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How Artificial Intelligence is Contributing to Casino Gaming Success in Finland – Hardware Times

Artificial Intelligence (AI) is gaining massive adoption in Finland and other countries, thanks to the penetrative reach of technology in major sectors, including sports and gaming. Artificial Intelligence is beginning to play an important role and helping to find more accurate results.

Casino gaming especially is witnessing a lot of artificial intelligence adoption. Casinos and technology have always shared an interesting relationship. From land-based to online casinos, technology continues to play critical roles in forming a direction for casino gaming, with artificial intelligence being one of the most commonly applied technology initiatives.

Many casino Finnish players crave safer gaming environments with better success chances. This has been elusive for many years, but with artificial intelligence applied to casino gaming, a secure gaming environment is gradually becoming a reality. Most top casinos are adopting artificial intelligence to stand out among their competitors. Many people do not know the specific areas where artificial intelligence is being applied to casino gaming.

Information about the application of artificial intelligence to casino gaming is usually treated as a top secret in Finland and other countries. Casinos already utilising artificial intelligence can tell the huge difference it makes. Therefore, they are keeping information about artificial intelligence tightly to themselves and using it as an edge over their competitors.

Casino gaming utilises technology solutions that use multi-layered algorithms. These algorithms are complex, but they also make casino gaming a data gold mine. Unfortunately, casino gaming data is not usually utilised. The application of artificial intelligence to casino gaming helps to address these problems with data. With AI, casino gaming data can be used to improve casino gaming in many ways. They include:

AI has been beneficial in helping casino gaming operators to reduce the occurrence of cheating to the barest minimum. Online casinos face many threats from players who want to outsmart the system to claim bogus winnings. With AI, their chances of success are greatly reduced. AI will typically feed on players data and observe if theres been a spooky pattern in their activities. AI can easily check and take actions on such player profiles. Without AI, it is almost impossible to detect game manipulation, especially among online casino players.

Player security is important in casino gaming. Without a guarantee of player security, casino gamers will no longer trust the gaming environment. AI contributes to the increased security of the casino gaming environment in many ways. AI guarantees the security of player information through different encryption modes that can also be applied to protect player payments (deposits and withdrawals inclusive).

Casino gamers usually have different interests, and navigating through the many options available to find their interests per time may be very tiring. With AI, casino gaming operators can note players gaming preferences. This way, they can provide options that match the players interests according to their observed AI collated data.

Artificial Intelligence is helping to address myriad casino gaming challenges. Issues of providing personalized customer experiences for Finnish players and addressing challenging problems such as gambling addiction that proved difficult in the past are now being resolved, thanks to the applications of AI in casino gaming.

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How Artificial Intelligence is Contributing to Casino Gaming Success in Finland - Hardware Times

10 tracks that harness the power of artificial intelligence – MusicRadar

Despite the numerous AI platforms which serve up routes to auto-generate functional music, many artists who have overtly worked with AI have approached the concept via more individual means.

Take Holly Herndon, the Berlin-based composer and musicologist who recently created her own intelligent musical accomplice. Dubbed Spawn, this vocal-sample generator was taught by Herndon and partner Mat Dryhurst to reproduce a bank of vocal-types (including her own) via months of training its complex neural network. Spawn was able to organically add vocals to tracks presented to it.

Though, as Herndon told Art in America, the process is still finding its feet: AI is not that smart, its very low fidelity, its not real time, its very slow and unwieldy. Spawn can take more than 24 hours to process someones vocal input. On the other hand, it has some unique capabilities that are pretty exciting-slash-scary. The AI can extract the logic of something outside its operators own logic and re-create it. This is entirely new for computer music.

Herndons approach to upturn the often predictable creative choices of the human musician, and hack out new musical avenues of exploration is a commonality shared across numerous artists who have worked with AI.Alex Da Kids collaboration with IBMs Watson was triggered by the Grammy-winning producers interest in whether it was genuinely possible to make a song with a virtual colleague.

Watson uses an accumulation of data gathered by a web of smart APIs. These include Watson Alchemy Language, which studies five years of media to determine current pop cultural themes, Watson Tone Analyser, which similarly analysed around 26,000 lyrics, and, crucially, Watson Beat, which determines the best chords, keys and frameworks to correspond with a certain theme. With the track Not Easy, the pair explored the theme of heartbreak and produced a stunning statement, that also saw interjections from Wiz Khalifa, Elle King and the X Ambassadors. The brilliant end result was popular enough to top both the iTunes and Spotify charts.

While Alex and Hollys involvement with AI was driven by a desire to research the potential of AI, YouTube star Taryn Southerns stunning I Am AI LP was conceived when the singer/songwriter was finding it difficult to realise the musical ideas she had in her head. Using a combination of AI-music generators Amper Music and Aiva as well as Googles Magenta and IBM Watson, Taryn created a new musical toolkit with which to work. As Southern told Digital Trends, her approach was to use the fine-tuning tools of software like Amper to deviate from her original ideas, download the stems and then rearrange the end results in her DAW.

These artists, each venturing further down the AI rabbit hole, albeit differently, are at the vanguard of a new paradigm for creators. Its almost a question of when as opposed to if AI will cease to be regarded as an industry buzzword and become an everyday facet of music creation. Via their work, these artists are laying the foundations for the fruitful absorption of AI into the creative process.

While these examples only scratch the surface of how AI has been applied by artists in various genres and contexts (see our list of ten AI-built records), in other areas, AI has already fully started informing our daily lives.

Its entirely suffused itself into how we listen to our music. Pervading streaming platforms and music-listening services are algorithms which smartly serve up similar tracks to the ones we regularly play, building cleverly curated playlists based on the data imparted by our listening habits.

Spotifys Discover Weekly smart playlists were designed in collaboration with French AI startup Niland. This smart neural network determines how to best populate these lists by scanning other users playlists that feature these tracks, as well as analysing the waveforms of the tracks to determine musicological similarities. Were working on a number of ways to elevate the experience even further.

Spotifys research lead, Rishabh Mehrotra explained to AI News, Reinforcement learning will be an important focus point as we look into ways to optimise for a lifetime of fulfilling content, rather than optimise for the next stream. In a sense this isnt about giving users what they want right now as opposed to evolving their tastes and looking at their long term trajectories.

Well take the liberty of skirting around the numerous prophecies of doom foretold by those fearing AIs encroachment into their financial territory. Lets instead consider the alternative, that while online AI platforms can easily create the type of utilitarian music which accompanies a simple YouTube video or a podcast, for genuine soundtracking requirements, real human composers will certainly be needed.

Feeling, heart, soul and also the story of the soundtracking process are all essential to modern film, television and video game soundtracks. In production terms too, theres so much that AI can bring, as opposed to take away, from the modern producers workflow. Will AI take away all mixing jobs? Definitely not, says Sonibles CMO Alexander Wankhammer.

Will it change the way the industry is working in some fields? Definitely yes. I think people should not be afraid of the new possibilities AI is creating they should remain curious and see AI as a new, really exciting tool that will (up to a certain extent) shape the future of music production.

While not strictly speaking AI in the modern sense, Bowies use of the Verbasizer software to concoct new lyrical ideas was something of a digital continuation of the randomising cut-up technique hed relied on for earlier works. Many of the tracks on Bowies hidden gem Outside were mapped out of these computer-suggested ideas.

A collaboration between Dr Dre and Eminem producer Alex Da Kid and IBMs cultural learning and idea-forming digital intelligence, Watson. Told to investigate the theme of heartbreak, Watson provided Alex with a suitable rhythmic, lyrical and chordal foundation. The stunning results also featured Wiz Khalifa, Elle King and X Ambassadors.

Created by Iranian composer Ash Koosha in conjunction with artificial intelligence Yona, Return 0 is a sometimes beautiful, sometimes challenging work that emphasises its innovative conceit in its futuristic arrangements. Capable of generating melodic sequences and even its own lyrics and singing, were very keen to see what Yona does next.

Built in tandem with commercially available AI soundscape designer Endel, American producer and songwriter Toro Y Moi built up a series of four tracks for the purposes of promoting Glaceau Smartwater. The Smartbeats EP contains the tranquil and emotive fruits of his and his virtual assistants labours.

Harnessing Spawn, Herndons self-trained AI vocal generator, Protos melding of the characterful artistic sensibilities of Herndon, and the twisting, rule-bending Spawn result in a captivating listen. Its glitchy, computer-ised textures battles against Hollys creative persona in fascinating ways, ultimately leading to the aural equivalent of the merging of the two.

Examining the space between human composition and AI-driven arrangements, Oakland-based duo The Cotton Modules debut is a remarkable listen. Working with machine learning decision-maker Jukebox, the record swims in ambient, evolving textures and loops. In making the record, the duo came to regard this other sentience as an instrument that they needed to learn how to work with.

Benoit Carr presented Sony CSLs Flow Machines with ideas, which the AI then built out. He took that and made a solid pop record. Rather than a neural network, Flow Machines used Markov chains, probability equation generators that need less data to work.

Perhaps the most high-profile AI-built record on the list. YouTube superstar Taryn Southerns eight-track LP was constructed with music composition fine-tuner Amper. Southerns work received widespread press.

A novel idea, to encourage musicians to speak openly about mental health. The 27 Club used AI to make new tracks, based on the music of those famous, epoch-making musicians who oddly all left us at the age of 27. With real vocalists replacing the late singers, this is AI used for the human act of remembrance.

Similarly to The 27 Club project, this album was created by an AI which studied the typical structure of the rap metal quartets canon and generated short samples, which artists AI Kittens carved into robust guitar workouts.

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The Artificial intelligence/AI in drug discovery Market is projected to reach USD 4.0 billion by 2027 from USD 0.6 billion in 2022, at a CAGR of 45.7%…

New York, June 23, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in Drug Discovery Market by Offering, Technology, Application, End User - Global Forecasts" - https://www.reportlinker.com/p05828730/?utm_source=GNW On the other hand, the inadequate availability of skilled labor is key factor restraining the market growt at certain extent over the forecast period. .

Services segment is estimated to hold the major share in 2022 and also expected to grow at the highest over the forecast periodOn the basis of offering, the AI in drug discovery market is bifurcated into software and services. the services segment expected to account for the largest market share of the global AI in drug discovery services market in 2022, and expected to grow fastest CAGR during the forecast period. The advantages and benefits associated with these services and the strong demand for AI services among end users are the key factors for thegrowth of this segment.

Machine learning technology segment accounted for the largest share of the global AI in drug discovery marketOn the basis of technology, the AI in drug discovery market is segmented into machine learning and other technologies.The machine learning segment accounted for the largest share of the global market in 2021 and expected to grow at the highest CAGR during the forecast period.

High adoption of machine learning technology among CRO, pharmaceutical and biotechnology companies and capability of these technologies to extract insights from data sets, which helps accelerate the drug discovery process are some of the factors supporting the market growth of this segment.

Pharmaceutical & biotechnology companies segment expectd to hold the largest share of the market in 2022On the basis of end user, the AI in drug discovery market is divided into pharmaceutical & biotechnology companies, CROs, and research centers and academic & government institutes.In 2021, the pharmaceutical & biotechnology companies segment accounted for the largest share of the AI in drug discovery market.

On the other hand, research centers and academic & government institutes are expected to witness the highest CAGR during the forecast period. The strong demand for AI-based tools in making the entire drug discovery process more time and cost-efficient is the key growth factor of pharmaceutical and biotechnology end-user segment.

North America to dominate the AI in drug discovery market in 2021The global AI in the drug discovery market is segmented into four major regions, namely, North America, Europe, APAC, and the Rest of the World.In 2021, North America accounted for the largest and the fastest-growing regional market for AI in drug discovery.

North America, which comprises the US, Canada, and Mexico, forms the largest market for AI in drug discovery.These countries have been early adopters of AI technology in drug discovery and development.

Presence of key established players, well-established pharmaceutical and biotechnology industry, and high focus on R&D & substantial investment are some of the major factors responsible for the large share and high growth rate of this market.

Breakdown of supply-side primary interviews, by company type, designation, and region: By Company Type: Tier 1 (31%), Tier 2 (28%), and Tier 3 (41%) By Designation: C-level (31%), Director-level (25%), and Others (44%) By Region: North America (45%), Europe (20%), Asia Pacific (28%), and RoW (7%)

Prominent players in this market are NVIDIA Corporation (US), Microsoft Corporation (US), Google (US), Exscientia (UK), Schrdinger (US), Atomwise, Inc. (US), BenevolentAI (UK), NuMedii (US), BERG LLC (US), Cloud Pharmaceuticals (US), Insilico Medicine (US), Cyclica (Canada), Deep Genomics (Canada), IBM (US), BIOAGE (US), Valo Health (US), Envisagenics (US), twoXAR (US), Owkin, Inc. (US), XtalPi (US), Verge Genomics (US), Biovista (US), Evaxion Biotech (Denmark), Iktos (France), Standigm (South Korea), and BenchSci (Canada). Players adopted organic as well as inorganic growth strategies such as product upgrades, collaborations, agreements, partnerships, and acquisitions to increase their offerings, cater to the unmet needs of customers, increase their profitability, and expand their presence in the global market.

Research Coverage The report studies the AI in drug discovery market based on offering, technology, application, end user, and region The report analyzes factors (such as drivers, restraints, opportunities, and challenges) affecting the market growth The report evaluates the opportunities and challenges in the market for stakeholders and provides details of the competitive landscape for market leaders The report studies micro-markets with respect to their growth trends, prospects, and contributions to the total AI in drug discovery market The report forecasts the revenue of market segments with respect to five major regions

Key Benefits of Buying the Report:The report will help the leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall market and the sub-segments.This report will help stakeholders understand the competitive landscape and gain more insights to better position their businesses and plan suitable go-to-market strategies.

The report also helps stakeholders understand the pulse of the AI in drug discovery market and provides them information on key market drivers, restraints, challenges, and opportunities.Read the full report: https://www.reportlinker.com/p05828730/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Artificial intelligence/AI in drug discovery Market is projected to reach USD 4.0 billion by 2027 from USD 0.6 billion in 2022, at a CAGR of 45.7%...

Artificial Intelligence On The Hunt For Illegal Nuclear Material – Texas A&M University Today

Nuclear engineering doctoral student Sean Martinson works on plutonium solution purification inside a protective glove box in Sunil Chirayaths nuclear forensics laboratory.

Justin Elizalde/Texas A&M Engineering

Millions of shipments of nuclear and other radiological materials are moved in the U.S. every year for good reasons, including health care, power generation, research and manufacturing. But there remains the threat that bad actors in possession of stolen or illegally produced nuclear materials or weapons will try to smuggle them across borders for nefarious purposes.

Texas A&M University researchers are making it harder for them to succeed.

If border agents intercept illicit nuclear materials, investigators need to know who produced them and where they came from. Fortunately, nuclear materials carry certain forensic markers that can reveal valuable information, much like fingerprints can identify criminals.

For instance, when scientists examine the concentration of certain key contaminant isotopes in separated plutonium samples they can determine three different attributes of the samples history: the type of nuclear reactor that produced it, how long the plutonium or uranium was contained in the reactor and how long ago it was produced.

With current statistical methodologies, they can determine these three attributes utilizing a generated database that stores the required information as a mathematical variation of these attributes for various nuclear reactor types and emerge with a good idea of who made the material.

But what if investigators are presented with a mixed plutonium sample? said Sunil Chirayath, author of a new study on nuclear forensics recently published in the journal Nuclear Science and Engineering.Suppose the adversary is mixing materials from two nuclear reactors at two different times, and that material is cooled for different times. A bad actor might do this intentionally to disguise it.

Mixed samples of nuclear material are significantly more challenging to identify with traditional methodologies. In a real-world situation, the extra time required could have a catastrophic impact on the global community.

To improve the process, Chirayath, associate professor in the Department of Nuclear Engineering and director of the Texas A&M Engineering Experiment StationsCenter for Nuclear Security Science and Policy Initiatives, along with his research team, has developed a methodology using machine learning, a type of artificial intelligence.

He can produce identifying markers through simulations, and then store that data in a 3D database. Each attribute is one level of the database, and a standard computer can quickly process the data and lead investigators to the reactor type that produced the plutonium sample and, potentially, the suspects by joining other pieces of the puzzle gathered through traditional forensics.

Three experiments of irradiating uranium using three different reactor types and post-irradiation examinations have been conducted at Texas A&M to date. Without knowing the samples origins, doctoral student researcher Patrick ONeal successfully identified where each of the plutonium samples was produced by using machine learning.

The work is being done through aconsortium of national labs and universitiesfunded by the U.S. Department of Energys National Nuclear Security Administration. The consortium focuses on development of new methods of detecting and deterring nuclear proliferation and to educating the next generation of nuclear security professionals. Chirayaths team will soon run one more irradiation and the corresponding post-irradiation examination with funding already in place.

The next step is to take this machine-learning methodology to high-level government labs, where researchers can work with much larger samples of nuclear materials. University labs are constrained by more restrictive irradiation safety limits.

Chirayath is confident efforts to prevent nuclear proliferation are working. The international Treaty on the Non-Proliferation of Nuclear Weapons arose from concern about atomic weaponry, and all but four countries India, Israel, Pakistan and South Sudan signed it.North Korea signed it but walked away from it later.

Chirayath also notes that with the rise in nuclear energy production comes an increased risk that the technology will be used to make weapons capable of mass destruction.

We have to make sure materials are not diverted from peaceful use, he said. We need to double-up our tools and methodologies, but its not just technical tools. We also have to double-up on policies and agreements to prevent proliferation from happening.

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Artificial Intelligence On The Hunt For Illegal Nuclear Material - Texas A&M University Today

How artificial intelligence (AI) will help Autodesk expand in the metaverse – VentureBeat

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

For the 40-year-old Autodesk known for its design and creation software (including AutoCAD) used by professionals in industries including architecture, engineering, construction, manufacturing and entertainment artificial intelligence (AI) has become a must to help boost creativity and collaboration.

A common theme is helping the designer, said Tonya Custis, director of artificial intelligence research at Autodesk, whose team includes 15 AI research scientists based in San Francisco, Toronto and London.

But AI will also help Autodesk expand in the metaverse. According to Custis, Autodesks use of AI is also helping to tackle challenges around geometry understanding to help contextualize the geometric world around us which will be super-important as the metaverse expands, in terms of speeding up animation and CGI processes, as well as in architecture and engineering.

Its about how we can understand the geometry of the world around us not just of objects, but of space, she said, adding that Autodesks AI efforts will absolutely be important as the metaverse evolves.For example, how is a space organized? What are the things in it? How can we break it down into geometry and, then, what are its functions because a computer does not know that.

Media coverage acknowledges that Autodesk, along with companies such as Meta, Roblox, Microsoft and Nvidia, may play a role in building the metaverse.

That may include the role played by Autodesks investments and acquisitions: The San Rafael, California-based company recently announced an investment in Radical, a New York-based developer whose proprietary AI combines modern deep learning strategies, human biomechanics, and computer graphics to estimate, track and reproduce skeletal joint rotations in 3D from a single conventional video feed. From videos to metaverses, this data can be used to automate the animation of 3D characters and avatars and requires no special hardware, training or custom coding.

The investment in Radical follows Autodesks acquisition of Moxion, with its cloud solution for digital dailies, in January and last Novembers acquisition of cloud-based animation pipeline software firm Tangent Labs.

Autodesk has a lot of tools that people use to make things in the professional space of things like animation and movies, but as far as content creation goes, these tools are becoming more ubiquitous, Custis said. So Autodesks investment in a company like Radical democratizes a lot of that technology.

But Autodesk is most well-known for its work in architecture, engineering and construction, particularly through their AutoCAD software.

My AI research team, in particular, works on things like floor-plan generation, while there are some projects product teams are working on using machine learning to make command sequences easier, to make it easier to import information from drawing, she said. A lot of architects like to use paper to do their designs, and then they have to be translated into CAD so thats a real waste of time for them.

Since many AutoCAD users are experts often even getting graduate degrees in the use of the software theres a fine line between automation that is helpful and taking control away.

Its a lot about how we provide algorithms that automate things that make sense that will save them time, but also giving them the agency to make choices, or give them recommendations that they can then choose, she said. Its definitely a collaborative AI environment on the AEC side.

For manufacturers, Custis said her team works a great deal with Autodesks Fusion product, on issues such as deep learning for 3D CAD models. For example, we teach the computer to learn how to put assemblies together, such as all the parts you need to build a unicycle, she said. And then, can we teach specific robots to do that, once we understand what the steps are, whats required, how the pieces go together?

Autodesk is also highly focused on AI-based generative design, in which designers or engineers input design goals into the generative design software, along with parameters such as performance or spatial requirements, materials, manufacturing methods and cost constraints. The software explores all the possible permutations of a solution, quickly generating design alternatives. It tests and learns from each iteration what works and what doesnt.

While debate around the use of large language models is all the rage at the moment, they offer use cases that are very relevant to Autodesk, especially in media and entertainment, said Custis.

Its definitely something were looking at closely, and were actually also working with OpenAI, she said. I think generative models are really exciting in our space the trajectory in machine learning is usually first we do stuff on text, then we do stuff on pictures, then we do stuff on videos, then we do stuff in 3D so all of this is happening right now.

The ultimate goal at Autodesk, she reiterated, is to use AI to help users have more time to be more creative.

We dont want to replace them, we dont want to take their job from them, she said. But we do want to give them more flexibility and agency about how they use their time and support that creativity.

As for Autodesks impact on the metaverse, Custis said the future remains to be seen.

Theres a place there and a lot of the work my team is working on in AI research is pretty applicable, she said. But I cant speculate how those particular things will play out.

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How artificial intelligence (AI) will help Autodesk expand in the metaverse - VentureBeat