intelligent networks is expected to increase at a compound annual growth rate of 28% through 2024, according to Zion Market Research.
The company's revenue increased nearly 21% year over year during the last reported quarter, a trend that should continue in the long run thanks to emerging technologies such as artificial intelligence.
Backreaction: The Real Problems with Artificial Intelligence
by a self-driving Tesla in Las Vegas ahead of CES Accident occurred on Paradise Rd in Las Vegas as engineers transported bots One of the Promobots stepped out of line and into the roadway, where it was hit Tesla Model S was operating autonomously, though a passenger was on board https://www.dailymail.co.uk/sciencetech/article-6566655/Oops-Autonomous-robot-struck-killed-self-driving-Tesla-Las-Vegas-ahead-CES.html
It was amazing to watch but the computer didn't understand the answers and sometimes got them wrong, and didn't know it had won.AI with never develope thoughts, feelings, or ambitions - I think that is what the people you mention are worried about.
I find it important that the author raises social inequality as an immediate problem.Apart from that I would like to share an example about how AI contributes in the case of protein folding:https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins
"there’s a risk that humans will use it to push an agenda by passing off their own opinions as that of the AI."Worse, every prejudice available is already burned into the AI by way of the training material.
Second, what good is a human level intelligence for if there is cheap human labor ready for hire.What would make real difference, if we had an artificial intelligence that is far more intelligent than a human.
An individual human brain may be limited in that, however humanity as a collective has already gone far beyond the progress of what an individual brain could achieve.
Using technology and communication as tools, humanity has discovered and understood complex systems the way no individual brain could understand.Communication enables research groups to divide up the work, so that different people work on different sub-problems, without having the need for a single person to have to understand every aspect of all problems.Technology enabled us to comprehend very complicated systems, that are far beyond human capability, like Lattice QCD, the analysis of the CMB, weather patterns, etc., all enabled by computers, that are used as the extension of the human brain.What we are doing now is a very efficient use of a combination of cheap human intelligence and state of the art technology.
One recent crash was due to sun glare on a traffic sign.How would a self driving car solve the "runaway trolley problem"?If it keeps going straight it will kill 5 people.
Bahle "The problem is simple: if a machine was not told to do anything but was simply given a few algorithms and lots of data as a basis, who is to blame if something goes wrong?"This really is no problem at all.
In the end, someone has to keep an eye on the functioning of the car and the traffic.And if we switch to completely autonomous cars, a completely new job has been predicted: Remote car driver (cf, drone pilots).
(IIRC, while the computer chess champion was programmed to play chess, the computer go champion was programmed to learn, and then learned go.) For that matter, just normal calculations: we are hopelessly inadequate.
(OK, this isn't normally considered part of intelligence, but we must be careful to disqualify something from belonging to intelligence just because a computer can do it.) What happens when computers learn to design AI?
This is not just Moore's law, which makes all computing faster (though it will presumably break down at some point).It would take a human centuries to do the calculations of just a simple program which runs for a few minutes.
Once someone writes a program which designs AI, then this can design a better AI, really fast, just like a computer can calculate in a few minutes what would take a human a lifetime.
That is relevant insofar that some of the possible problems to come with AI will get only crucial when it's real AI.There are some prominent people who state that today's AI is not "intelligent"
You may want to read for example here: https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/Looking at the brain, one can see that today's AI approaches rely on a overly simplified model of biological brains as seen in the 60ies of the past century.
Much of what we know today about real brains and what is essential for their effective functioning is missing in AI.I want to mention two examples: Real brains are not just networks of neurons who exchange electrical impulses via synapses.
Real brains are profundly chemical devices and the dozens of neurochemicals add a vast amount of additional degrees of freedom to the brain's state space.
From modern affective neuroscience we know that even very tiny, locally applied changes to the neurochemistry can have a dramatic effect on the brain's functioning.A second example is the topology.
Closed loops are ubiquitous in the brain and the phylogenetic developmental layers of the brain add even more nested, recursive processing: The brain builds representations of representations of representations ...
An illustrative example for top-level self-referential operation is vision: When you cannot move (not even your eyes) you loose your capability of vision within a short time and you drift into weird haluzinations.
The reason for this is, that the brain uses a sensori-motor feedback configuration: It brings together how visual stimuli change due to motor actions it initiated.
Without this sensori-motor closed-loop, the brain cannot construct a stable visual representation.I am pretty sure that the possibilities of AI are and remain very limited as long as they will not be based on fundamentally different models.
The assumption seems to be that reason is an attribute of the soul, that having a soul means you freely will your own goals, that therefore an artificial soul in computers will have ungodly traits, because Men Are Not Gods.
How many people care about creating hardware designed to operate for a hundred year?Artificial Intelligence, if it ever happen, is so unknown that only the most general (and therefore useless) statements can be made about it.
planning tasks than any human beings.What I am worried about is the situation when human beings subject to greed and hatred get to define greedy and hateful goals for AIs.
It will just do that with no mercy and no regret.I'd rather have a benevolent AI controlling greedy and hateful human beings than having greedy, hateful human beings controlling malicious AIs.But the latter is just what is going to happen when the development of AIs is left to evolve by market forces, given the state this world is in.
If you've read science fiction from the 50s, and popular science literature from around that time, it was assumed that computers would be large, expensive, rare, be owned only by governments, universities, large corporations, etc., and that access to them would be limited to a privileged few.
Re who gets to ask questions:I think one of the world’s leading cancer institutes - Memorial Sloan Kettering, in the Big Apple - uses Watson to assist oncologists with diagnosis and treatment options (also research);
It turns out the future we actually live in is one of networks of microcomputers, something that many really had a hard time to see coming ("There is no reason for any individual to have a computer in his home", one CEO famously said).
or wisdom.As someone pointed out an AI would have to be designed for each specific problem.It's not very romantic, but the machine is just searching databases and making millions of mindless comparisons per second.
A worthy project would be an AI trained to look for correlations in different sets of data (do power lines cause cancer, is social program A really working, etc., etc.This is something people are bad at, even physicists!An international collaboration like CERN (!) could be be formed so everyone shares the expense and the access and results.
For example, it takes decades to train a human radiologist, who then has a potential career of a few more decades, but AI radiologists are already competitive or superior in several domains, and once you have trained one, you have essentially trained them all.
Once the AI equivalent is trained, a few milliseconds is sufficient to transfer it to any other computer with sufficient processing power, and such computers can be manufactured for a few thousand dollars.In any case, the real processing power today doesn't reside in some box under somebody's desk, but in the internet, and a thousand or a million cpu can go belly up without changing this.All that said, I loved Arun's story about bot on bot violence.
AI that looks superficially convincing at first sight, but is in reality just a scripted doll that looks dumb as soon as you ask it unexpected questions.
Awesome for diagnosing stroke, or eye pathologies, etc, perhaps awesome for other commercial purposes, but still just tools that do as they are directed.It is not precisely true that we cannot figure out why trained neural nets are doing what they are doing, or how they are doing it.
This can reveal whether inputs matter, how much, and often analysis reveals the unexpected relationships found by the net.I am also not convinced by the article that a neural net could define another neural net.
Programmers of AlphaZero had to decide what the inputs and outputs would be, how to segregate the inputs (if they used a divide-and-conquer approach), how the layers would by sized and interact, the activation function(s), and finally what the output would be and how to interpret it.
All that requires a human understanding of how to formulate the problem to be solved.I doubt the humans disappear in the next 50 years, I don't think anybody knows how to formulate for a neural net the problem of understanding how to formulate problems for a neural net.
Meaning, I don't know how to start on a general intelligence neural net, that could automatically search for and read online literature about playing game X and then produce a net that learns to play game X.
https://www.nature.com/articles/d41586-018-05084-2Particle physicists turn to AI to cope with CERN’s collision delugeA comparison and evaluation of results that are not subject to the expectations of human nature might turn up something of value.
This principle is put forth to explain how a car control AI will surpass the abilities of a human driver when all the experiences of every move that a computer controlled car makes from all over the world will allow the program to handle every possible condition that could ever happen.
All experimental results could be validated and then encoded in a global world wide all inclusive statistical database that holds the sum of all discovered experimental experience.This process would avoid a problem that I have seen in science where the same results are discovered over and over again by experimenters that have no idea about the details of what has been turned up in the past.For example, I have seen the results produced by a chemist that has found a way to produce metallic crystals of hydrogen using Rydberg blockade that produce muons when irradiated by UV light.
This experience might be interesting to particle physics if they had access to the data and believed it since the experimental results were peer reviewed, replicated, validated, and universally accepted.
You’ll notice that the people (by which I mean corporations, ahem) most interested in advancing AI aren’t concerned about keeping the techniques to generate it.
Lighthill's argument was that AI progress isreally advances in sensors and actuators going back to WW IIand runs into the combinatorial explosion problem.
Some facts that do not get mentioned by the AI PR hype machinere that the very best Chess players can now beat the best chesscomputer program's (expert chess player plus ability to use computersto evaluate positions offline).
To the best of my knowledge, this is the unvarnished state of the art in machine learning (note I avoid the use of the word "artificial intelligence"):Machine learning is good at well defined tasks for which it is possible to prepare a training sequeuence from a known database, such as:1.
Wouldn't it be nice to just load all you laundry at once: bath rugs, red silk blouse, cat vomit rag, husband's navy blue work shirts, child's expensive jeans that can shrink, into one giant load and have an intelligent washer/dryer deal with it?
likely came with early inter-city commerce when a tally was needed (and usually just some notches on a stick.) People, though, have probably created poetry for millennia -- math is just a recent novelty.
Either the driver, human or otherwise, can control the car well enough to avoid hitting anyone or it will have too little time to deal with moral judgements.
We aren't going to suit up autonomous car passengers like fighter jet pilots on the off chance of saving one orphan with a good sob story at the expense of killing two boring former Dancing With The Stars contestants.Granted, the idea has a lot of attraction to wealthy technical types who have realized that they are going to die.
Nowadays they buy into the AI myth, that somehow they will build a machine so mentally capable and somehow compatible that it might offer their minds a chance at immortality.
It was a tool that used AI to solve a particular set of problems.It's fun discussing this kind of stuff, but AI is pretty far from an existential threat to humanity.
And computers will be large machines requiring banks of memory, and only governments and large corporations will be able to afford them, or have the expertise necessary to run them...
At that time the accepted metric for machine intelligence was the "Turing Test", that a human could engage in a conversation via a Teletype machine and was unable to discern whether he was talking to a machine or a person.
How do you prevent that limited access to AI increases inequality, both within nations and between nations?dlb wrote: if it ever happen, is so unknown that only the most general (and therefore useless) statements can be made about itWe already know enough to make some useful statements.
So would it be ethical to kill someone in the waiting room and harvest their organs?Someone has pointed out that delaying AI in self-driving cars and so on will kill far, far, far, far more people than actual trolley-problem situations ever would.
(Famously, Univac once correctly predicted that Eisenhower would win, which no-one believed, so the prediction was held back to avoid embarrassing those working with Univac.) As some pundit remarked, you know that you are reading old science fiction when, as future time goes on, computers get bigger and bigger instead of smaller and smaller.Asimov of course also wrote much about robots, which were of human intelligence with a "positronic brain"
positrons were new at the time, so he adopted the term to sound cool.) When I was reading Max's Life 3.0, I noticed that many of the moral questions had already been discussed in Asimov's fiction more than half a century ago.It turns out the future we actually live in is one of networks of microcomputers, something that many really had a hard time to see coming ("There is no reason for any individual to have a computer in his home", one CEO famously said).That was Ken Olsen, CEO of Digital Equipment Corporation (see the link above to see how this relates to Multivac).
Markus wrote: To me, this fear of AI is just ridiculousI suspect that people find it easier to talk about the potential problems with AI than tackle the long list of real problems we're facing right now and the near future.
Again, I am perfectly aware that people find this terribly distasteful, but young people and/or people with children who have many "work hours"
more than the other way around.Abstract:With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour.
You are right in that any such decision would have to factor in political decisions and regulations that are enforced for this reason, but this does nothing to negate my point that the question of ethics will be settled by market forces - within the legal bounds of course.
In this case it is possible that the AI system makes the (logical but most would agree least desirable) conclusion that the optimum outcome is to run over the pregnant mother."Then it would be a very stupid idea if it thinks that killing a few people in car crashes is a solution to the problem of overpopulation.
If it were really smart enough to think about overpopulation, a better strategy might be to aim for the Vatican and keep killing the Pope until one is elected who doesn't pronounce that birth control is a sin.
:-|"the current problem with full AI - there is no way to address moral issues"Yes, but holding back on AI for self-driving cars causes far, far more deaths than the occasional AI-caused death (which is a choice between killing fewer and killing more, or whatever).
But what about the moral issue of the human driver ("the nut behind the wheel", as old discussions of automobile safety put it)?
(specific) AI is already taking decisions without human interference (like buying and selling at the stock market), or advising humans (drones in army, analyzing medical data), where it is conceivable that the decisions will be shifted to specific AI in the future.
But, what the heck, we will to some extent trust or distrust human rulers and experts because of double agendas, so it is likely that also with AI ethical / trust / control issues will likely remain present for ever.
You have to set up and configure your car just like you have to set up your computer or cell phone.In the simulator, you are given, say, fifty scenarios (all of which the car must be capable of distinguishing from its sensory data).
And of course, you can reconfigure whenever you like.The AI that does this can be trained to generalize from these inputs like any other AI, a long process, to be sure, but once it is trained the actual decisions is just a lot of dot-products that can be formulated in microseconds, in real-time, far faster than any human reaction time.
In court, if necessary, prosecution or defense can show how the actual accident situation is most similar to one of the training scenarios, or a combination of them, and what the owner decided, and let a human jury decide on how closely the owner's decision and the cars decision correlate.
So I don't think jury information has to be perfect for them to decide.The insurance companies have incentive to use any standardized method of assigning liability (because long court battles are very expensive) and they can also see how you set up and trained your car in order to set your insurance rates.
And finally, it means that car owners, to avoid liability, would have an incentive to set up their car with a minimum of selfish interest in an emergency, because that will result in the lowest insurance premiums.
after the AI taking action the response of the vehicle, it's technology (steering, brakes, tyres, acceleration, service history) and individual environmental conditions become the limiting factor.
The vast range of factors involved - many of which the AI will not be aware of or learnt to take into account - the AI decision speed and individual vehicle characteristics make the interactions uncertain or at least predictable only within certain limits.
Tibor Rado found a similar result with the Busy Beaver, that is never able to find enough space to determine the computational status of a TM with n states or that BB(n) grew exponentially.
Theoretically, we can think characterizing the kind of structures that exist in all the three aspects: the input data, the algorithmic transformations of data, and the outputs or predictions.
Implementations of AI can be differentiated (or classified) according to the characteristic combinations of these three features.If a new problem involves a combination that is similar to the one that is studied already, then you have a ``base-case''
But it sure would have to be taken care of, during the actual engineering practice.Of course, following such an analysis scheme (of characterizing the structures in the three aspects) is easier said than done.
It never is going to be the case (except possibly in poorly written sci-fi novels / movies / media hype) that an implementation of an AI is some identity-less beast that ``somehow''
Let me give an analogy: Just because you have gears, shafts, cams and stepper-motors readily available in all shapes and sizes in the market, people therefore don't go out and connect them together in a random way, and *then* begin worrying whether the machine would stamp out its human operator or not.
It's high time that the media and the sci-fi authors stopped believing their own hype and stupid theories, and began taking the actual practice of engineering a bit more seriously.3.
If someone (like Google, like Putin, etc) devote a billion dollars to private research into AI, even non-conscious problem solving AI, they might solve problems in many forms of investing, manipulation of markets, etc, with the money to implement such things and take control of economies, micro-target certain markets, and basically legally (perhaps with a modicum of illegally if they don't mind that) win most of the money in the world, and use it as leverage to bludgeon whole governments and populations.You wouldn't be able to stop them, their nets would be proprietary and sequestered away, guarded by private armies.So what then?
For many people, it would no longer make sense to spend a lot of money to buy a car, pay for the insurance and maintenance, be directly liable for any harm the car caused, find parking spots and pay for them, wash and clean it, and store it somewhere when they're home.
As for evaluating relative harm vis-à-vis an elderly homeless man and a pregnant mother of three (Sabine's scenario), that's a great plot for a sci-fi story.
Sabine's sci-fi story would be an oddly dystopian world in which a technologically-advanced society puts great value on the lives of mothers and children but doesn't care enough to solve the very solvable problem of homelessness.
Artificial intelligence designed by human intelligence may prove useful under certain constrained circumstances, but will be as generally satisfying as artificial food.
When Barack Obama was president, market force gurus predicted that mandatory health insurance and raising the minimum wage would increase the price of fast-food hamburgers.
A poorly designed, unsafe e-waste processing plant in a destitute Chinese community is better than *no* e-waste processing plant, at least in the short term.
We tell ourselves that market forces will eventually allow them to move beyond unsafe e-waste processing plants or onerous sweat shops.
Predictably, the market force gurus argued that the rich can afford humane treatment of chickens, but the poor can't afford that luxury and the "efficient"
If someone points to problems with market forces, more often than not he's labeled a socialist or bleeding-heart liberal who doesn't know how the real world works.
Indeed, this is how companies today justify clearly unethical - but legal - behavior, by pointing out their fiduciary responsibilities to stakeholders and shareholders, as well as a general obligation to remain competitive in a market-driven world.
The exact way your computer performs this task depends both on your hardware and your software, hence the output can be used to identify a device."There are literally thousands of computer vulnerabilities at all levels of computing: in hardware, in software, in networks, and in operating systems.
We will be able to build driver assistance systems that can't be hacked.It should be said that many of the computer security vulnerabilities that exist today are there not because we didn't know about them, but because up until now, no one cared about security enough to want to invest in it, at least not the companies building commercial microprocessors.
We generally know it is a computer that we are interacting with, but we don't necessarily know if the computer is using machine learning to give us the answers it is giving.
How do you prevent that limited access to AI increases inequality, both within nations and between nations?"Having an AI to answer difficult questions can be a great advantage, but left to market forces alone it’s likely to make the rich richer and leave the poor behind even farther.
we should think about how to deal with it."I agree that use of advanced machine learning, if placed only in the hands of wealthy individuals or only wealthy countries, would increase inequality.
If you are good at that you will experience, that your visual perception starts to change and the longer you can take that the more you loose your ability to realy see exactly what is there and properties like contrast, color and brightness start to vanish or „move“.
the squareroot operator (a very simple operator) converges towards it‘s Eigenvalue 1 when applied recursively on it‘s own output (with real numbers).
By doing this, they change the activities and capabilities of the neuron networks profoundly.Taking these two large scale self-referential loops, the brain is dual-closed-loop and topologically a torus.
The real killer AI app will come with maxima/minima programs running on a quantum computer using a humongous highly correlated statistical database.
The big breakthrough will be when a quantum AI builds and maintains its own highly correlated statistical database from analyzing random data that is feed into it.For example, how the weather, time of day, internet traffic activity, and solar activity events effects stock market prices.
An aircraft can fly much faster than a bird, but watch birds for a while and marvel at the bio-avionics that allows them to use each feather as an aileron that allows them to end their flight by landing on a tree branch with near perfect precision.
One thing I was glad to see ("Your computer isn’t like my computer") is that substrates should matter more in (computer science) semantics.On robots in the future, a big issue will be the economic one of redistributing the wealth of robot makers and owners to the people the robots replace.
So this doesn't solve the issue of liability if the automatic driver kills somebody.SM: For lots of obvious reasons, driverless cars would reduce insurance premiums to a small fraction of what they are now.Sure, they will be less likely to get into accidents;
but this doesn't address assigning liability when they DO get into accidents, and encounter situations where a moral choice could have been made.SM: For obvious reasons, the behavior of driverless cars needs to be standardized with the aim of minimizing harm."Harm"
Should I progress on a straight line into a large crowd in the street, or intentionally swerve away from the crowd onto the sidewalk and possibly kill ten pedestrians?SM: if our technology ever gets that good, it's unlikely that there will be homeless people.We've already got the solution to homelessness, it is building houses and apartments and care facilities for the mentally disabled and disturbed.
It is socialism and taxes used to care for our relatively small percentage of people incapable of earning a living or taking care of themselves.
We already are "a technologically-advanced society that puts great value on the lives of mothers and children but doesn't care enough to solve the very solvable problem of homelessness."
Why in the world would it surprise you if some great technological advance occurs in AI, without changing humanity's selfish propensity to ignore the suffering of others when addressing it would cost them money?
The potential leap forward is profound: today the talk is about making devices the size of a domestic drone, capable of deciding for themselves and without human supervision who is to be attacked and then doing so.
If a majority of people don't own cars, the moral options are being configured by someone else and these options might not be transparent or particularly agreeable to the passengers.
I haven't forgotten that the only tangible benefit you offered for moral options was reduced liability for car manufacturers by putting "the owner of the vehicle on the moral spot again."
However that also will almost certainly be insignificant.One key problem will be decisions and bias based on using human data.Another, though related, is resolving intrinsic emotional content attached to thought.The biggest is that moral calculus will naturally lead to a decision to exterminate, subjugate or alter humans.Early NNs are evolutionary in nature with the same issues.
Five Top Artificial intelligence (AI) trends for 2019
As the recently launched AI Monthly digest shows, significant improvements, breakthroughs and game-changers in machine learning and AI are months or even weeks away, not years.
It is, therefore, worth the challenge to summarize and show the most significant AI trends that are likely to unfold in 2019, as machine learning technology becomes one of the most prominent driving forces in both business and society.
Current products are being enhanced (according to 44% of respondents), internal (42%) and external (31%) operations are being optimized and better business decisions are being made (35%).
In 1950, Alan Turing proposed his famous test to determine if a particular computer is intelligent by asking the ordinary user to determine if his conversational partner is a human or a machine.
PwC states that customers prefer to talk with companies face-to-face but chatbots are their second preferred channel, slightly outperforming email.
With their 24/7 availability, chatbots are perfect for emergency response (46% of responses in the PwC case study), forwarding conversations to the proper employee (40%) and placing simple orders (33%).
Academic work on AI often focuses on reducing the time and computing power required to train a model effectively, with the goal of making the technology more affordable and usable in daily work.
The technology of artificial neural networks has been around for a while (theoretical models were designed in 1943), but it works only when there are enough cores to compute machine learning models.
Expert augmented learning is one of most interesting ways to reduce the effort required to build reinforcement-based models or at least ones that are reinforcement learning-enhanced.
Contrary to policy-blending, expert augmented learning allows data scientists to channel their knowledge not only from another neural network but also from a human expert or another machine.
Researchers at deepsense.ai have recently published a paper on using transfer learning to break Montezuma’s Revenge, a game that reinforcement learning agents had long struggled to break.
By using GPipe, researchers were able to improve the performance of ImageNet Top-1 Accuracy (84.3% vs 83.5%) and Top-5 Accuracy (97.0% vs 96.5%), making the solution the new state-of-the-art.
In a related bid, US grocery giant Kroger recently started tests of unmanned delivery cabs, sans steering wheel and seats, for daily shopping.
Bolder still are those companies (including Uber) testing their autonomous vehicles on the roads of real towns, while others build models running in sophisticated simulators.
The adoption of AI tools will no doubt be one of the most important AI trends in 2019, especially as business and tech giants are not the only organizations using AI in their daily work.
On the heels of the first fatal accident involving an autonomous car, the question of who is responsible for crashes and the famous trolley problem are getting more important.
The issue of countering bias unconsciously developed within datasets and taken by machine learning models as truth incarnate is being discussed seriously by tech giants like Salesforce.
The machine learning community has also taken up the problem: there is a Kaggle competition aimed at building unbiased and cultural context-agnostic image recognition models to use in computer vision.
Considering the high level of standardization within diagnostic data, medical data is ripe for utilizing machine learning models, which can be employed to augment and support the treatment process.
- On 16. oktober 2021
↗️AI Artificial Intelligence Research Trend 2019 [Brian Ka Chan]
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