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05 August 2021

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On July 28, the Companies and Intellectual Property Commissionof South Africa granted the world's first patent on aninvention created by an artificial intelligence (AI)inventor. This development marks an important milestone inwhat will certainly be a significant battle for legal recognitionof such inventions in the United States and other countries.

Device for Autonomous Bootstrapping of UnifiedSentience aka DABUS is an AI developed byMissouri physicist Stephen Thaler. The recently-issued patentis directed to a food container based on fractal geometry.The patent application was filed on September 17, 2019 under thePatent Cooperation Treaty. 1 Under the heading ofinventor, the application identifies DABUS and statesThe invention was autonomously generated by an artificialintelligence." 2

It is important to note that patent applications in South Africaare not subject to a formalized patent-examination procedure of thekind found in the U.S., Canada, Europe, and many otherjurisdictions. Indeed, this aspect of the South Africanpatent system appears to have been a motivating factor for Thalerto seek patent protection in the country. Thus, it should notbe surprising that this patent was granted and the ultimate legalsignificance of this grant may be yet to be seen. With thatsaid, Thaler and his legal team have attempted so far invain to have AI-invented technologies recognized in othercountries including the United States.

In Europe, the Board of Appeal (BOA) of theEuropean Patent Office (EPO) handed down a pair ofpreliminary communications stating that an inventor on a patentapplication must have legal capacity. TheBOA's communications were responsive to appeals of theEPO's rejection of the DABUS patent applications. Anoral hearing before the BOA is scheduled for December 2021.

In the United States, Thaler filed U.S. Patent Application Nos.16/524,350 and 16/524,532 on July 29, 2019. 3Along with the patent applications, an Application Data Sheet(ADS) was filed in each case identifying a singleinventor with the given name DABUS and a family nameInvention generated by artificial intelligence.4 The ADSs also identify Stephen Thaler as theApplicant and Assignee. In both cases, the United StatesPatent and Trademark Office (USPTO) responded byissuing a Notice to File Missing Parts of Nonprovisional PatentApplication (the Notice) and asserted that the ADSdid not identify each inventor by his legal name.5 A subsequent petition to request supervisory review ofthe Notice and vacate the Notice was then filed by Thaler anddismissed by the USPTO. 6 Thaler then appealed tothe U.S. District Court for the EasternDistrict of Virginiaseeking, among other things, a reversal of the USPTO'sdecision on the petition.

In his complaint to the district court, Thaler argues that nonatural person meets the criteria for inventorship under thecurrent statutory and regulatory scheme. 7 Thus, if nocorrective action is taken, Thaler asserts that future AI-generatedpatents would enter the public domain once disclosed.8 Additionally, Thaler argues that allowingpatents on AI-generated inventions would be consistent with theConstitution and the Patent Act, will incentivize the furtherdevelopment of inventive machines, and that failure to do so allowsindividuals to take credit for work that they have not done.9 Finally, Thaler argues that the notion ofconception does not necessarily exclude artificialinventors. 10 Thaler seeks an order compelling the USPTOto reinstate the DABUS U.S. patent applications, a declaration thata patent application should not be rejected on the grounds that nonatural person is identified as an inventor, and a declaration thata patent application should properly identify an AI in cases wherethe AI has met the inventorship criteria. 11

Oral arguments were heard in the spring of 2021 and, so far, noorder has been issued. The outcome of this case will not onlyimpact the DABUS U.S. patent applications, but could also havedrastic implications for other areas of patent law such as ourunderstanding of conception and obviousness.

Footnotes

1. Patent Application No. PCT/IB2019/057809 (filed Sept.17, 2019).

2. Id. at [72].

3. Complaint for Declaratory and Injunctive Relief at 3,Stephen Thaler v. Iancu, No. 1:20-cv-00903 (E.D. Va. Aug. 6,2020).

4. Id. at 4.

5. Id. at 5.

6. Id. at 5.

7. Id. at 7.

8. Id.

9. Id. at 8-9.

10. Id. at 12.

11. Id. at 16-17.

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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Recent Developments In Artificial Intelligence And IP Law: South Africa Grants World's First Patent For AI-Created Invention - Intellectual Property -...

This article is part of the philosophy of artificial intelligence, a series of posts that explore the ethical, moral, and social implications of AI today and in the future

For decades, scientists have tried to create computational imitations of the brain. And for decades, the holy grail of artificial general intelligence, computers that can think and act like humans, has continued to elude scientists and researchers.

Why do we continue to replicate some aspects of intelligence but fail to generate systems that can generalize their skills like humans and animals? One computer scientist who has been working on AI for three decades believes that to get past the hurdles of narrow AI, we must look at intelligence from a different and more fundamental perspective.

In a paper that was presented at the Brain-Inspired Cognitive Architectures for Artificial Intelligence (BICA*AI), Sathyanaraya Raghavachary, Associate Professor of Computer Science at the University of Southern California, discusses considered response, a theory that can generalize to all forms of intelligent life that have evolved and thrived on our planet.

Titled, Intelligenceconsider this and respond! the paper sheds light on the possible causes of the troubles that have haunted the AI community for decades and draws important conclusions, including the consideration of embodiment as a prerequisite for AGI.

Structures, from the microscopic to human level to cosmic level, organic and inorganic, exhibit (respond with) phenomena on account of their spatial and temporal arrangements, under conditions external to the structures, Raghavachary writes in his paper.

This is a general rule that applies to all sorts of phenomena we see in the world, from ice molecules becoming liquid in response to heat, to sand dunes forming in response to wind, to the solar systems arrangement.

Raghavachary calls this sphenomics, a term he coined to differentiate from phenomenology, phenomenality, and phenomenalism.

Everything in the universe, at every scale from subatomic to galactic, can be viewed as physical structures giving rise to appropriate phenomena, in other words, S->P, Raghavachary told TechTalks.

Biological structures can be viewed in the same way, Raghavachary believes. In his paper, he notes that the natural world comprises a variety of organisms that respond to their environment. These responses can be seen in simple things such as the survival mechanisms of bacteria, as well as more complex phenomena such as the collective behavior exhibited by bees, ants, and fish as well as the intelligence of humans.

Viewed this way, life processes, of which I consider biological intelligenceand where applicable, even consciousnessoccur solely as a result of underlying physical structures, Raghavachary said. Life interacting with environment (which includes other life, groups) also occurs as a result of structures (e.g., brains, snake fangs, sticky pollen) exhibiting phenomena. The phenomena are the structures responses.

In inanimate objects, the structures and phenomena are not explicitly evolved or designed to support processes we would call life (e.g., a cave producing howling noises as the wind blows by). Conversely, life processes are based on structures that consider and produce response phenomena.

However different these life forms might be, their intelligence shares a common underlying principle, Raghavachary says, one that is simple, elegant, and extremely widely applicable, and is likely tied to evolution.

In this respect, Raghavachary proposes in his paper that intelligence is a biological phenomenon tied to evolutionary adaptation, meant to aid an agent survive and reproduce in its environment by interacting with it appropriatelyit is one of considered response.

The considered response theory is different from traditional definitions of intelligence and AI, which focus on high-level computational processing such as reasoning, planning, goal-seeking, and problem-solving in general. Raghavachary says that the problem with the usual AI branchessymbolic, connectionist, goal-drivenis not that they are computational but that they are digital.

Digital computation of intelligence haspardon the punno analog in the natural world, Raghavachary said. Digital computations are always going to be an indirect, inadequate substitute for mimicking biological intelligence because they are not part of the S->P chains that underlie natural intelligence.

Theres no doubt that the digital computation of intelligence has yielded impressive results, including the variety of deep neural network architectures that are powering applications from computer vision to natural language processing. But despite the similarity of their results to what we perceive in humans, what they are doing is different from what the brain does, Raghavachary says.

The considered response theory zooms back and casts a wider net that all forms of intelligence, including those that dont fit the problem-solving paradigm.

I view intelligence as considered response in that sense, emanating from physical structures in our bodies and brains. CR naturally fits within the S->P paradigm, Raghavachary said.

Developing a theory of intelligence around the S->P principle can help overcome many of the hurdles that have frustrated the AI community for decades, Raghavachary believes. One of these hurdles is simulating the real world, a hot area of research in robotics and self-driving cars.

Structure->phenomena are computation-free, and can interact with each other with arbitrary complexity, Raghavachary says. Simulating such complexity in a VR simulation is simply untenable. Simulation of S->P in a machine will always remain exactly that, a simulation.

A lot of work in the AI field is what is known as brain in a vat solutions. In such approaches, the AI software component is separated from the hardware that interacts with the world. For example, deep learning models can be trained on millions of images to detect and classify objects. While those images have been collected from the real world, the deep learning model has not directly experienced them.

While such approaches can help solve specific problems, they will not move us toward artificial general intelligence, Raghavachary believes.

In his paper, he notes that there is not a single example of brain in a vat in natures diverse array of intelligent lifeforms. And thus, the considered response theory of intelligence suggests that artificial general intelligence requires agents that can have a direct embodied experience of the world.

Brains are always housed in bodies, in exchange for which they help nurture and protect the body in numerous ways (depending on the complexity of the organism), he writes.

Bodies provide brains with several advantages, including situatedness, sense of self, agency, free will, and more advanced concepts such as theory of mind (the ability to predict other the experience of another agent based on your own) and model-free learning (the ability to experience first and reason later).

A human AGI without a body is bound to be, for all practical purposes, a disembodied zombie of sorts, lacking genuine understanding of the world (with its myriad forms, natural phenomena, beauty, etc.) including its human inhabitants, their motivations, habits, customs, behavior, etc. the agent would need to fake all these, Raghavachary writes.

Accordingly, an embodied AGI system would need a body that matches its brain, and both need to be designed for the specific kind of environment it will be working in.

We, made of matter and structures, directly interact with structures, whose phenomena we experience. Experience cannot be digitally computedit needs to be actively acquired via a body, Raghavachary said. To me, there is simply no substitute for direct experience.

In a nutshell, the considered response theory suggests that suitable pairings of synthetic brains and bodies that directly engage with the world should be considered life-like, and appropriately intelligent, anddepending on the functions enabled in the hardwarepossibly conscious.

This means that you can create any kind of robot and make it intelligent by equipping it with a brain that matches its body and sensory experience.

Such agents do not need to be anthropomorphicthey could have unusual designs, structures and functions that would produce intelligent behavior alien to our own (e.g., an octopus-like design, with brain functions distributed throughout the body), Raghavachary said. That said, the most relatable human-level AI would likely be best housed in a human-like agent.

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To create AGI, we need a new theory of intelligence - TechTalks

The show must go on, an often heard sentence that makes absolute sense in the pandemic hit the world. Yes, it all became at the end of 2019 when Covid-19 was first reported in Wuhan. Later, the virus spread across the globe and pushed governments to impose strict lockdowns. An international sports event that was supposed to take place in 2020 got delayed and finally, when people started living with the virus in 2021, the IOC and Japan, the host country, came forward to go on with it. One of the most welcomed guests in the summer Tokyo Olympics isartificial intelligence.Artificial intelligence in the Olympicsis changing the face of how tech is used in sports and other relative areas.

The sports industry implies calculative features that make it ideal for the applications ofartificial intelligence. For many years now,AI in sportsis widely lauded as a tool to improve athletes performance and calculate their moves. Olympics games have been an early adopter of technology ever since stopwatches and time trackers came into existence. Omegas Magic Eye camera, which debuted in 1948, gave us the first of many photo finishes for track events. Later, technology evolved to be a part of every sporting event that happened across the globe. Taking a big leap inartificial intelligenceandmachine learning, the Summer Tokyo Olympics is giving athletes and moderators a world-class experience onAI in sports.

Before jumping into howartificial intelligence in the Olympicsis powering athletes and help conduct games with more perfections, lets have a look at how technology is carrying out simple tasks in the Olympics village. A major fact about the Olympics is that hundreds of people from across the globe gather at a designated place. They are provided with accommodations properly. But a major challenge that event managers face is to take the athletes and their teams to the event avenue from their accommodations. Fortunately, autonomous vehicles come as a handy solution to tackle mobility issues. Autonomous vehicles are designated to chauffeur athletes around the Olympics village. Besides,machine learning-poweredself-driving cars are programmed to bring back athletic equipment such as javelins, discuses, and hammers to athletes during the games.

As mentioned earlier, the Olympics is a place where different countries collide. Along with them comes the language issue. Not everybody is familiar with other counties languages. Therefore, the Olympics is using AI-backed real-time translation systems to make different people understand instructions. The translator is installed on smartphones or other compatible devices, enabling users to select the target language, speak into the device, and subsequently deliver the spoken words in the targeted language. Besides, artificial intelligence in the Olympicsis being used in different forms like tracking tools, cloud-based broadcasting, robotic assistants, and 5G.

Big dataplays a drastic role in improving athletes performance. Especially, when it comes to surfing,big datais aiding the athletes by tracking down their performance and putting it in numbers. As a result, the USA Surfing organization has employed plenty of big data techniques to help its athletes gain an edge. Besides, big data is also being used to monitor the physiological state of athletes including cardiovascular output, sleep patterns, heart-rate variability, etc. On the other hand,machine learningdevised a big role in selecting the perfect site for surfing games.

Intel and Alibaba have jointly made 3DAT (3D Athlete Tracking) to closely monitor athletes moves. First debuted at the US Olympic Trials in Eugene Oregon, 3DAT is being used in the Tokyo Olympics for many purposes. The AI-base system takes images from five special trackside cameras, sends it to Alibaba cloud where it is converted to actionable insights. The method is seen as a disruptive way to find the potential of elite athletes.

Although Toyotas AI-powered humanoid basketball player is not one among the team members in the basketball games, the robot is a legend of its own kind. The humanoid robot has made history in 2019 when it netted the Guinness World Record for most consecutive basketball free throws by a humanoid robot. Now, the robot is being used in the Olympics to show its throwing skills before every commercial break.

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Artificial Intelligence in Olympics Introduces a New Phase of Sporting - Analytics Insight

Booz Allen Hamilton, a global technology consulting firm with expertise in analytics, digital, engineering and cyber technology, has partnered with NCStates Artificial Intelligence (AI) Academy to train their employees to meet a growing demand for talent in the area of artificial intelligence.

The AI Academy is a nationally registered apprenticeship program funded by a $6 million grant from the U.S. Department of Labor and designed to train individuals to assume roles within the area of artificial intelligence. Carla C. Johnson, Ph.D., a professor of science education in the NCState College of Education, is the principal investigator on the grant and the executive director of the program.

The program began in March 2020 with a 12-month planning period in which industry leaders worked alongside Johnson and collaborators from NCStates Department of Computer Science, including Collin Lynch, Ph.D., Thomas Price, Ph.D., Min Chi, Ph.D., and Noboru Matsuda, Ph.D.

The four-course, 40-week workforce development program has set a goal of upskilling 5,000 individuals to enter the artificial intelligence pipeline, with a specific focus on recruiting veterans, underrepresented workers, underemployed workers, informational technology employees and those seeking an opportunity to move into a different career.

Booz Allens workforce is roughly one-third military-connected, including veterans, Guard members, Reservists and military spouses. While transitioning service members come to the firm with relevant skills based on their military career, the apprenticeship offered as part of the AI Academy will allow these employees to use their military experience while training them for future-focused careers and gaining skills in critical areas.

Data analytics and machine learning skills, coupled with mission understanding from our military veterans, are critical to the future of our nations defense and much of modern industry, said Greg Wenzel, executive vice president at Booz Allen Hamilton, leader of the firms Army business, Global Defense CTO, and executive sponsor of Booz Allens Mil/Tech Workforce Initiative. Companies like Booz Allen need a constant influx of talent in order to help keep America at the forefront. But many of these skills require experiential learning to be useful, so an apprenticeship that combines classroom learning with on-the-job training is really ideal.

Johnson said there are currently too few qualified individuals in the artificial intelligence workforce and a high demand for workers with relevant skills. The AI Academy aims to remedy this gap by providing a streamlined approach to upskilling that is coupled with on-the-job training and mentoring within an employees existing workplace and by giving companies like Booz Allen an opportunity to select employees from their existing talent pool to invest in through training.

The NCState AI Academy is pleased to partner with Booz Allen Hamilton as we share similar missions focused on providing career pathways for transitioning military and veterans into much needed and highly skilled career opportunities. It is so important to support the thousands of individuals who have given so much to our country and we are excited to have Booz Allen Hamilton as our collaborator on this program, Johnson said.

Booz Allens Mil/Tech Workforce Initiative, a series of coordinated efforts to support extended transition from active duty to a civilian career, seeks out innovative programs like the AI Academy and connects transitioning military personnel with them while educating them about skills that are critical for roles in sectors like artificial intelligence and machine learning.

Booz Allen and the AI Academy have further engaged in helping to prepare transitioning service members for careers in the artificial intelligence sector during the Veteran Transition to Tech Networking Event at the North Carolina Military Business Center in Fayetteville, North Carolina, on Aug. 4.

AI Academy Program Director Stacey Smith, Ph.D., spoke during the event, which was co-hosted by Booz Allen and attended by transitioning service members, veterans and military spouses.Our work in the region requires a well-prepared workforce and local installations like Ft. Bragg and Camp Lejeune represent a huge opportunity to help transitioning service members stay in the area while creating a path to future-focused careers, said Jay Dodd, vice president at Booz Allen and a leader in the firms Fayetteville office. We love working with partners like NCState who know the region and are familiar with transitioning military talent, and we are excited for this and future engagements in the region in order to continue building a strong local tech talent pipeline.

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AI Academy Partners with Booz Allen Hamilton To Help Veterans, Transitioning Service Members Grow Skills in Artificial Intelligence - NC State College...

Illustration: Chaitanya Dinesh Surpur

Artificial intelligence (AI) is fast becoming a topic that is relevant to everyone today and, therefore, a subject that everyone ought to learn at least the rudiments of, say experts. From the humble milkman delivering packets of milk to households in the morning to the highest lawmakers and biggest industrialists, AI will increasingly touch everyone.

A lot of people look at AI as a vertical that calls for experts to develop, says Amit Anand, founding partner at Jungle Ventures, a VC firm in Singapore that has invested in several tech startups in India. However, both in his own mind and as an advisor to the Singapore government on the ethical use of AI, We have taken a view that AI is going to affect everybody, and hence everyone should be knowledgeable and have a certain level of understanding of AI.

The government has also complemented this with education at the grassroots level, Anand says, with centres of excellence and so on. The common man should know what happens when an AI programme takes over his loan processing, for example. How do you get the consumers ready for that wave, because its coming, he says.

There has been an explosion of use cases that take advantage of AI across industries, says Sumit Sarawgi, managing director and senior partner at Boston Consulting Group. In parallel, there has been an explosion of data that large companies and their end-consumers are generating, he adds.

This now makes it even more urgent that organisations around the world proactively embrace ways

of using AI in a responsible manner. While on the one hand, AI can make for better quality of services, improve customer experience and boost the financial performance of companies, the need to ensure its responsible use has also increased.

In Europe, for example, the European Union has published proposals for rules on AI, which include banning certain uses of AI, heavily regulating high-risk uses and lightly regulating less risky AI systems. In India, Niti Aayog, a government-backed think tank, released a discussion paper towards a national strategy for AI three years ago. Subsequently, in January 2020, a follow-on paper was also released on developing AI-specific technology infrastructure.

In the past, India didnt capture its share of benefits from technological advancements, such as semiconductor manufacturing, for example. Today, there is recognition that the country cant afford to miss the AI bus.

(This story appears in the 13 August, 2021 issue of Forbes India. You can buy our tablet version from Magzter.com. To visit our Archives, click here.)

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AI Special: Artificial Intelligence Will Affect Everyone. Here's What You Need To Know - Forbes India