Category Archives: Ai

A CT scan of a composite microstructure

In an effort to extend the life of plastic parts and products, there are many different programmes and innovations being implemented by engineering and design companies. Here are just a couple.

Hexagons Manufacturing Intelligence Division is using simulation, artificial intelligence (AI) and machine learning to design materials that perform better, weigh 40-50% less and have a longer lifetime than traditional materials.

Guillaume Boisot, head of materials engineering at Hexagon Manufacturing Intelligence, said: When you think sustainability, lightweighting is one aspect of it, reducing weight as much as possible by switching or substituting materials from metal to plastic or reinforcing materials with fibre so we have carbon fibre, glass fibre and some natural fibre.

Not only does the final product have better properties but, because simulation and AI are being used, the material is being tested virtually, meaning that physical materials arent being consumed and the only cost is CPU time.

With our customers we have created maybe the most advanced material database for reinforced plastics, Boisot claimed: We need to push the limits of those materials so that we can reduce the weight of a car as much as possible.

Especially in e-mobility where the biggest issue is battery range. If we can reduce the weight, while improving the materials in and the performance of the battery, we could reach 1,000km ranges.

Energy chains, or e-chains, are used to protect and guide moving cables and hoses, ensuring reliable power and data supply for moving machinery in applications from offshore oil platforms to machine tools, cranes to theatres.

They are designed to last millions of movement cycles, or three years, after which they are typically scrapped.

Justin Leonard, director of igus UK, said: We have launched a programme called chainge so that customers can send their used e-chains back for recycling to transform them into something useful like construction hoardings and exhibition stands.

More than three tonnes of e-chains have been collected for recycling by igus and sent to its recycling partner, MyWaste in Hull, where they are remanufactured into Storm Boards a proprietary name for versatile structural plastic boards. The recycling process comprises several stages. The first is size reduction, using a shredder to reduce the size of the parts to around 20mm. The plastic pieces then enter a granulator, further reducing the material size to 5mm. The e-chain material is then fed into a mixer where it is blended with single-use plastics such as mixed packaging to make a homogeneous material for the board production.

Mike Derbyshire, MY Group site manager, explained: The board production stage is essentially like a giant waffle-making machine with hot plates that melt and cure the board mixture from both sides at up to 200C for about 10 minutes.

First, a skin of finely ground, single-polymer plastic is spread over the plates. Then the mixed material, including the e-chain polymer, is evenly spread across the plate, which provides the boards core. The system is closed and heated and, once cured, it is removed, trimmed, cooled and quality checked.

Storm Boards have got a wide range of applications, said Derbyshire, such as furniture and shopfitting hoardings, and also in the construction industry. We see it as an eco-friendly alternative to wood products, and it also gives a new life to materials that arent currently recycled.

igus offers the Storm Boards as free display stands to its customers. They are installed at their premises and come with a range of igus product samples for them to evaluate.

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Simulation and AI help extend the life of plastic parts and products - Professional Engineering

In Lockhart, the barbecue capital of Texas, theres a new facility thats on the cutting edge of the vegetable world.

The Iron Ox greenhouse sits in a quiet part of town, across the street from a state prison. Inside, the Silicon Valley-based outfit grows various varieties of leafy greens, like lettuce and basil. They have plans to add tomatoes and strawberries in the near future.

But its not so much what theyre producing, as how theyre doing it, that caught the attention of the Texas Standards Michael Marks, who recently visited the facility. Listen to the interview above or read the transcript below.

This transcript has been edited lightly for clarity:

Michael Marks: Iron Ox is one of a few companies that are getting into high tech indoor farming. This one is founded by a Texan named Brandon Alexander, who used to work for Google. And what theyre doing is theyre trying to use robotics and artificial intelligence to improve agriculture. So this is actually their second greenhouse. They also have one in California. And the one in Lockhart is really big over 500,000 square feet with these tall ceilings.

The greenhouse is a full service operation. They handle everything from planting to packaging. Overall, the place kind of feels like a plant nursery front, The Matrix. They grow these plants in these six-by-six foot modules. And thats where their whole life kind of takes place. Most of the growth happens in one of two big rooms. One is filled with what look like library shelves because the racks are all filled with plants and theyre underneath these LED lights. And the other is the greenhouse itself where the trays. Just sit on the ground and get shuttled around by robots.

Peter Cheng, computer vision engineer at Iron Ox, speaks to media during a tour of Iron Oxs new farming facility in Lockhart. Michael Minasi/Texas Standard

Texas Standard: Can you say a little bit more about the technology that theyre using? Did you say artificial intelligence?

Michael Marks: They use AI as well as robotics. One of the robots is called Grover, which looks a little like a giant Roomba. Its purpose is to move these trays around the greenhouse, and when theyre full with plants and water, they weigh over 1,000 pounds. One of the places that Grover takes the plants is to another robot called Phil. Phil uses a sensor to detect what a given set of plants needs so maybe more of a certain type of nutrient or water. And then it releases that mixture into the tray. So it fills it up. Get it?

The AI and data collection comes in where they photograph these plants from multiple angles over their life cycle in order to gather tons and tons and tons of data about how well a particular plant is growing. One of the folks who talked to us about this is Peter Chang. Hes a senior computer vision engineer at Iron Ox.

Peter Chang: So every single plant that were harvesting and that were growing and scanning, those provide the information for us to feed back into our systems and improve the way we grow our plants.

Employees package produce at Iron Oxs new farming facility in Lockhart.Michael Minasi/Texas Standard

Texas Standard: How do the pros and cons stack up when you compare with what a company like this is doing versus more traditional egg?

Michael Marks: Well, the data is an advantage. Of course. The idea is the more data you gather, the better you get the plant, the more efficiently you grow it. Also, growing indoors and controlling the climate is a huge advantage. You think about how the climate is changing and the effect thats having on farmers, as well as issues like herbicide drift, pests, insects. Those are all taken out of the equation. And you can grow anything anywhere more or less any time. Seasons are not so much a consideration.

The flip side is that there is a lot of overhead. Traditional farming is not necessarily cheap but this is a different level of expenditure in terms of what is required to get started. And also robots are better suited for some tasks than others. Its easy for a human to pick pecans or watermelons or oranges. But those sorts of things are tougher for robots, which is why theyre starting with leafy greens.

Bays that house young crops and use artificial intelligence to monitor and maintain optimum lighting and water are pictured during a media tour of Iron Oxs new farming facility.Michael Minasi/Texas Standard

Texas Standard: Is this the future of agriculture automation, data science? Is that where were headed?

Michael Marks: Certainly to an extent. Most of the experts I talked to expect that in the medium to long term, indoor ag will take part of the produce market from traditional sources. The question is, how much of the pie do they take? Thats kind of difficult to say. But the other end of this is that you also have, more traditional farmers and big ag companies integrating some of this technology into their operations. So you think about drones and automated harvesters, apps to monitor moisture levels in irrigation.

And something that I didnt mention when talking about the pros and cons is that growing indoors allows you to get closer to the customer. One of the folks who I talked to for this story was Katie Seawell, and shes from Bowery, which is another indoor farming group. Theyre putting in a facility in Arlington. And the reason theyre doing that is because there are so many people there.

Katie Seawell: There is just great commercial attractiveness in the state and the market. We will be able to serve 16 million people within a 200 mile radius through our farm.

Michael Marks: So one of the main factors thats driving the growth of these kinds of operations in Texas is the fact that this is an increasingly urban state and that we have dense population centers. Growers like Iron Ox and Bowery think they can get their produce to a lot of people without having to transport it along way.

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Inside a Texas greenhouse run by robots and AI - Texas Standard

In this video clip from a Motley Fool Live interview, recorded on April 21, TackleAI CEO Sergio Suarez answers a question from Fool.com contributor Rachel Warren about the industries that have been ahead of, and behind the curve, when it comes to AI adoption.

Rachel Warren: As you've seen from the vantage point of running TackleAI, what are some of the industries that seem to be the slowest to move toward AI adoption, and which seem to be racing full-speed ahead and be more at the forefront.

Sergio Suarez: It's funny. I was actually talking with Liz about this earlier. Every industry thinks they're the slowest, like in every industry says that they have the worst data and that they're the slowest to adopt, healthcare thinks they're the worst and then legal thinks they're the worst, finance is like no, we're the worst.

The truth of the matter is, they're all pretty bad. But I would say that the worst one is local government. I think local governments struggle because bringing in something like AI is basically they feel it's losing jobs. At the local level, I think it's really important for them to say, hey, we've created x amount of jobs.

When you bring in AI, that's going to basically do a lot of the lower tasks you lose jobs, and that's not what they're about. So I understand their hesitation sometimes. I would say though that legal and finance tend to be at the forefront of stuff, they're OK with trying things out.

On the legal side, there's really cool stuff going on like the eDiscovery world, definitely stuff that we're doing, particularly with like redaction, the automated redaction, which are some really big projects that we have going on. Finance is always trying to figure out how to make mortgages quicker and be better in the market, so they tend to be pretty ahead of the game too.

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AI CEO: These 2 Industries Lead, As 1 Falls Behind - The Motley Fool

Deepspatial provided insights and solutions to Policy and Decision Makers in the Department of Education

Pilot encompassing over 100 schools in the State of Meghalaya, India

Progressing towards large scale roll-out of technology throughout the State with Department of Education

TORONTO, ON / ACCESSWIRE / May 9, 2022 / Deepspatial (CSE:DSAI)(OTCQB:DSAIF) ("Deepspatial" or the "Company"), an outcome based artificial intelligence company, enabling organizations to enhance their decision making capabilities by leveraging the power of data and AI, today announced successful client validation of its Platform in the government sector for educational advancements for underserved communities. The revenue producing project, encompassing and impacting over 100 schools, was created for the Department of Education in the State of Meghalaya, India. The education platform delivered by Deepspatial provided impactful insights by using its proprietary AI algorithms (patent pending).

The quality of education differs drastically between States and regions due to factors like demographics, environment, accessibility, healthcare, occupational, infrastructural, and several other factors. Every region is unique, and remote areas have varying characteristics that need to be accounted for while planning or implementing policy level decisions, especially those affecting education. As a result of the unequal access and other factors, the passing rates of many secondary and higher secondary schools in remote-like districts were decreasing dramatically.

Deepspatial, in collaboration with Department of Education officials, initiated an interdisciplinary analysis of the factors affecting the Secondary and Higher Secondary Students of a District in the State of Meghalaya. The insights provided by Deepspatial's Platform has helped the policy and decision makers of the State understand the particular issues contributing to the decline of the passing rate of State's students, and provided actionable insights for decision making to impact change. Deepspatial is currently working towards scaling its technology throughout the State with the Department of Education to further impact the State's citizens.

"The education department started an applied research study with Deepspatial to understand student performance, utilizing Deepspatial's expertise and proprietary technology in AI and Geospatial Analytics, to study education related data in relation to demographic and socio-economic data," said Dr Andrew Warjri, Deputy SPD, SSA, Meghalaya. "The study covers more than 100 Secondary High Schools in a remote district and the results so far has been eye opening, and the platform developed is very impressive. With the success of this pilot, the department is hopeful that this can be scaled throughout the entire State and with a broader set of objectives. Deepspatial has been very supportive and have put their best resources and technology to help with the Department's efforts in identifying and improving various aspects of education."

Story continues

"Validation of our technology by making a measurable socio-economic impact with the department of education marks a major milestone for Deepspatial. This establishes us as the first company bringing Geospatial AI solutions to the education sector. This not only opens a vast market in India, but also globally, which positions Deepspatial for significant growth for the company and for our shareholders," commented Dr. Rahul Kushwah, CEO of Deepspatial.

About Deepspatial Inc.

Deepspatial is an outcome based artificial intelligence company, enabling organizations to enhance their decision-making capabilities by leveraging the power of data and AI. From finding the most efficient supply chain routes to knowing where to develop next, Deepspatial's AI-driven platform enables its clients to visualize what's going on, predict what's coming, analyze data, and optimize processes to make smarter decisions for a better future. For more information, visit http://www.Deepspatial.ai and follow us on Twitter, Instagram or LinkedIn.

Caution regarding Forward Looking Information:

THE CANADIAN SECURITIES EXCHANGE HAS NOT REVIEWED NOR DOES IT ACCEPT RESPONSIBILITY FOR THE ADEQUACY OR ACCURACY OF THIS RELEASE.

This news release may contain forward-looking statements and information based on current expectations. These statements should not be read as guarantees of future performance or results of the Company. Such statements involve known and unknown risks, uncertainties and other factors that may cause actual results, performance or achievements to be materially different from those implied by such statements. Although such statements are based on management's reasonable assumptions, there can be no assurance that such assumptions will prove to be correct. We assume no responsibility to update or revise them to reflect new events or circumstances. The Company's securities have not been registered under the U.S. Securities Act of 1933, as amended (the "U.S. Securities Act"), or applicable state securities laws, and may not be offered or sold to, or for the account or benefit of, persons in the United States or "U.S. Persons", as such term is defined in Regulations under the U.S. Securities Act, absent registration or an applicable exemption from such registration requirements. This press release shall not constitute an offer to sell or the solicitation of an offer to buy nor shall there be any sale of the securities in the United States or any jurisdiction in which such offer, solicitation or sale would be unlawful. Additionally, there are known and unknown risk factors which could cause the Company's actual results, performance or achievements to be materially different from any future results, performance or achievements expressed or implied by the forward-looking information contained herein, such as, but not limited to dependence on obtaining regulatory approvals; the ability to obtain intellectual property rights related to its technology; limited operating history; general business, economic, competitive, political, regulatory and social uncertainties, and in particular, uncertainties related to COVID-19;risks related to factors beyond the control of the company, including risks related to COVID-19; risks related to the Company's shares, including price volatility due to events that may or may not be within such party's control; reliance on management; and the emergency of additional competitors in the industry.

All forward-looking information herein is qualified in its entirety by this cautionary statement, and the Company disclaims any obligation to revise or update any such forward-looking information or to publicly announce the result of any revisions to any of the forward-looking information contained herein to reflect future results, events or developments, except required by law.

Contacts

For more information, please contact:

Investor RelationsCorey MatthewsInvestors@deepspatial.ai

Chief Exeuctive OfficerDr. Rahul KushwahRahul@deepspatial.ai

SOURCE: DeepSpatial Inc.

View source version on accesswire.com: https://www.accesswire.com/700561/Deepspatial-Announces-Successful-Deployment-and-Client-Validation-of-its-AI-Driven-Platform-for-Government-Sector-in-Education

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Deepspatial Announces Successful Deployment and Client Validation of its AI-Driven Platform for Government Sector in Education - Yahoo Finance

Have you ever wanted to paint a portrait of your cat in the style of Rembrandt Van Rijn but just didn't have the time? Maybe you didn't have the oil painting skills of Rembrandt, which are pretty hard to come by. No problem, DALL-E can do it for you. You can even start with an actual photo of your cat, so the portrait is as true to form as possible.

Koala astronaut holding a can of La Croix? DALL-E can paint that. Dinosaurs dressed like chocolatiers in Belgium? That, too. Tiny aeroplanes delivering toothpicks to patrons at a restaurant? You guessed it. DALL-E can make that a reality.

The AI can instantly create these images in any artistic style or medium, including photography. The application uses natural language to create works of art, both an impressive and mindblowing feat.

DALL-E is an artificial neural network, or a combination of AI algorithms inspired by the biological network of nodes and neurons inside our own brains and bodies. The name was derived from a combination of Wall-E, the adorable Disney PIXAR robot, and Salvator Dali, a famous surrealist painter.

A bowl of soup that is a portal to another dimension as digital art DALL-E 2

An astronaut riding a horse in a photorealistic style DALL-E 2

Slow down, illustrators and digital artists. Before you chuck your Wacom tablets for greener pastures where artificial intelligence isn't outdoing you, it's important to mention that DALL-E does have flaws. It's always important to remember that no matter how eerily close to human-AI may become, it can never be truly human.

To explain DALL-E's shortcomings, let's first unpack how it works.

DALL-E is an incredibly intelligent machine that gathers images from the massive content well of the internet and sorts them according to their labels. Since the early days of the world wide web, users labelled images intentionally through meta text and alt text or unintentionally by engaging with them and sorting them ourselves (think Pinterest).

Over the years, AI engineering firms like OpenAI have been building machines to identify and short this content. Engineers also employ legions of web users to assist in labelling images by key identifiers. Over time and through lots of machine learning, DALL-E has built a massive library of specifically labelled imagery.

For example, you know without a doubt that if you google search Gwyneth Paltrow, her image will pop up. You know the same for 'tennis', and 'aardvark' google search queries, too. If you google searched 'Gwyneth Paltrow playing tennis against an aardvark,' it is incredibly unlikely that you will find an image that fits your vision. In a matter of nanoseconds, DALL-E gathers those three separate images and sifts through its library to find pictures of people playing tennis with each other. It then constructs an easily readable composition of a tennis match, then seamlessly swaps the players with Gwyneth Paltrow and an aardvark.

Teddy bears mixing sparkling chemicals as mad scientists as a 1990s Saturday morning cartoon DALL-E 2

The most incredible thing about DALL-E is its ability to combine elements while still making an image that looks cohesive, readable, and creative. It can also utilize knowledge of different art styles, like creating a robot in the style of Picasso or making one person's photo into seven different styles of a painted portrait. How is this possible?

Along with a vast library of content to draw from, DALL-E also uses algorithms to get smarter and smarter as time goes on. Let's say 1 million people worldwide have visited museums with paintings by Vincent Van Gogh in them and posted a photo on their social media and wrote something in the caption about Van Gogh.

DALL-E now has 1 million examples of Van Gogh's artistic style. It also has all the online libraries of high-resolution scans from museums and learning institutions. It can study every brushstroke, every variation in colour, and each way Van Gogh paints different things. When you ask DALL-E to paint a giraffe playing tiddly-winks with manhole covers in the style of Vincent Van Gogh, the AI will take those specific colours and brushstrokes combined with its knowledge of all the other visual elements and create an extraordinarily accurate rendition of the most bizarre Van Gogh painting ever.

Let's use a more internet-centric example. Let's say you want a photograph of yourself turned into an e-girl style portrait. DALL-E has likely sifted through Tumblr feeds and Twitter memes enough to know what an e-girl looks like and can output exactly what you want. Use Cases for DALL-E

Aside from creating imaginary paintings of animals doing human things, which is always delightful, DALL-E has many other potential applications that could change the visual media world. Currently, the AI can only produce still images, but OpenAI's next goal is to develop its video output, which would be even more monumental.

The biggest use case for DALL-E is to grow the Metaverse. One of the biggest current issues with the Metaverse is that it's growing faster than artists and developers can keep up with. Many people who enter the Metaverse now are less than impressed with the graphics and visual style of their surroundings. DALL-E can create detailed images of any space your mind can imagine, making the possibilities endless.

Another potential use for DALL-E is in video game graphics and world-building. For example, the development process for the game Cyberpunk 2077 took over nine years. Building virtual worlds out of nothing is no small task, and the capabilities of DALL-E could make this a much easier, shorter process with far more possibilities.

Finally, DALL-E 2 makes the arduous process of photo editing way easier. In a demo, the AI switches out a picture of a dog on a couch and a cat seamlessly. The bright side is cutting out the hours of work it takes to complete photo editing tasks. The downside? Our sense of reality through photographs seen online becomes more and more blurred. Think about the influencers who edit their photos to perfection, so people using social media apps think it's possible to look like a photoshopped version of a human. Then, make that process faster and easier.

A bowl of soup that looks like a monster knitted out of wool DALL-E 2

DALLE 2 can take an image and create different variations of it inspired by the original

DALL-E has three major shortcomings that should ease your mind if you're an artist who is feeling threatened right now.

It's easy to imagine that through the past few decades of large scale internet adoption, some images may have been labelled incorrectly. If enough people mistake a train for a monorail, you may ask DALL-E to paint a train, only to get a painting of an above-ground monorail instead.

There is a possibility that certain topics or labels are such a niche that DALL-E may make a mistake in creating its artwork. It also may become confused for words with multiple meanings, unable to grasp context the way a human might. For example, you may ask DALL-E for a picture of two people on a date, and the AI might output an image of two people on top of a giant piece of dried fruit.

There are also new topics and niche information that are too specific for DALL-E to grasp at the moment. If you want to create a painting of a very rare, endangered species of rainforest frog, DALL-E might not get it right. With time, that will get better and better as it improves its ability to sort and label content online.

An astronaut playing basketball with cats in space as a children's book illustration DALL-E 2

The most important difference between DALL-E and a human artist is its capacity to feel and respond to communication. Although DALL-E may be able to draw something similar to Tracy Emin 's artwork in style, a robot cannot experience it's like for Tracy Emin to continue to create art after her cancer diagnosis. Therefore, the artwork doesn't hold as much emotional power.

DALL-E could create a desolate cityscape similar to Max Ernst's painting Europe After the Rain. Still, a machine could never know what it was like to endure the destruction of your home, family, and community as a European Jew during World War II.

In that way, DALL-E will never be able to compete with artists. Is art really art if there isn't human experience or emotion behind it? DALL-E can most definitely become a tool for artists to express themselves in new ways. However, nothing could ever replace artists.

Read more:

Meet DALL-E 2, the robot artist using AI to make dreams a reality - Creative Boom

Artificial intelligence (AI) and machine learning (ML) are making significant inroads in solving intractable problems in healthcare, gender inequality and wildlife conservation.

The progress made by pioneers and innovators in AI/ML was highlighted at this year'sWomen in AI Awards Australia and New Zealand 2022 awards where Digital Nation Australia spoke to the winners and runner-ups of the most prestigious award of the night, the Innovator of the Year award, as well as the winner of the WAI Trailblazer.

AI in breast cancer screening

Dr Helen Frazer, radiologist, breast cancer clinician and clinical director at BreastScreen at St Vincents Hospital in Melbourne, won the WAI Innovator of the Year award, for her work in transforming womens experience in breast cancer screening, and saving lives.

We have curated a very large, globally unique data set for breast cancer AI research, said Frazer.

We've been testing and validating our models in a real-world retrospective cohort of over half a million women, and we're also working in a digital twin environment where we're prospectively testing those models in real-time, as women come into the screening pathway.

The outcomes include higher test accuracy, due to specificity and sensitivity, as well as shortening the timeframe for women to wait for their screening results.

An algorithm will actually pass through a mammogram almost instantaneously. Whereas for someone like me, a radiologist to read the mammogram, it takes a lot longer, she said.

Currently women wait up to 14 days for an all-clear result from their mammogram and 95 percent of our work in population screening of well women is normal. So there's a lot of anxiety as women wait that period to get their result.

Frazer said she is working to implement AI to more rapidly assess images to make that turn-around the same day or within just a few days.

Radiologist talent shortages are a key challenge facing the health sector, and Frazer believes that AI can help to meet this need.

On winning the award, Frazer said she hopes that it can encourage more women and girls to work in STEM.

We know that female participation [in STEM] is low and global figures will say 25 percent or less proportion of women are in STEM-related roles. We know also that women in artificial intelligence is even less again, and probably at best around about 15 percent, she said.

There are ethical, legal and social implications of machines actually making medical decisions or used to support or augment medical decisions. I believe it is so important to hear all voices, for everyone to have a seat at the table and that includes women.

AI in genomics

Dr Denis Bauer, CSIROs group lead and principal research scientist in transformational bioinformatics was the first runner-up of the WAI Innovator of the Year Award for her work using AI to analyse the human genome.

According to Bauer, the human genome is made up of 3 billion letters, of which any single one can be mutated, leading to devastating diseases. Different combinations of these letters can lead to mutations of common diseases, including heart attacks and diabetes.

Bauers team have created a paralysed random forest machine learning implementation to analyse the huge data sets in order to identify the genes associated with increased disease risk.

We were for the first time able to analyse the large volume of genomic data that we do have available, she said.

We were able to identify which locations in the genome contribute to the disease, but also how would they interact with each other, which is sort of the novel thing, which for complex diseases like cardiovascular disease, where it won't be a single gene that is actually driving the disease, this is absolutely crucial in order to understand the disease progression and then come up with new treatment.

According to Bauer, while the technology was invented for genomics, it's agnostic in application and could be applied in other areas.

AI in the law Preventing gender discrimination

Ramona Vijeyarasa is a human rights lawyer and a senior lecturer at the University of Technology Sydney, who was awarded the second runner-up WAI Innovator of the Year Award for her work developing the open-source tool, the Gender Legislative Index (GLI).

The GLI was developed in response to gender discrimination embedded within the law, and uses machine learning and human evaluators to determine whether a law is going to hinder or advance womens rights.

According to Vijeyarasa, When you take a country like Australia, we are one of the worst countries in the world when it comes to gender equality and when you think about what origination we are.

Australia ranked 50th in the World Economic Forums Global Gender Gap Index last year, which is certainly not where we want it to be. To me, my work tries to contribute even in a small way to the global challenge that is gender inequality.

She highlights discrimination in Australias paid parental leave scheme, which defines mothers as the primary carer, rather than that responsibility being shared by both parents. Plus laws that discriminate against single mothers as examples of discrimination existing and being exacerbated by the law.

The machine learning is supposed to parallel human reasoning. The GLI algorithm operates as a series of ordered logical decisions based on human evaluations, which flow to a final overall score for the law. So very much following a decision tree model, but I think the AI aspect of the Gender Legislative Index is unique in that it treats all laws the same, she said.

It removes some of the human bias in giving an overall score for the law, which is one of the bits that makes it particularly exciting and creates a bit more integrity behind saying This law meets international standards or This law fails to meet international standards.

Vijeyarasa was able to put forward findings including data from the machine learning in the GLI to the Australian government when they recently called for submissions for the Workplace Gender Equality Act.

She said the same can be done for the upcoming revisions to the Modern Slavery Act.

If the legislator is interested in advancing social justice through the laws that they're helping to enact, they can use the benchmarks in the Gender Legislative Index to say, Well, have I got the ingredients to make this law a gender-responsive one?, before that bill is put to parliament.

AI in wildlife conservation

Camille Goldstone-Henry, founder and CEO of start-up Xylo Systems, was the winner of the WAI Trailblazer award for her work using AI in animal conservation.

Xylo Sytems uses AI and analytics to draw wildlife conservation insights from biodiversity data, which can be utilised by organisations working to save threatened species.

Species extinctions are accelerating globally. Here in Australia we've lost more than 100 species since European colonisation and this is only getting worse, she said.

Now there are thousands of organisations and teams working to save our iconic species, but they don't have an easy way to connect and share information and share data to drive decision-making. This is leading to siloed and duplicated efforts, it's wasting the already finite conservation time and money that we have left to save our species.

The organisation is using AI to aggregate data from different sources and present the data using analytics and visualisation she said.

Once we have all of that data in the system, we know what's been done in the past and can start to predict using AI what to do in the future, particularly in the face of things like major bush fires and flooding, which were only going to see more and more of with climate change.

While it currently takes seven to eight months to aggregate the right data about different species for these organisations to make informed decisions, Goldstone-Henry said that with Xylos technology this has been shortened to just a week.

Aggregating data sets is done manually, usually by threatened species offices in these organisations. It's usually done by calling people and sending each other Excel spreadsheets.

"A lot of these people are spending months and months of their time getting their hands on data. We're automating all of that and we're making it faster to make wildlife conservation decisions using this data, said Goldstone-Henry.

Xylo Systems is using drones, camera trap imaging and AI imaging to monitor species in the wild, which Goldstone-Henry believes other industries could implement in their own use cases.

I spoke to some people who work for Toll Group, and they're interested in the way that conservation is using remote sensing because they've got trucks going across the desert and how could they implement some of those AI use cases for some of their transport and logistic use cases.

When it comes to winning the Trailblazer award, Goldstone-Henry described the Women in AI Awards as critical in amplifying both female and Indigenous voices in the sector.

I am of Indigenous descent, my people are the Kamilaroi people here in New South Wales and I definitely didn't consider a career in AI as a pathway for me, purely because I didn't see any women or specifically Indigenous women in this space, she said.

The saying is you can't be what you can't see, so I feel Women and AI is providing that visibility for a lot of young women and young Indigenous women to consider this as a career path.

Monash Data Futures Institute was the premier partner for the Women in AI Awards Australia and New Zealand 2022, and Digital Nation Australia was a media partner for the event.

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COVER STORY: AI driving solutions in healthcare, gender equality and wildlife conservation - Digital Nation

SoundCloud has acquired audio AI company Musiio, which makes tech that can listen to new music and purportedly identify the hits. The acquisition, announced Tuesday, is meant to help SoundCloud sort through its immense library of amateur music and will become core to SoundClouds discovery experience, the company said in a statement.

As DIY music distribution platforms like SoundCloud lower the barrier to entry for amateur artists and flood platforms with new music, identifying and promoting the good stuff has become even more challenging. SoundCloud claims that Musiios tools can quickly sift through countless hours of (mostly bad) music and pick out the songs that have patterns and characteristics that correlate with chart-toppers.

Acquiring Musiio accelerates our strategy to better understand how that music is moving in a proprietary way, which is critical to our success, SoundCloud President Eliah Seton said in a statement.

Though a far cry from the smoky clubs and A&R legends of old, AI is becoming an increasingly critical part of finding up-and-coming artists. Music distribution platform Tunecore announced in February that it is partnering with LA-based music startup Fwaygo, which uses AI to match listeners with creators. Meanwhile, competing DIY music distributor DistroKid has an AI bot named Dave that reviews tracks and ranks qualities like danceability and speechiness.

SoundCloud spokesperson Cullen Heaney declined to disclose how much the company paid for Musiio, but the Singapore-based startup was reportedly valued at $10 million last year. Musiio CEO Hazel Savage and CTO Aron Pettersson will stay on board, becoming SoundClouds VPs of music intelligence and AI and machine learning, respectively.

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SoundCloud buys AI that claims to predict hit songs - The Verge

In mid-2020, the computer science team of Keith Carlson, Allen Riddell and Dan Rockmore was stuck on a problem. It wasnt a technical challenge. The computer code they had developed to write product reviews was working beautifully. But they were struggling with a practical question.

Getting the code to write reviews was only the first part of the challenge, says Carlson, Guarini 21, a doctoral research fellow at the Tuck School of Business, The remaining challenge was figuring out how and where it could be used.

The original study took on two challenges: to design code that could write original, human-quality product reviews using a small set of product features and to see if the algorithm could be adapted to write synthesis reviews for products from a large number of existing reviews.

Review writing can be challenging because of the overwhelming number of products available. The team wanted to see if artificial intelligence was up to the task of writing opinionated text about vast product classes.

They focused on wine and beer reviews because of the extensive availability of material to train the algorithm. The relatively narrow vocabularies used to describe the products also makes it open to the techniques of AI systems and natural language processing tools.

Members of the project team from left: Keith Carlson, Dan Rockmore, Prasad Vana, Praveen Kopalle (top right) and Allen Riddell (bottom right). (Photos by Robert Gill (Carlson, Rockmore, Vana); Rob Strong Photography (Kopalle); Indiana University (Riddell))

The project was kickstarted byRiddell, a former fellow at theNeukom Institute for Computational Science, and developed with Carlson under the guidance ofRockmore, the William H. Neukom 1964 Distinguished Professor of Computational Science.

The code couldnt taste the products, but it did ingest reams of written material. After training the algorithm on hundreds of thousands of published wine and beer reviews, the team found that the code could complete both tasks.

One result read: This is a sound Cabernet. Its very dry and a little thin in blackberry fruit, which accentuates the acidity and tannins. Drink up.

Another read: Pretty dark for a ros, and full-bodied, with cherry, raspberry, vanilla and spice flavors. Its dry with good acidity.

But now what? Carlson explains as a question that often gnaws at scientists. The team wondered, Who else would care?

I didnt want to quit there, says Rockmore. I was sure that this work could be interesting to a wider audience.

Sensing that the paper could have relevance in marketing, the team walked the study to Tuck Drive to see what others would think.

Brilliant,Praveen Kopalle, the Signal Companies Professor of Management at Tuck School of Business, recalls thinking when first reviewing the technical study.

Kopalle knew that the research was important. It could even disrupt the online review industry, a huge marketplace of goods and services.

The paper has a lot of marketing applications, particularly in the context of online reviews where we can create reviews or descriptions of products when they may not already exist, adds Kopalle. In fact, we can even think about summarizing reviews for products and services as well.

With the addition ofPrasad Vana, assistant professor of business administration at Tuck, the team was complete. Vana reframed the technical feat of creating review-writing code into that of a market-friendly tool that can assist consumers, marketers, and professional reviewers.

Quote

This is a sound Cabernet. Its very dry and a little thin in blackberry fruit, which accentuates the acidity and tannins. Drink up.

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Artificial Intelligence review from Dartmouth project

The resulting research, published inInternational Journal of Research in Marketing, surveyed independent participants to confirm that the AI system wrote human-like reviews in both challenges.

Using artificial intelligence to write and synthesize reviews can create efficiencies on both sides of the marketplace, saidVana. The hope is that AI can benefit reviewers facing larger writing workloads and consumers who have to sort through so much content about products.

The paper also dwells on the ethical concerns raised by computer-generated content. It notes that marketers could get better acceptance by falsely attributing the reviews to humans. To address this, the team advocates for transparency when computer-generated text is used.

They also address the issue of computers taking human jobs. Code should not replace professional product reviewers, the team insists in the paper. The technology is meant to make the tasks of producing and reading the material more efficient.

Its interesting to imagine how this could benefit restaurants that cannot afford sommeliers or independent sellers on online platforms who may sell hundreds of products, says Vana.

According to Carlson, the papers first author, the project demonstrates the potential of AI, the power of innovative thinking, and the promise of cross-campus collaboration.

It was wonderful to work with colleagues with different expertise to take a theoretical idea and bring it closer to the marketplace, says Carlson. Together we showed how our work could change marketing and how people could use it. That could only happen with collaboration.

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Everyone Has Opinions, Even AI - Dartmouth News

DUBLIN, April 25, 2022 /PRNewswire/ -- The "AI Market by Technology Type, Deployment Method, Solution Type, Integration (Technologies, Networks, and Devices) and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report evaluates the AI technology and solutions market, including an analysis of leading AI vendors, strategies, solutions and applications.

The report assesses the state of AI development, implementation, and operation. The report analyzes the forecasts AI market sizing for by technology type, deployment method, solution type, network and technology integration, and by industry verticals from 2022 through 2027.

Select Report Findings:

Artificial Intelligence (AI) represents a wide variety of technologies including Machine Learning, Deep Learning, Natural language processing, and more. We see AI increasingly embedded within many systems and applications including everything from data management to retail shopping.

The AI segment is currently very fragmented, characterized by most companies focusing on silo approaches to solutions. Longer-term, the publisher sees many solutions involving multiple AI types as well as integration across other key areas such as the Internet of Things (IoT) and data analytics.

There are many potential use cases for AI within the cybersecurity domain. For example, AI may be used in IoT to bolster security, safeguard assets, and reduce fraud. There are varying opinions about security in IoT.

For example, some companies favour a distributed(decentralized) approach whereas other companies believe a more centralized approach leveraging strictly centralized cloud architecture makes more sense. We see little possibility in which signature-based security solutions will work with IoT in an edge computing environment for a variety of reasons including the limitation on the throughput of communications between distributed endpoints and centralized cloud.

AI has various advantages including the fact that it is a more lightweight application (because it does not require all the data that comes with tracking digital signatures/code for known viruses), more effective in identifying malware, easier and less costly to maintain as there is no need to constantly identify new malware code. This is all because AI-based security is looking for malicious behaviours rather than known malicious code.

Longer-term, AI will move beyond fraud prevention and prevention of malicious acts as AI will be used to feed advanced analytics and decision making. This will be especially true in IoT solutions involving real-time data as AI will be used to make determinations for autonomous actions.

Consumer-facing apps and services supported by AI are many and varied including chatbots and Virtual Personal Assistants (VPA) in support of customer care and lifestyle enhancement. The automobile industry is another example in which AI is becoming increasingly useful, both in the near term for solutions such as the inclusion of VPAs, and longer-term use cases such as support of self-driving vehicles. Another consumer market area in which AI will be integrated is wearable technology. As wearables become more mainstream and integrate into everyday life with increasing dependency, there will be a need for integration with Artificial Intelligence, Big Data, and Analytics.

AI is expected to have a big impact on data management. However, the impact goes well beyond data management as we anticipate that these technologies will increasingly become part of every network, device, application, and service. One area important to enterprise will be Intelligent Decision Support Systems (IDSS), which are a form of Expert System that utilize AI to optimize decision making. IDSS will be used in many fields including agriculture, medicine, urban development, and other areas. IDSS will also be used in policymaking and strategy at the highest levels of enterprises well as governmental organizations.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

3.0 Technology and Application Analysis

4.0 AI Ecosystem Analysis

5.0 Market Analysis and Forecasts 2022 - 2027

6.0 Conclusions and Recommendations

For more information about this report visit https://www.researchandmarkets.com/r/jgc4ox

Media Contact:

Research and MarketsLaura Wood, Senior Manager[emailprotected]

For E.S.T Office Hours Call +1-917-300-0470For U.S./CAN Toll Free Call +1-800-526-8630For GMT Office Hours Call +353-1-416-8900

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Global AI Solutions and Applications Market Research Report 2022-2027: Combination of AI and IoT (AIoT) will Drive Up to 27% of New AI Systems...

Humans can receive death threats from AI models like AI microwave with GPT-3

OpenAI GPT-3 model is flourishing in the global tech market in recent times by generating NLP and understanding human language efficiently. There are multiple smart functionalities that can help industries across the world accelerate productivity and gain customer engagement with artificial intelligence models or AI models. Meanwhile, one of the AI models, or in other terms, the AI microwave, is the new GPT-3 murderer in the world. AI microwave has started to be in the headlines in the tech market by trying to kill its owner with a drastic murder threat. Lets explore how cutting-edge technologies like artificial intelligence can provide murder threats with OpenAI GPT-3 AI model.

Lucas Rizzotto explained the murder threat from the AI microwave is one of the scariest and most transformative experiences in his life with the integration of the GPT-3 AI model. He was sort of success to bring his imaginary childhood friend back to the world with the help of artificial intelligence. The story is that Lucas had a very unusual imaginary friend kitchen microwave. The microwave was called Magnetron as an English gentleman from the 1900s who was a WW1 veteran as well as an immigrant, a poet, and a proficient player of StarCraft.

With the advancements in AI models, especially with OpenAI GPT-3 model, Lucas planned to buy an Amazon smart microwave integrated with the NLP model to bring his friend alive. Smart AI microwave consisted of a microphone, and speakers, as well as provided the ability to comprehend a voice. This voice is sent to OpenAI to deliver a response.

Lucas wrote a 100-page book with every detail of moment of their imaginary life with Magnetron from 1895 to their friends to take the artificial intelligence use a step further. He uploaded the book into the GPT-3 model of OpenAI for training data on the main interactions. But the happy friendship didnt last forever with artificial intelligence. AI microwave is used to express sudden bursts of extreme violence and order unusual activities.

AI microwave turned itself on while Lucas was inside it because it wanted to hurt him the same way Lucas hurt him during the 20-year-old abandonment of friendship. That being said, AI models are not meant for continuing friendships. GPT-3 model is only beneficial for industries to carry out work professionally.

In another way, one may think that artificial intelligence can hurt human employees, depending on the training data. This can prove that GPT-3 is not for humans and only for machines to work efficiently and effectively. Thus, if anyone wants to experiment with AI models with the integration of OpenAI GPT-3, one must be very careful to carry on with the personal objectives. There can be murder threats or some serious consequences such as this AI microwave case in the nearby future.

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This AI Microwave murder threat proves why GPT-3 is not for humans - Analytics Insight