Category Archives: Artificial Intelligence

Music has forever been moved by technology from the invention of the phonograph, to Bob Dylan pivoting from acoustic to electric guitar, to the ubiquity of streaming platforms and, most recently, an ambitious attempt at crossing AI with commercial music.

FN Meka, introduced in 2021 as a virtual rapper whose lyrics and beats were constructed with proprietary AI technology, had a promising rise.

But just days after he signed on with Capitol Records the label that carried The Beatles, Nat King Cole and The Beach Boys and released his debut track Florida Water, the record company dropped him. His pink slip was a response in part to fans and activists widely criticizing his image a digital avatar with face tattoos, green braids and a golden grill and decrying his blend of stereotypes and slur-infused lyrics.

The AI artist, voiced by a real person and created by a company called Factory New, was not, technologically, a groundbreaking experiment. But it was a needle-mover for a discussion that is imminent within the industry: How AI will continue to shape how we experience music.

In 1984, classical trombonist George Lewis used three Apple II computers to program Yamaha digital synthesizers to improvise along with a live quartet. The resulting record a syrupy and spacey co-creation of computer and human musicians was titled Rainbow Family, and is considered by many as the first instance of artificially intelligent music

In the years since, advances in mixing boards popularized the practice of sampling and interpolation igniting debates about remixing old songs to make new ones (art form or cheap trick?) and Auto-Tune became a central tool in singers recorded and onstage performances.

FN Meka isnt the only AI artist out there. Some have been introduced, and lasted, with less commercial backing. YONA, a virtual singer-songwriter and AI poet made by Ash Koosha, has performed live at music festivals around the globe, including MUTEK in Montreal, Rewire in the Netherlands and Barbican in the U.K.

In fact, the most crucial and successful partnerships between AI and music have been under the hood, said Patricia Alessandrini, a composer, sound artist and researcher at Stanford Universitys Center for Computer Research in Music and Acoustics.

During the pandemic, the music world leaned heavily on digital tools to overcome challenges of sharing and playing music while remote, Alessandrini said. JackTrip Virtual Studio, for example, was an online platform used to teach university music lessons while students were remote. It minimized time delay, making audiovisual synchronicity much easier, and was born from machine learning sound research.

And for producers who deal with large music files and digital compression, AI can play a role in signal processing, Alessandrini said. This is important for sound engineers and musicians alike, saving time and helping them more smoothly create, or export, big records.

There are beneficial applications for technology and music to intersect when it comes to accessibility, she said. Instruments have been made using AI to require less strength or pressure in order to generate sound, for example allowing those with injuries or disabilities to play with eye movements alone.

Alessandrinis own projects include the Piano Machine which uses computers and voltages as fingers to create new sounds and Harp Fingers, a technology that allows users to play a harp without physically touching it.

On a meta level, algorithms are the ubiquitous drivers of online streaming platforms Spotify, Apple Music, SoundCloud, YouTube and others are constantly using machine learning, in less transparent ways, to personalize playlists, releases, lists of nearby concerts and music recommendations.

Less agreed upon is the concept of an AI artist itself. Reactions have been split among those loyal to the humanity of art; some who argued that if certain artists were indistinguishable from AI, then they deserved to be replaced; others who invited the newness; and many whose feelings fall somewhere in between.

With any cultural form, part of what youre dealing with are peoples expectations for what things sound like or what an artist looks like, Oliver Wang, a music writer and sociology professor at California State University, Long Beach, told Grid.

Some experts argue that those questions leave out a critical point: Whatever the technology, there is always a human behind the work and that should count.

Sometimes people dont know or see how much human work is behind artificial intelligence, said Adriana Amaral, a professor at UNISINOS in Brazil and expert in pop culture, influencers and fan studies. Its a team of people developers, programmers, designers, people from production and marketing.

But this misunderstanding isnt always the fault of the public, said Alessandrini. It often comes down to marketing. Its more exciting to say that somethings made entirely by AI, Alessandrini said. This was how FN Meka was marketed and promoted online as an AI artist. But while his lyrics, sound and beats were AI-generated, they then were performed by a human and animated, cartoon-style.

If it sounds strange that one would become a dedicated fan of a virtual persona, it shouldnt, Amaral said. The world of competitive video gaming, which is nothing without its on-screen characters, is a multibillion-dollar industry that sells out arenas worldwide.

Still, music purists and audiophiles and any person who appreciates music as an experience, rather than just entertainment may very well resist AI musicians. In particular, Alessandrini said, AI is better at generating content faster and copying genres, though unable to innovate new ones a result of training their computing models, largely, using what music already exists.

When a rap artist has these different influences and their own specific cultural experience, then thats the kind of magical thing that they use to create, Alessandrini said. You can say that Bobby Shmurda is one of the first Brooklyn drill artists because of a particular song. So thats a [distinctly] human capacity, compared to AI.

Alessandrini likens this artistic experience to the advancements of AI in medicine the applications of robotic technologies used during surgeries that are more efficient and mitigate the risk of human error. But, she said, there are some things that humans do better caring for a patient, understanding their suffering.

Its hard to imagine AI vocals ever reaching the emotional and beautifully human depths, say, of a Nina Simone or Ann Peebles; or channeling the authentic camaraderie and bounce of a group like OutKast.

In 2017, the French government commissioned mathematician and politician Cdric Villani to lay ambitious groundwork for the countrys artificially intelligent (AI) future.

His strategy, one that considered economics, ethics and education, foremost straddled the thinning line between creation and consumption.

The division between the noncreative machine and the creative human is ever less clear-cut, he wrote. Creativity, he went on to say, was no longer just an artists skill it was a necessary tool for a world of co-inhabitance, machine and human together.

Is that what is happening?

One cant talk about music on grand scales without also talking about money. Though FN Meka was a failure, AI has strong ties to the music sphere that wont be broken because one AI rapper got cut from a label. And it feels inevitable that another big record company or music festival will give it a go.

Why? It might all come down to cost, say experts and music listeners who run the cynicism gamut.

Wang said he has a sneaking suspicion that record companies and executives see AI musicians as a way to save money on royalty payments and travel costs moving forward.

Beyond the money-hungry music industry, there is also room for a lot of good moving forward with AI, said Amaral. She hopes FN Mekas image, and how he was received, was a wake-up call for whatever AI artist inevitably comes next. She also mentioned YONA, which she saw in concert in Japan, as a thin, white, able pop star not unlike many who dominate the music scene today.

We have all the technological tools to make someone who could be green, or fat or any way we like, and we still are stuck on these patterns, she said.

What will the landscape look like five or 10 or 15 years from now? Wang asks. Pop music, despite peoples cynicism, rarely stays static. Its constantly changing, and perhaps these computer-based attempts at creating artists will be part of that change.

Thanks to Dave Tepps for copy editing this article.

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The future of AI in music is now. Artificial Intelligence was in the music industry long before FN Meka. - Grid

Image: Pexels.com

There is no doubt that artificial intelligence (AI) is rapidly changing the online entertainment landscape. With the help of AI, we are now able to personalize our entertainment experiences with online casinos such as the betvictor sign up offer in ways that were once impossible.

For example, Netflix uses AI to recommend TV shows and movies that you might like based on your watching history. Amazons Prime Video service uses AI to suggest videos that you might be interested in.

But this is just the beginning. In the future, AI will become even more involved in our online entertainment experiences. Lets explore how!

The online entertainment industry has been booming in recent years, with a growing number of people streaming movies, TV shows, and other forms of content on a variety of platforms. This trend is only expected to continue in the coming years, as more and more people turn to the internet for their entertainment needs.

One of the key factors driving this growth is the increasing availability of artificial intelligence (AI) technology. AI can be used to improve the user experience on entertainment platforms by providing recommendations for what to watch next, helping to curate personalized content feeds, and even creating new forms of content through machine learning.

AI can be used to create more personalized experiences, in that, social media platforms are using AI to show users content that is most relevant to them. This ensures that users see content that they are interested in and are less likely to get bored with the platform.

AI is also being used to create more immersive experiences. For example, gaming companies are using AI-powered chatbots to provide players with in-game support and advice. These chatbots can understand natural language and provide helpful information when needed.

Here are the top three ways that AI will shape the future of online entertainment:

1. More personalized content recommendations

As AI gets better at understanding our individual preferences, we will see more personalized content recommendations. This means that we will see less of the one size fits all approach to the content recommendation and more tailored suggestions based on our specific interests.

2. Improved search and discovery features

AI will also help improve search and discovery features on entertainment platforms. For example, if youre looking for a particular type of movie or TV show, AI will be able to provide more accurate and relevant results.

3. Enhanced content creation and curation

Finally, AI will also play a role in enhancing content creation and curation. With the help of AI, content creators will be able to produce higher-quality content faster and more efficiently. Additionally, AI can also be used to curate existing content so that it is more relevant and engaging for users

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The Innovative Role of Artificial Intelligence in the Online Entertainment Industry - Varsity Online

The Artificial Intelligence (AI) in food and beverage market size was USD 3.34 Billion in 2020 and is expected to register a CAGR of 44.5% during the forecast period.

Scope of the report @https://market.us/report/artificial-intelligence-in-food-and-beverage-market/

Introduction: what is AI and its potential in the food and beverage industry

The potential for artificial intelligence (AI) in the food and beverage industry is significant. By automating repetitive and time-consuming tasks, AI can help food and beverage companies improve efficiency and quality while reducing costs.

For example, AI can be used to automate the inspection of food products for defects. Currently, inspectors must manually inspect each item on a production line, which is slow and prone to human error. However, by using computer vision to automatically detect defects, AI can speed up the inspection process while also reducing mistakes.

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In addition, AI can be used to optimize production schedules and ingredient lists. For example, by analyzing past sales data, AI can predict future demand for certain products and adjust production accordingly. AI can also suggest recipes based on available ingredients, helping companies reduce waste and save money.

Artificial intelligence is a new phenomenon that has attracted many major players to the food and beverage industry.The global artificial intelligence market in food and beverage is highly competitive.However, the market is dominated by a few market players in terms of market share.Major players focus on expanding their reach in unexplored areas and expanding their customer base abroad.

Here is List of BEST KEY PLAYERS Listed in Artificial Intelligence in Food and Beverage Market Report are:

Aboard SoftwareAnalytical Flavor SystemsDeepnifyImpactVisionIntelligentX BrewingNotCoSight Machine

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Artificial Intelligence in Food and Beverage Market Segmentation:

Artificial Intelligence in Food and Beverage Market is segmented in various types and applications according to product type and category. In terms of Value and Volume the growth of market calculated by providing CAGR for forecast period for year 2022 to 2032.

Most Important Types of Artificial Intelligence in Food and Beverage Market are covered in this Report:

HardwareSoftwareServices

Artificial Intelligence in Food and Beverage Market Product Applications:

Transportation and logisticsQuality ControlProduction Planning

Top countries data covered in this report:

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Artificial Intelligence in Food and Beverage Market to Record a CAGR of 44.5%, North America to Contribute Majority of Industry Growth: Market.us -...

SAN FRANCISCO--(BUSINESS WIRE)--Headroom, a meeting platform leveraging artificial intelligence to improve communications and productivity, today announced a $9 million investment led by Equal Opportunity Ventures with participation from Gradient Ventures, LDV Capital, AME Cloud Ventures and Morado Ventures. The capital brings total funding to date to $14 million and will be used to expand Headrooms team, product development and mobile offering. The company also recently added new Shareable Automatic Summaries to its suite of tools for remote and hybrid meetings, furthering its mission to support balanced, entertaining, productive and memorable meetings.

Virtual meetings have become the de facto method for gathering, connection and collaboration. According to Fortune Business Insights, the meeting collaboration market is expected to exceed $41 billion by 2029. Gartner predicts that by 2025, 75% of conversations at work will be recorded and analyzed, enabling the discovery of added organizational value or risk. Yet despite the increase in meetings, productivity and engagement rates are down. Even before the start of the pandemic, a Harvard Business Review survey revealed 65% of senior managers felt meetings kept them from completing their own work and 64% said meetings come at the expense of deep thinking. Smarter meetings may be the biggest opportunity for improved work productivity and satisfaction.

The more meetings held, the more time wasted, with too many people spending time in redundant meetings. Headroom is leveraging AI to help companies do more with less, enabling individual workers to be more productive, choose which meetings to attend and which to watch later, or just quickly get the key pieces of information discussed, said Julian Green, CEO and Co-Founder of Headroom. Particularly in this environment, where for startups every dollar and every meeting minute counts, those that can move faster and stay better connected with people wherever they are, in real time and asynchronously, will win.

Headroom is self-learning; its relevance and impact on productivity improves with use. Headroom data shows 90% of every meeting lacks useful information. To maximize the 10% meeting content that is helpful, the company developed Shareable Automatic Summaries which auto-generate highlight reels that provide key moments, shared notes and action items, and enable easy sharing with others. Additional platform functionality that maximizes synchronous and asynchronous communication includes:

"Hybrid work is here to stay and virtual meetings are the norm, but they allow for a wide margin of distraction," said Roland Fryer, Founder and Managing Partner at Equal Opportunity Ventures and newly appointed Headroom Board Member. Headroom at its core is an engagement and productivity platform - streamlining collaboration and information sharing, without a heavy lift. It saves time in scheduling, reporting and collaborating."

Simply put: meetings should be better. Unlike any other video communication and collaboration platform, Headroom is stateful. Meeting information is generated during live conversations, and can be augmented and accessed forever after. Participants are free to act naturally and engage with the information without being restricted by the actual meeting slot, said Andrew Rabinovich CTO and Co-Founder of Headroom. Those who didn't attend the meeting itself, have all the details readily available to them. With Headroom, this is automated and highlights go to non-attendee stakeholders who can replay key decisions. Our customers are also using it as an information resource they can search for key information later.

Headroom was co-founded by Julian Green and Andrew Rabinovich in 2020. The companys executive team experience spans founding and leadership roles at GoogleX, Houzz, Magic Leap, Patreon and Square. Headrooms platform currently serves more than 5,000 customers spanning technology and online education startups, as well as marketing, design, consulting and recruiting agencies. It is free with no usage caps or storage limits, and is available on Google Chrome with no download or app required. Users have full control over sharing of meeting information. Get started at https://www.goheadroom.com/.

ABOUT HEADROOM

Headroom, founded in 2020, is improving communication in meetings by augmenting meeting intelligence. Automated virtual meetings in Headroom allow attendees to act naturally, replay key decisions, build smart summaries and search everything later. Headroom is brought to you by an experienced team that has created and managed AI products used by billions of people at tech startups and large companies including Google and Magic Leap. The founders helped create the worlds leading Computer Vision, Augmented Reality and Virtual Reality products, started Unicorns, and have won a Webby. To get started with Headroom visit https://www.goheadroom.com/.

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Headroom Solves Virtual Meeting Fatigue with Artificial Intelligence that Eliminates Wasted Time and Reveals Essential Highlights - Business Wire

Companies come in promising the world and often dont deliver, said Bob Wachter, head of the department of medicine at the University of California, San Francisco. When I look for examples of true AI and machine learning thats really making a difference, theyre pretty few and far between. Its pretty underwhelming.

Administrators say algorithms the software that processes data from outside companies dont always work as advertised because each health system has its own technological framework. So hospitals are building out engineering teams and developing artificial intelligence and other technology tailored to their own needs.

But its slow going. Research based on job postings shows health care behind every industry except construction in adopting AI.

The Food and Drug Administration has taken steps to develop a model for evaluating AI, but it is still in its early days. There are questions about how regulators can monitor algorithms as they evolve and rein in the technologys detrimental aspects, such as bias that threaten to exacerbate health care inequities.

Sometimes theres an assumption that AI is working, and its just a matter of adopting it, which is not necessarily true, said Florenta Teodoridis, a professor at the University of Southern Californias business school whose research focuses on AI. She added that being unable to understand why an algorithm came to a certain result is fine for things like predicting the weather. But in health care, its impact is potentially life-changing.

Despite the obstacles, the tech industry is still enthusiastic about AIs potential to transform health care.

The transition is slightly slower than I hoped but well on track for AI to be better than most radiologists at interpreting many different types of medical images by 2026, Hinton told POLITICO via email. He said he never suggested that we should get rid of radiologists, but that we should let AI read scans for them.

If hes right, artificial intelligence will start taking on more of the rote tasks in medicine, giving doctors more time to spend with patients to reach the right diagnosis or develop a comprehensive treatment plan.

I see us moving as a medical community to a better understanding of what it can and cannot do, said Lara Jehi, chief research information officer for the Cleveland Clinic. It is not going to replace radiologists, and it shouldnt replace radiologists.

Radiology is one of the most promising use cases for AI. The Mayo Clinic has a clinical trial evaluating an algorithm that aims to reduce the hours-long process oncologists and physicists undertake to map out a surgical plan for removing complicated head and neck tumors.

An algorithm can do the job in an hour, said John D. Halamka, president of Mayo Clinic Platform: Weve taken 80 percent of the human effort out of it. The technology gives doctors a blueprint they can review and tweak without having to do the basic physics themselves, he said.

NYU Langone Health has also experimented with using AI in radiology. The health system has collaborated with Facebooks Artificial Intelligence Research group to reduce the time it takes to get an MRI from one hour to 15 minutes. Daniel Sodickson, a radiological imaging expert at NYU Langone who worked on the research, sees opportunity in AIs ability to downsize the amount of data doctors need to review.

When I look for examples of true AI and machine learning thats really making a difference, theyre pretty few and far between. Its pretty underwhelming.

Bob Wachter, head of the department of medicine at the University of California, San Francisco

Covid has accelerated AIs development. Throughout the pandemic, health providers and researchers shared data on the disease and anonymized patient data to crowdsource treatments.

Microsoft and Adaptive Biotechnologies, which partner on machine learning to better understand the immune system, put their technology to work on patient data to see how the virus affected the immune system.

The amount of knowledge thats been obtained and the amount of progress has just been really exciting, said Peter Lee, corporate vice president of research and incubations at Microsoft.

There are other success stories. For example, Ochsner Health in Louisiana built an AI model for detecting early signs of sepsis, a life-threatening response to infection. To convince nurses to adopt it, the health system created a response team to monitor the technology for alerts and take action when needed.

Im calling it our care traffic control, said Denise Basow, chief digital officer at Ochsner Health. Since implementation, she said, death from sepsis is declining.

The biggest barrier to the use of artificial intelligence in health care has to do with infrastructure.

Health systems need to enable algorithms to access patient data. Over the last several years, large, well-funded systems have invested in moving their data into the cloud, creating vast data lakes ready to be consumed by artificial intelligence. But thats not as easy for smaller players.

Another problem is that every health system is unique in its technology and the way it treats patients. That means an algorithm may not work as well everywhere.

Over the last year, an independent study on a widely used sepsis detection algorithm from EHR giant Epic showed poor results in real-world settings, suggesting where and how hospitals used the AI mattered.

This quandary has led top health systems to build out their own engineering teams and develop AI in-house.

That could create complications down the road. Unless health systems sell their technology, its unlikely to undergo the type of vetting that commercial software would. That could allow flaws to go unfixed for longer than they might otherwise. Its not just that the health systems are implementing AI while no ones looking. Its also that the stakeholders in artificial intelligence, in health care, technology and government, havent agreed upon standards.

A lack of quality data which gives algorithms material to work with is another significant barrier in rolling out the technology in health care settings.

Over the last several years, large, well-funded systems have invested in moving their data into the cloud, creating vast data lakes ready to be consumed by artificial intelligence.|Elaine Thompson/AP Photo

Much data comes from electronic health records but is often siloed among health care systems, making it more difficult to gather sizable data sets. For example, a hospital may have complete data on one visit, but the rest of a patients medical history is kept elsewhere, making it harder to draw inferences about how to proceed in caring for the patient.

We have pieces and parts, but not the whole, said Aneesh Chopra, who served as the governments chief technology officer under former President Barack Obama and is now president of data company CareJourney.

While some health systems have invested in pulling data from a variety of sources into a single repository, not all hospitals have the resources to do that.

Health care also has strong privacy protections that limit the amount and type of data tech companies can collect, leaving the sector behind others in terms of algorithmic horsepower.

Importantly, not enough strong data on health outcomes is available, making it more difficult for providers to use AI to improve how they treat patients.

That may be changing. A recent series of studies on a sepsis algorithm included copious details on how to use the technology in practice and documented physician adoption rates. Experts have hailed the studies as a good template for how future AI studies should be conducted.

But working with health care data is also more difficult than in other sectors because it is highly individualized.

We found that even internally across our different locations and sites, these models dont have a uniform performance, said Jehi of the Cleveland Clinic.

And the stakes are high if things go wrong. The number of paths that patients can take are very different than the number of paths that I can take when Im on Amazon trying to order a product, Wachter said.

Health experts also worry that algorithms could amplify bias and health care disparities.

For example, a 2019 study found that a hospital algorithm more often pushed white patients toward programs aiming to provide better care than Black patients, even while controlling for the level of sickness.

Last year, the FDA published a set of guidelines for using AI as a medical device, calling for the establishment of good machine learning practices, oversight of how algorithms behave in real-world scenarios and development of research methods for rooting out bias.

The agency subsequently published more specific guidelines on machine learning in radiological devices, requiring companies to outline how the technology is supposed to perform and provide evidence that it works as intended. The FDA has cleared more than 300 AI-enabled devices, largely in radiology, since 1997.

Regulating algorithms is a challenge, particularly given how quickly the technology advances. The FDA is attempting to head that off by requiring companies to institute real-time monitoring and submit plans on future changes.

But in-house AI isnt subject to FDA oversight. Bakul Patel, former head of the FDAs Center for Devices and Radiological Health and now Googles senior director for global digital health strategy and regulatory affairs, said that the FDA is thinking about how it might regulate noncommercial artificial intelligence inside of health systems, but he adds, theres no easy answer.

FDA has to thread the needle between taking enough action to mitigate flaws in algorithms while also not stifling AIs potential, he said.

Some argue that public-private standards for AI would help advance the technology. Groups, including the Coalition for Health AI, whose members include major health systems and universities as well as Google and Microsoft, are working on this approach.

But the standards they envision would be voluntary, which could blunt their impact if not widely adopted.

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Artificial intelligence was supposed to transform health care. It hasn't. - POLITICO

Artificial intelligence can now make convincing images of buildings. Is that a good thing? | Forum | Archinect '); }, imageUploadError: function(json, xhr) { alert(json.message); } }}); /*$(el).ckeditor(function() {}, {//removePlugins: 'elementspath,scayt,menubutton,contextmenu',removePlugins: 'liststyle,tabletools,contextmenu',//plugins:'a11yhelp,basicstyles,bidi,blockquote,button,clipboard,colorbutton,colordialog,dialogadvtab,div,enterkey,entities,filebrowser,find,flash,font,format,forms,horizontalrule,htmldataprocessor,iframe,image,indent,justify,keystrokes,link,list,maximize,newpage,pagebreak,pastefromword,pastetext,popup,preview,print,removeformat,resize,save,smiley,showblocks,showborders,sourcearea,stylescombo,table,specialchar,tab,templates,toolbar,undo,wysiwygarea,wsc,vimeo,youtube',//toolbar: [['Bold', 'Italic', 'BulletedList', 'Link', 'Image', 'Youtube', 'Vimeo' ]],plugins:'a11yhelp,basicstyles,bidi,blockquote,button,clipboard,colorbutton,colordialog,dialogadvtab,div,enterkey,entities,filebrowser,find,flash,font,format,forms,horizontalrule,htmldataprocessor,iframe,image,indent,justify,keystrokes,link,list,maximize,newpage,pagebreak,pastefromword,pastetext,popup,preview,print,removeformat,resize,save,smiley,showblocks,showborders,sourcearea,stylescombo,table,specialchar,tab,templates,toolbar,undo,wysiwygarea,wsc,archinect',toolbar: [['Bold', 'Italic', 'BulletedList','NumberedList', 'Link', 'Image']],resize_dir: 'vertical',resize_enabled: false,//disableObjectResizing: true,forcePasteAsPlainText: true,disableNativeSpellChecker: false,scayt_autoStartup: false,skin: 'v2',height: 300,linkShowAdvancedTab: false,linkShowTargetTab: false,language: 'en',customConfig : '',toolbarCanCollapse: false });*/ }function arc_editor_feature(el) { $(el).redactor({minHeight: 300,pasteBlockTags: ['ul', 'ol', 'li', 'p'],pasteInlineTags: ['strong', 'br', 'b', 'em', 'i'],imageUpload: '/redactor/upload',plugins: ['source', 'imagemanager'],buttons: ['html', 'format', 'bold', 'italic', 'underline', 'lists', 'link', 'image'],formatting: ['p'],formattingAdd: {"figcaption": {title: 'Caption',args: ['p', 'class', 'figcaption', 'toggle']},"subheading": {title: 'Subheading',args: ['h3', 'class', 'subheading', 'toggle']},"pullquote-left": {title: 'Quote Left',args: ['blockquote', 'class', 'pullquote-left', 'toggle']},"pullquote-centered": {title: 'Quote Centered',args: ['blockquote', 'class', 'pullquote-center', 'toggle']},"pullquote-right": {title: 'Quote Right',args: ['blockquote', 'class', 'pullquote-right', 'toggle']},"chat-question": {title: 'Chat Question',args: ['p', 'class', 'chat-question', 'toggle']}, "chat-answer": {title: 'Chat Answer',args: ['p', 'class', 'chat-answer', 'toggle']}, },callbacks:{ imageUpload: function(image, json) { $(image).replaceWith('

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Artificial intelligence can now make convincing images of buildings. Is that a good thing? - Archinect

Inventions such as the wheel, the printing press, light bulb, telescope, microscope, transistor, microchip, and the Internet, are amazing in and of themselves. However, these, and thousands of other inventions have also provided an indispensable foundation, and a toolkit, for other, newer inventions, leading to a pace of innovative progress unlike anything seen before. For example, the microchip, leading to the computer, has helped humans conceive of and find new inventions by helping them process information more efficiently. But the computer, until recently, has only helped to solve inventive problems framed by humans and arrive at solutions that are, in some sense, only anticipated by humans. Until now, prior inventions have only provided assistance to the inventive activity of human beings; historically, the human mind has ultimately been the source of invention.

That paradigm, however, is changing. Recent advances in computer technology, as well as the exponential growth in available data, are leading to the advent of artificial intelligence and machine learning. Some have said that most of the data ever created has been created in the last several years. What we call artificial intelligence represents a massive increase in the power of computer problem solving that has been enabled by massive amounts of new data. Data is like fuel the more data available to computer algorithms, the more powerful those algorithms become in operations that approach machine learning. And, with this new power, machines are becoming increasingly able to formulate problems and imagine (i.e., invent) solutions in ways that were previously reserved for human beings.

The possibility that a machine can be an inventor raises interesting questions for how we think about incentivizing inventorship and the kinds of monopolistic protection we afford to inventions in the future. Patent law is the body of law that deals with, and specifically, provides certain protections for, inventions. The concept of inventorship is core to patent law, and, with the change in the inventorship paradigm noted above, the question naturally arises who, or what, under the law can be an inventor? Can a machine be an inventor? More specifically, can artificial intelligence software be listed as an inventor on a patent application? This is the question that was recently addressed by the United States Court of Appeals for the Federal Circuit on Aug. 5.

In Thaler v. Vidal, the Appellate Court held that an inventor must be a naturalized person. Put another way, only human beings can be inventors. This case arose when Thaler tried to acquire patents for inventions developed by his Creativity machine known as DABUS. The United States Patent and Trademark Office (USPTO) denied Thalers applications, claiming that there must be a human inventor. Similarly, patent courts in the European Union, the UK, and Australia, all ruled against Thaler. Only South Africa allowed for an artificial intelligence inventor and granted Thaler a patent.

Here, in the United States, Thaler appealed the USPTO decision to the US District Court before appealing to the Appellate court. Both the District Court and the Appellate Court made the same conclusion that non-human entities cannot be inventors. No other American courts have addressed this issue, and unless the United States Supreme Court has an opportunity to consider the issue (in Thalers case or in a future case), the Federal Circuit Court of Appeals is the final authority on patent matters.

In its analysis of the issue, the Court of Appeals declined to engage in an analysis of the nature of an invention or the rights that might be attributed to artificial intelligence. Instead, the court left these issues open in favor of the safer, if perhaps no less controversial, practice of statutory interpretation. The Patent Act states that an inventor is the individual, or, if a joint invention, the individuals collectively who invented or discovered the subject matter of the invention. Because the patent statute does not define individual, the appellate court instead relied upon a previous United States Supreme Court case, Mohamad v. Palestinian Authority, in which the Supreme Court held that the word ordinarily refers to a human entity. Thus, the Appellate Court ultimately held that the term individual in the Patent Act refers only to natural persons and that artificial intelligence does not count as an inventor on a patentable invention.

The Mohamad case dealt with the application of the word individual as it pertains to the Torture Victim Protection Act of 1991 (the VPA). It is also worth emphasizing that in Mohamad, the Supreme Court held only that the term individual ordinarily means [a] human being, a person, and that its holding with regard to the VPA does not mean that the word individual invariably means natural person. Furthermore, the Supreme Court opinion dealt with whether a corporate or governmental agency could be considered an individual, and did not address the applicability of the word to a singular, individual, artificial intelligence.

The Appellate Court buttressed its decision denying the title of inventor to artificial intelligence by noting that nothing in the law shows or implies that the legislature intended the word individual to mean anything other than a natural person. The Court pointed to the fact that the Patent Act uses pronouns such as himself or herself when referring to inventors, indicating that congress did not intend to allow non-human inventors. The act does not use itself, which is the term that the court reasons Congress would have used if it intended to permit non-human inventors.

However, these are not the only ways in which the legislature could have illustrated an intent that the term individual be interpreted broadly. Indeed, as Thaler argued before the court, limiting innovation to natural persons is contrary to the general policy behind the Patent Act, namely to encourage innovation and public disclosure. As already stated, artificial intelligence could facilitate innovation at a rate and efficiency previously unseen. By limiting patent protections to inventions created purely by a human mind, the Appellate Court removes much of the incentive to utilize what promises to be the most powerful innovative tool in our toolbox. However, the court rejected this argument, and briefly categorized it as speculative, before referring again to its textualist approach.

Because the court relied on this textualist approach and did not consider the nature of inventorship, several questions remain to be answered. For example, because Thaler actually listed DARBUS as the inventor, Thaler presented no fact question regarding inventorship; he was simply asking the Court to determine that DARBUS could be an inventor. The Court expressly acknowledges this point: We are not confronted today with the question of whether inventions made by human beings with the assistance of AI are eligible for patent protection. So where, exactly, does the involvement of artificial intelligence in the inventorship process cross the line into an inventive activity that deprives the invention of patentability? How will companies navigate that line and structure their R&D to optimize the benefits of massive computing power and the potential for patent protection?

Additionally, Patents can only be granted if the invention is new and non-obvious. With the advent of powerful computers that can anticipate many inventions of which a human is capable, will those innovations, when eventually created by a human being, be determined to lack novelty and non-obviousness on the grounds that artificial intelligence as already thought of it? Will we reach a point in which artificial intelligence preempts the ability of a natural person to acquire a patent when that person eventually comes up with the invention on his own? And if that is the case, what effect will that have on the ultimate incentives for innovation generally?

Thaler plans to appeal to the US Supreme Court and argues that the Federal Circuit adopted a narrow and textualist approach that ignores the purpose of the Patent Act with real negative social consequences. Apart from any possible future action by the Supreme Court, further legislation is always possible after lawmakers, policymakers, think tanks, and academics have had the opportunity to re-evaluate existing law and its impact on innovation in light of growing experience with AI and emerging technologies. The Department of Commerce, which houses the USPTO, will no doubt continue to monitor this issue very closely and issue periodic reports. For further exploration of issues related to inventorship as related to artificial intelligence, see the USPTOs report here; and see generally, the USPTOs AI website.

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Artificial Intelligence and Inventorship: Federal Court of Appeals Determines That Patent Inventors Must Be Human - JD Supra

Artificial intelligence in China: Orchestras but no ultrasounds SupChina Skip to the content

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Artificial Intelligence (AI) is a critical technology for giving maritime companies a performance edge, but how can it be used to get ahead of the market? And how can AI accelerate digital transformation and meet the challenges of the upcoming energy transition?

Lloyds Registers new report, Artificial Intelligence in Maritime a learning curve, explores the current state of AI in the maritime industry, including market sizing and use cases, and explains how AI has the potential to revolutionise maritime operations and create significant competitive advantages for those companies that embrace it.

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Many futurists insist that technological advances will enable humans to upload our minds into computer systems, thereby allowing us to live forever, defying our biological limitations. This concept is deeply flawed but has gained popular attention in recent years. So much so, Amazon has a TV series based on the premise called Upload, not to mention countless other pop-culture references.

As background, the concept of mind uploading is rooted in the very reasonable premise that the human brain, like any system that obeys the laws of physics, can be modeled in software if sufficient computing power is devoted to the problem. To be clear, mind-uploading is not about modeling human brains in the abstract, but modeling specific people, their unique minds represented in such detail that every neuron is accurately simulated, including the massive tangle of connections among them.

Of course, this is an extremely challenging task. There are more than 85 billion neurons in your brain, each with thousands of links to other neurons.Thats around 100 trillion connections a thousand times more than the number of stars in the Milky Way. Its those trillions of connections that make you who you are your personality and memories, your fears and skills and ambitions.To reproduce your mind in software (sometimes called an infomorph), a computer system would need to precisely simulate the vast majority of those connections down to their most subtle interactions.

That level of modeling will not be done by hand. Futurists who believe in mind uploading often envision an automated process using some kind of super-charged MRI machine, that captures the biology down to the molecular level.They further envision the use of artificial intelligence (AI) software to turn that detailed scan into a simulation of each unique neuron and its thousands of connections to other neurons.

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This is a wildly challenging task but is theoretically feasible. It is also theoretically feasible that large numbers of simulated minds could coexist inside a rich simulation of physical reality.Still, the notion that mind uploading will enable any biological human to extend their life is deeply flawed.

The real issue is that the key words in that prior sentence are their life.While it is theoretically possible with sufficient technological advances to copy and reproduce the form and function of a unique human brain within a computer simulation, that human who was copied would still exist in their biological body. Their brain would still be safely housed inside their skull.

The person that would exist in the computer would be a copy.

In other words, if you signed yourself up for mind uploading, you would not feel like you suddenly transported yourself into a computer simulation.In fact, you would not feel anything at all. The brain copying process could have happened without your knowledge while you were asleep or sedated, and you wouldnt have the slightest inkling that a reproduction of your mind existed in a simulation.

We can think of the copy as a digital clone or twin, but it would not be you.It would be a mental copy of you, including all of your memories up to the moment your brain was scanned.But from that time on, the copy would generate its own memories inside whatever simulated world it was installed in. It might interact with other simulated people, learning new things and having new experiences.Or maybe it would interact with the physical world through robotic interfaces.At the same time, the biological you would be generating new memories and skills and knowledge.

In other words, your biological mind and your digital copy would immediately begin to diverge. They would be identical for one instant and then grow apart. Your skills and abilities would diverge.Your knowledge and understanding would diverge. Your personality and objectives would diverge.After a few years, there would be significant differences. And yet, both versions would feel like the real you.

This is a critical point the copy would have the same feelings of individuality that you have. It would feel just as entitled to own its own property and earn its own wages and make its own decisions.In fact, you and the copy would likely have a dispute as to who gets to use your name, as you would both feel like you had used it your entire life.

If I made a copy of myself, it would wake up in a simulated reality and fully believe it was the real Louis Barry Rosenberg, a lifelong technologist. If it were able to interact with the physical world through robotic means, the copy would feel like it had every right to live in my house and drive my car and go to my job.After all, the copy would remember buying that house and getting that job and doing everything else that I can remember doing.

In other words, creating a digital copy through mind uploading has nothing to do with allowing you to live forever. Instead, it would create a competitor who has identical skills, capabilities, and memories and who feels equally justified to be the owner of your identity.

And yes, the copy would feel equally married to your spouse and parent to your children. In fact, if this technology was possible, we could imagine the digital copy suing you for joint custody of your kids, or at least visitation rights.

To address the paradox of creating a copy of an individual rather than enabling digital immortality, some futurists suggest an alternate approach. Instead of scanning and uploading a mind to a computer, they hypothesize the possibility of gradually transforming a persons brain, neuron by neuron, to a non-biological substrate. This is often referred to as cyborging rather than uploading and is an even more challenging technical task than scanning and simulating. In addition, its unclear if gradual replacement actually solves the identity problem, so Id call this direction uncertain at best.

All this said, mind uploading is not the clear path to immortality that is represented in popular culture. Most likely, its a path for creating a duplicate that would react exactly the way you would if you woke up one day and were told Sorry, I know you remember getting married and having kids and a career, but your spouse isnt really your spouse and your kids arent really your kids and your job isnt really...

Is that something anyone would want to subject a copy of yourself to?

Personally, I see this as deeply unethical. So unethical, I wrote a cautionary graphic novel over a decade ago called UPGRADE that explores the dangers of mind uploading. The book takes place in a future world where everyone spends the majority of their lives in the metaverse.

What the inhabitants of this world dont realize is that their lives in the metaverse are continuously profiled by an AI system that observes all their actions and reactions, so it can build a digital model of their minds from a behavioral perspective (no scanning required). When the profiles are complete, the fictional AI convinces people to upgrade themselves by ending their life and allowing their digital copies to fully replace them.

When I wrote that book 14 years ago, it was intended as irony. And yet theres an emerging field today that is headed in this very direction. Euphemistically called the digital afterlife industry, there are many startups pushing to digitize loved ones so that family members can interact with them after their death. There are even startups that want to profile your actions in the metaverse so you too can live forever in their digital world. Even Amazon recently stepped into this space by demonstrating how Alexa can clone the voice of your dead grandmother and allow you to hear her speak.

With so much activity in this space, how long before a startup begins touting the cost-saving benefits of ending your life early and allowing your digital replacement to live on? I fear its just a matter of time.My only hope is that entrepreneurs will be honest with the public about the reality of mind uploading its not a pathway to immortality.

At least, not the way many people think.

Louis Rosenberg, Ph.D., is a pioneer in fields of VR, AR and AI. He earned his Ph.D. from Stanford University, has been awarded over 300 patents, and founded a number of successful companies. Rosenberg began his work at Air Force Research Laboratorywhere he developed the first functional augmented reality system to merge real and virtual worlds. Rosenberg is currently CEO of Unanimous AI, the chief scientist of the Responsible Metaverse Alliance and global technology advisor to the XR Safety Initiative (XRSI).

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Identity crisis: Artificial intelligence and the flawed logic of mind uploading - VentureBeat