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Tesla (TSLA) will hold its second annual AI Day in Palo Alto, California, Friday evening. The six-hour event will include updates on Tesla (TSLA)s work in artificial intelligence, Full Self-Driving, its supercomputer Dojo and maybe a humanoid robot, according to invitations posted online by Tesla (TSLA) supporters. The event is expected to be live-streamed.

Dojo is a supercomputer being designed to train AI systems to complete complex tasks like Teslas driver-assistance systems Autopilot and Full Self-Driving, which sometimes perform some driving tasks like steering and keeping up with traffic. Teslas previous AI Day included detailed technical explanations of the companys work in a bid to attract leading engineers.

Tesla CEO Elon Musk has claimed before that in the long run people will think of Tesla as an AI company, rather than a car company or energy company. He has said that Tesla AI may play a role in computers matching general human abilities, a huge milestone many experts say is decades away and perhaps unattainable. Musk, who has a long history of predictions, has said it may be reached in 2029.

But more limited and easier to develop forms of artificial intelligence like identifying emergency vehicles stopped on a highway have proven to be a significant hurdle for the company as it pursues its dreams of self-driving cars. AI powers Full Self-Driving, but the system has faced criticism and backlash as it still requires driver intervention to prevent collisions and Musks deadlines for its capabilities slip year after year.

And this summer Teslas director of artificial intelligence, Andrej Karpathy, exited the company, several months after it was announced he was taking a sabbatical.

Its not easy to predict what may or may not show up at any event helmed by Musk. Products heralded and talked about sometimes dont perform as designed like when Musk showed off the Tesla Cybertrucks supposedly unbreakable windows, that promptly broke and cant even be bought years later. (Three years after the event Tesla sells a T-shirt that memorializes the broken window, but it has yet to sell a Cybertruck.)

Musk has unquestionably disrupted entire industries with his work at Tesla and SpaceX. But hes also earned a reputation for missing deadlines and overpromising.

Last years AI Day surprise, for instance, was a Tesla robot, which was just a human dancing in a suit.

Musk then claimed that the automaker is building a 5-foot-8, 125-pound humanoid robot, called Optimus or Tesla Bot and a prototype would likely be unveiled this year. Its unclear if a prototype will be revealed Friday, but Musk tweeted Thursday that the event would include cool hardware demos.

Tesla is also working on wheeled robots for manufacturing and autonomous logistics, according to a Tesla job posting for a senior humanoid mechatronics robotics architect.

Musk claimed last year that the humanoid robot would have a profound impact on the economy. It would begin by working on boring, repetitive and dangerous tasks, he said.

Tesla and Musk are not, of course, the first to bet on robots. Robots already handle many factory jobs, and companies like Boston Dynamics have worked for years to develop humanoid, animal-like, and other robots for industrial applications.

Humanoid robots have long fascinated the public and earned a place in pop culture as powerful but sometimes dangerous. Tesla tapped into this when it posted on Instagram in a promotion for the event that, if you can run faster than 5 mph, youll be fine. The Tesla humanoid robot is planned to have a top speed of 5 mph, the automaker has said.

But creating a humanoid robot that rivals a humans abilities has proved incredibly difficult for robotics experts. Artificial intelligence has seen major advances yet trails the general abilities of a human toddler. Most robots in use today are restricted to simple tasks in basic environments like vacuuming a home or moving parts in a factory.

Tesla would not be the first automaker to build a humanoid robot, either. Honda worked on a series of robots, known as Asimo, for nearly 20 years. The Japanese company shut down development of Asimo in 2018. Korean automaker Hyundai bought Boston Dynamics in 2020.

Musk said Thursday that AI Day would be highly technical as it is meant for recruiting engineers to work on artificial intelligence, robotics and computer chips.

Engineers who understand what problems need to be solved will like what they see, Musk tweeted Friday.

Tesla did not respond to a request for comment.

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Tesla's AI Day is tonight. It may wow you -- or end with a gaffe - CNN

NIH has issued a challenge to develop an algorithm that can identify COVID-19 patients with a high risk of developing Long COVID.

NIH has issued a challenge to develop an algorithm that can identify COVID-19 patients with a high risk of developing Long COVID.

Studies have shown that recovery from infection with SARS-CoV-2, the virus that causes COVID-19, can vary from person to person. Most patients seem to recover from COVID-19 quickly and completely. However, others report experiencing COVID-19 symptoms that last for weeks or months or developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID.

To better understand which patients develop Long COVID, the NIH Rapid Acceleration of Diagnostics (RADx) initiative has launched the NIH Long COVID Computational Challenge (L3C). Part of the RADx-Radical program, the challenge aims to support research that uses artificial intelligence and machine learning tools to identify COVID-19 patients with a high risk of developing Long COVID. The challenge will award $500,000 in total cash prizes to first, second, and third place and up to five honorable mentions.

Researchers who participate in the challenge will create algorithms that analyze anonymized medical records to find out which patients with SARS-CoV-2 infections are most likely to develop Long COVID.

A panel of judges will test the projects on quantitative metrics, such as how well the projects can analyze data from different times, sites, and demographics. The judges will also test the projects on qualitative metrics, such as whether the tool can predict Long COVID risk before the patient is diagnosed in a different way and how likely health care providers would be to use the tool in their clinic.

The submission deadline is December 15, 2022. Winners will be announced in March 2023.

The tools developed for this challenge can help health care providers predict whether a person infected with SARS-CoV-2 has a high risk for having Long COVID later. If providers could identify people at high risk of Long COVID, they would have a chance to recommend ways to manage symptoms and prevent that outcome.

NIH Long COVID Computational Challenge (L3C)

Researching COVID to Enhance Recovery (RECOVER) Initiative

Studying Long COVID Might Help Others With Post-Viral Fatigue Ailments

Avindra Nath, M.D., the clinical director of the National Institute of Neurological Disorders and Stroke, discusses Long COVID research and how it can benefit people with other diseases.

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Predicting Long COVID with Artificial Intelligence - National Institutes of Health (NIH)

Opinion: AI can now help write essays that appear highly similar, if not indistinguishable, from that of a human author

By Eamon Costello and Mark Brown, DCU

Artificial intelligence-powered automatic essay writing technologies have taken a huge leap forward and are becoming widely available. In many cases, AI can help write essays that appear highly similar, if not indistinguishable, from that of a human author.

As this piece was being typed, Google AI helpfully suggested words to auto-complete these sentences. This speeds up writing but other tools and services, such as those based on the GPT-3 technology from the OpenAI group, can do much more than simple sentence completion: they can write the whole thing from scratch from start to finish.

AIs can generate surprising comprehensible text

By simply taking a prompt of a sentence or phrase from a human author, AIs can generate surprising comprehensible text. When the prompt "when an AI wrote my essay" was typed into a freely available online AI text generator to see what it would produce, the AI wrote: the following

"And don't forget", the professor said, "AIs are not allowed to write your essay or help you write your essay in any way" I groaned and rolled my eyes, thinking that I knew what he was going to say. I had never been good at writing essays, and I knew that an AI would only make it worse. But when I sat down to start it, I found myself in the middle of a beautiful essay. The words flowed easily from my fingers, and I was suddenly inspired. I had never written so well, and I was surprised that the AI had written it for me. I finished the essay and handed it in, feeling proud of myself. I didn't know what the professor would say, but I was sure that I had done a better job than if I had written it myself.

One can see how a student could generate text in this way, perhaps generating several texts, seeing which one suited best and then lightly editing for clarity. AI can also take large bodies of text as input and attempt to summarize them.

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From RT Radio 1's Today with Claire Byrne, Brian O'Connell reports on student essay mills

A student could prompt an AI to write several paragraphs and could then select which paragraphs seemed most well written and coherent. Finally, they could feed these paragraphs back to the AI asking it to summarise them. This could form the basis of a summary/conclusion section of an essay.

Using AI in this way requires particular skills. Just as correct spelling is becoming less critical with the ability of autocorrect, perhaps essay writing will evolve similarly. It may be that writers in the future engage in the higher level activity of orchestrating a composition, while AI does the heavy lifting of producing the actual sentences.

If using a spell checker isn't seen as cheating, will the use of AI for essay writing then be accepted as the new normal? Perhaps future students will use AIs to write their essays, while professors deploy AIs to check their authenticity.

Of course AIs can grade essays too, but does this mean that teachers will have less work?

Of course AIs can grade essays too, but does this mean that teachers will have less work? The jury is still out on this question: one major review of the research on AI in education found a conspicuous absence of reference to actual teachers. One scenario is a teacherless future where students are accelerated through courses of study by advanced robo-Profs.

A contrasting future has been foreseen by AI education expert Peirre Dillenbourg. He has predicted that we will have more teachers in the future, not less. He foresees teachers working in teams to oversee and design learning scenarios using multiple AIs dedicated to specific educational tasks.

That is the future taken care of but what about the present? Universities worldwide currently invest heavily in anti-plagiarism and academic integrity technologies. Many of these systems have been termed 'data-extractive', in that they often rely on extracting and mining large bodies of student work. At their worst these expensive systems can create climates of fear., where students feel they are being policed by big brother or sister.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education

AI essay writing may be seen as just another chapter in the long history of so-called "essay mills", services that students can use to commission and buy their homework from. Will AI make these services redundant in the future? What constitutes cheating and breaches of academic integrity in the world of AI? After all, irrespective of how we define cheating, who loses if the student does not fully engage in their own learning?

Something that educators can do is to have conversations with students about their learning and especially their assessment. A guiding principle should be that a student will always want to do the work themselves given the right conditions. This is the opposite of a starting principle that says: every student is a potential cheater.

Assessment mixes that are not completely dependent on traditional essays can allow students to express themselves in a variety of ways. Do we try to tame AI to protect old ways of learning or should we embrace its potential and reimagine our assessment practices to reflect the modern reality of living in the 21st century? One creative educator had his students purposefully use and evaluate AI essay writers as part of their assignment.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education. Student and teacher workloads should be key considerations in the design of assessment. Giving each other space to build trusting environments in which to teach and learn will require much human ingenuity, care and intelligence.

Dr Eamon Costello is an Associate Professor of Digital Learning at the DCU Institute of Education. Professor Mark Brown is Chair of Digital Learning and Director of the National Institute for Digital Learning at DCU.

The views expressed here are those of the author and do not represent or reflect the views of RT

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Will artificial intelligence be able to write my college essay? - RTE.ie

The UK Intellectual Property Office (UKIPO) released a guidance note for the examination of patent applications relating to artificial intelligence (AI) inventions. The UKIPO has confirmed that patents can be granted for AI inventions, given they provide a technical contribution to the state of the art.

Following a period of consultation that ran from 7 September to 30 November 2020, the new guidance note details the requirements of AI technologies to meet patentability criteria. As computer programs are specifically excluded from patentability criteria, the new guidance note and accompanying scenarios provide clarity when seeking to patent AI-based technologies.

The UKIPO defines AI as:

Technologies with the ability to perform tasks that would otherwise require human intelligence, such as visual perception, speech recognition, and language translation.

In summary, the new guidance note states that:

When considering the patentability of AI technology, focus is therefore placed on the technical contribution the invention makes to the state of the art.

Example scenarios

The UKIPO has also released a series of scenarios concerning AI or machine learning (ML) technologies and whether they meet the criteria for patentability. These scenarios focus on the issue of excluded matter and cover a breath of fields and technologies with worked examples of why each invention is or isnt excluded from patentability.

Particularly interesting scenarios include the training of a neural network, in which the end result of the process and its intended use can be the deciding factor in whether or not it is excluded from patentability.

For example, training a neural network classifier system to detect cavitation in a pump system is allowed. Such a method involves correlating data pairs with class values to produce a training dataset (wherein each class value is indicative of an extent of cavitation within the pump system) and then training the neural network classifier system, using the training dataset and back propagation.

The fact that this process is reliant on a computer program does not exclude it from patentability, since it provides a contribution which uses physical data to train a classifier for a technical purpose namely, the detection of cavitation in a pump system. The end result of this training, and its contribution, is therefore technical in nature.

By contrast, active training of a neural network is not allowed. Such a process involves determining areas of weakness in the neural network by comparing confidence levels to a threshold, then augmenting the training data with data related to the area of weakness. For example, a neural network used for detecting animals in pictures may struggle to identify cats, so the specimen data may be augmented with additional pictures of cats. This is more efficient than simply expanding the dataset across all elements.

While this method may result in a more efficient training method for a neural network, it does not itself produce a neural network that operates itself more effectively or efficiently. The mere identification of specific additional training data cannot be said to relate to a technical problem. As such, no technical problem has been solved within the neural network, and no technical effect is produced. A claim directed to this would therefore be excluded as a program for a computer as such.

Original post:
Guidance on examining patent applications relating to artificial intelligence inventions in the UK - Lexology