At the core of DEPT®’s approach is the strategic utilisation of large language models. Briski articulated the delicate balance between the ‘pioneering’ and ’boutique’ ethos encapsulated in their tagline, “pioneering work on a global scale with a boutique culture.”

While ‘pioneering’ and ’boutique’ evokes innovation and personalised attention, ‘global scale’ signifies the broad outreach. DEPT® harnesses large language models to disseminate highly targeted, personalised messages to expansive audiences. These models, Briski pointed out, enable DEPT® to comprehend individuals at a massive scale and foster meaningful and individualised interactions.

“The way that we have been using a lot of these large language models is really to deliver really small and targeted messages to a large audience,” says Briski.

However, the integration of AI into various domains – such as retail, sports, education, and healthcare – poses both opportunities and challenges. DEPT® navigates this complexity by leveraging generative AI and large language models trained on diverse datasets, including vast repositories like Wikipedia and the Library of Congress.

Briski emphasised the importance of marrying pre-trained data with DEPT®’s domain expertise to ensure precise contextual responses. This approach guarantees that clients receive accurate and relevant information tailored to their specific sectors.

“The pre-training of these models allows them to really expound upon a bunch of different domains,” explains Briski. “We can be pretty sure that the answer is correct and that we want to either send it back to the client or the consumer or some other system that is sitting in front of the consumer.”

One of DEPT®’s standout achievements lies in its foray into the web3 space and the metaverse. Briski shared the company’s collaboration with Roblox, a platform synonymous with interactive user experiences. DEPT®’s collaboration with Roblox revolves around empowering users to create and enjoy user-generated content at an unprecedented scale. 

DEPT®’s internal project, Prepare to Pioneer, epitomises its commitment to innovation by nurturing embryonic ideas within its ‘Greenhouse’. DEPT® hones concepts to withstand the rigours of the external world, ensuring only the most robust ideas reach their clients.

“We have this internal project called The Greenhouse where we take these seeds of ideas and try to grow them into something that’s tough enough to handle the external world,” says Briski. “The ones that do survive are much more ready to use with our clients.”

While the allure of AI-driven solutions is undeniable, Briski underscored the need for caution. He warns that AI is not inherently transparent and trustworthy and emphasises the imperative of constructing robust foundations for quality assurance.

DEPT® employs automated testing to ensure responses align with expectations. Briski also stressed the importance of setting stringent parameters to guide AI conversations, ensuring alignment with the company’s ethos and desired consumer interactions.

“It’s important to really keep focused on the exact conversation you want to have with your consumer or your customer and put really strict guardrails around how you would like the model to answer those questions,” explains Briski.

In December, DEPT® is sponsoring AI & Big Data Expo Global and will be in attendance to share its unique insights. Briski is a speaker at the event and will be providing a deep dive into business intelligence (BI), illuminating strategies to enhance responsiveness through large language models.

“I’ll be diving into how we can transform BI to be much more responsive to the business, especially with the help of large language models,” says Briski.

As DEPT® continues to redefine digital paradigms, we look forward to observing how the company’s innovations deliver a new era in AI-powered experiences.

DEPT® is a key sponsor of this year’s AI & Big Data Expo Global on 30 Nov – 1 Dec 2023. Swing by DEPT®’s booth to hear more about AI and LLMs from the company’s experts and watch Briski’s day one presentation.

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Gaming QA testers regularly come under fire as bug-riddled games reach the hands of disappointed gamers with seemingly increasing prevalence.

The backlash QA testers receive from frustrated gamers is often misplaced. Managers are often aware that games are full of bugs but decide to release unfinished games anyway in the hope of starting to recoup development costs.

However, proper QA testing is a time-consuming and resource-intensive process. On the PAX stage, Booty explained how a game needs to be re-tested from start to finish whenever a feature is added.

“Some of the processes that we have [currently] haven’t really kept up with how quickly we can make content. One of those is testing,” Booty said.

“There’s a lot going on with AI and ML right now, and people using AI to generate all these images. What I always say when I bump into the AI folks, is: ‘Help me figure out how to use an AI bot to go test a game.’”

If bugs can be caught earlier in the development process, they have a greater chance of being fixed prior to management feeling the financial pressure to send the game to market regardless.

The full ‘Storytime with Matt Booty’ talk can be viewed here. At around 01:02:00, you can hear Booty explain his vision for AI in game testing.

“I would love to be able to start up ten thousand instances of a game in the cloud – so there are ten thousand copies of the game running – deploy an AI bot to spend all night testing that game, and in the morning we get a report. That would be transformational,” explains Booty.

Booty’s core idea of improving QA testing with AI is solid. However, the way it was presented – of replacing human testers – may not go down too well. Mistreatment of QA testers in the gaming industry has led to a growing call for their members to unionise.

(Photo by Jose Gil on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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The startup has attracted interest for its ability to design and deploy interactive characters with more realistic interactions across the metaverse and other virtual words like video games.

In current video games, NPCs have pre-scripted responses. AI-powered virtual characters like those Inworld are developing can offer dynamic responses to general questions about the local area or wider world.

While graphics have generally become more immersive over the years, interactions have largely remained the same. The ability for NPCs to hold conversations more naturally will be a giant leap forward for gaming and help to deliver on the metaverse’s bold promises.

Inworld’s potentially key role in the future of the metaverse and gaming market – which is already worth hundreds of billions of dollars – has opened investors’ wallets.

Since it left stealth last year, Inworld has raised $70 million. In March, it raised $10 million in a strategic funding round.

Inworld was also notably included in the latest Disney Accelerator cohort which also included blockchain network Polygon, NFT platform Flickplay, AR hardware and interface developer Red 6, metaverse e-commerce platform Obsess, and immersive experience provider Lockerverse.

“I’ve long believed that interactions, memories, and connections formed in immersive worlds can become as real as those formed in real life. It’s why I’m excited about being named a Disney Accelerator company,” wrote Inworld AI CEO Ilya Gelfenbeyn in a LinkedIn post about the accelerator.

“Our platform for creating memorable, AI-powered characters will unlock new opportunities for creators in entertainment, gaming, the metaverse, and other digital realms who are ready to create memories like mine.”

Inworld Studio launched in beta a few months ago. The no-code system works with most common game and metaverse engines, including Unreal and unity.

“It’s crazy to think that Inworld was founded just 13 months ago. Since then, we raised two seed rounds, were selected for the Disney Accelerator, launched the beta version of our product, and now, closed our Series A!” wrote Gelfenbeyn in a blog post.

“It’s been impressive to see our community bring characters to life with AI. I know the best is yet to come.”

(Image Credit: Inworld)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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Some masochists enjoy the pain of being repeatedly beaten in games like Dark Souls – it’s understandable, overcoming those challenges is a great feeling – but most of us have a breaking point where the power button gets hit and the game just ends up collecting dust on a shelf.

A patent spotted by SegmentNext details an AI which learns from the human player and can continue when they’re not playing:

“The method includes monitoring a plurality of game plays of the user playing a plurality of gaming applications,” the patent explains.

“The method includes generating a user gameplay profile of the user by adjusting the default gameplay style based on the plurality of game plays, wherein the user gameplay profile includes a user gameplay style customized to the user. The method includes controlling an instance of a first gaming application based on the user gameplay style of the user gameplay profile.”

It’s easy to see how such an AI could be used in grind-heavy games to reach a required level, or another player’s AI that has completed a particular challenge could even be used to overcome it rather than lead to a situation where they give up completely.

For those who feel that level of intervention is cheating, the AI could instead offer tips to the human player as they’re gaming. The AI basically wants to be a helpful player two rather than the person on your couch that mocks you and then does worse when they take the controller.

While great strides have been made in making games accessible for people with disabilities, there are some parts of games which remain problematic even with the best intentions of developers. These individuals will also benefit from an AI which can take the reins for a bit and allow them to continue enjoying the rest of the game and the increasingly immersive storytelling many provide.

(Photo by Fabian Albert on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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While gaming remains Nvidia’s primary source of growth, the company’s artificial intelligence business is growing rapidly. In the second fiscal-quarter, Nvidia reported increased demand for both its graphics and AI chips.

For some idea of Nvidia’s growth in AI, Huang says there are now over 4,000 AI startups working with his company. That number is up from 2,000 in April 2017 and goes to show the growing interest around the technology.

Nvidia continues to innovate in AI and is quickly establishing itself as a major player; particularly in driverless cars.

The company’s self-driving platform DRIVE is powered by Nvidia Xavier chips and consists of three parts:

1. DRIVE AV — This part of the DRIVE platform was also available in the previous generation and uses neural networks to perform the calculations required for self-driving cars.
2. DRIVE IX — The first of the two new inclusions is a software development kit which enables AI assistants that can harness data from sensors inside and outside the vehicle.
3. DRIVE AR — While there have been many advancements around self-driving technologies and AI assistants for cars, augmented reality is still relatively unexplored. With DRIVE AR, Nvidia intends to enable new graphical experiences which can deliver things such as information about points of interest along the route.

Nvidia says its Xavier chips have been in development for over four years and represents the work of over 2,000 engineers. Xavier features more than nine billion transistors and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created.

In a conference call last August, Huang teased Tesla’s reported difficulties in building its own AI chips: “It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

Autonomous car manufacturers need to train petabytes of data per day, reiterate their models, and deploy them again in order to get those vehicles to market. Nvidia has a server, equivalent to 800 traditional servers, called the DGX-2 which delivers two petaflops of performance.

During AI Expo London earlier this year, Nvidia senior director of enterprise David Hogan said: “The compute demands of AI are huge and beyond what anybody has seen within a standard enterprise environment before. You cannot train a neural network on a standard CPU cluster.”

While Nvidia is well-positioned in AI, and is already reaping the benefits, it foresees gaming continuing to be its primary source of revenue for some time.

Nvidia reported earnings per share of $1.24 on revenues of $2.58 billion in the second fiscal quarter ended July 31. Gaming was about 50.3% of total revenues, and GPUs were 81% of revenues.

Huang believes Nvidia’s GPU sales will increase thanks to the growing popularity of ‘ray tracing’ in games, deliver stunning realism by enabling things such as full-detail reflections which mirror the action happening in and around the player’s field of view:

Few games offered support for ray tracing last year and supporting RTX chips were expensive, so sales were subdued. That’s changed this year, with more supporting games and cheaper RTX chips available; a combination which produced $1.3 billion in gaming revenue for Nvidia this quarter.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

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Most sports have been established for some time and ‘new’ ones are generally just similar but with some tweaked rules.

Design agency AKQA set out to create a truly new sport with help from an AI – meet, Speedgate.

Speedgate features six-player teams (take that, five-a-side lovers…) which compete on a field with three open-ended gates.

To score, players must first kick a ball through a centre gate which they’re unable to step through. After which, they can score a point by putting a ball through either of the end gates. If a teammate catches the ball and ricochets it back through, an extra point is scored.

To keep things fast-paced, the ball cannot be still for more than three seconds.

Rather than a rewrite of the rules of an existing sport, Speedgate sounds more like a collision of several. There are elements of soccer (I’m a Brit, so don’t tell my mates I called it that but NFL is king…), basketball/netball in terms of not standing still for long, and I’d imagine even some dodgeball.

That’s probably of little surprise when you hear how the AI came to create its new sport.

Data from 400 existing sports was fed into a neural network before it spat out some basic outlines for a sport and its accompanying rules. Some of these rules, while pretty awesome, were unrealistic. Many of us would like to see things like exploding Frisbees – but health and safety people are sticklers.

Playtests were then used to whittle down the sports to one champion. AKQA is now in discussions with the Oregon Sports Authority to get a Speedgate league going this summer.

AI even created the logo and motto, though we think the latter could do with some work (“face the ball to be the ball to be above the ball”).

It’s a cool example of what AI is able to create, but let’s make a pact to keep clips of The Hunger Games locked away. After all, AIs are already beating us at our own creations

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo..

The post AI is now creating sports for puny humans to play appeared first on AI News.

AI News first reported of OpenAI’s gaming prowess in August 2017 when it took on three of the best Dota 2 players in the world and won.

The AI learned how to play the game from scratch and was able to beat regular players within the space of an hour. Professional gamers put up more of a fight, with the AI requiring two weeks of training to beat some of humankind’s best.

At the time, OpenAI said they believe their AI is beatable as it’s not better in terms of actions-per-minute but made smarter decisions. Some players were able to confuse the bot and distract it from the main objectives; showing it’s not flawless.

In the latest man versus machine spectacle, the humans lost the first two rounds but won in the third.

The opponents faced off Valve’s The International 2018 esports competition in San Francisco. Rules were kept the same as the last bout which meant things like ‘couriers’ (NPCs used for delivering items to heroes) were not invulnerable.

On average, a match features 80,000 individual frames and each character is able to perform around 170,000 possible actions. The AI’s ability to comprehend and take relevant actions is nothing short of incredible.

According to OpenAI Cofounder and Chairman Greg Brockman, the firm’s AI now has the equivalent of 45,000 years of Dota 2 gameplay experience. Taking that into account makes it even more impressive the human players managed to beat the AI even once.

OpenAI is currently able to play just 18 of the 115 heroes featured in Dota 2, so – if you’re thinking of issuing a challenge – perhaps get to grips with those it’s not had the equivalent of around 562 human lifetimes training in.

An archived broadcast of the match can be viewed on Twitch here.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post Humans won a Dota 2 round against OpenAI! But lost overall appeared first on AI News.

Back in 2015, DeepMind attempted to play various Atari games. The AI was competent in most of the games and became as good at Video Pinball as a human player.

DeepMind notoriously struggled with Montezuma’s Revenge, a notoriously complex game from the 1980s. The AI was unable to learn a path and retrieve even the first ‘key’ in the game.

Video games, in general, have become a battleground for AIs to show-off. DeepMind’s failure with Montezuma’s Revenge set the game as one benchmark for the industry to prove advancements.

A new algorithm designed by Fabio Zambetta and his team from RMIT University learns from past mistakes and identified next steps 10 times faster. The AI was successful in autonomously playing Montezuma’s Revenge.

In a statement, Zambetta explained:

“Truly intelligent AI needs to be able to learn to complete tasks autonomously in ambiguous environments.

We’ve shown that the right kind of algorithms can improve results using a smarter approach rather than purely brute forcing a problem end-to-end on very powerful computers.”

Zambetta presented his findings at the 33rd AAAI Conference on Artificial Intelligence in Hawaii today and explained how it works.

DeepMind and similar AIs struggle with adventure games like Montezuma’s Revenge due to a lack of reward until it obtains the first item, a key in this case. This makes it difficult for the AI to work out if what it’s doing is correct/optimal.

Games like Video Pinball provide the AI with quick rewards due to things such as point increases. This approach enables the AI to learn what path is going to achieve the highest score.

By implementing reinforcement learning, the researchers added ‘pellet rewards’ for the system to promote it exploring more paths.

“With time, this technology will be valuable to achieve goals in the real world, whether in self-driving cars or as useful robotic assistants with natural language recognition,” said Zambetta.

Other AI researchers are continuing to advance their approaches. DeepMind itself published two papers last summer describing how an AI could learn to conquer Montezuma’s Revenge from YouTube videos.

We look forward to watching upcoming AI bouts for the Montezuma’s Revenge title.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post DeepMind’s AI bested in Atari game Montezuma’s Revenge appeared first on AI News.

The live stream of the showdowns was viewed by more than 55,000 people.

“This is, of course, an exciting moment for us,” said David Silver, a researcher at DeepMind. “For the first time, we saw an AI that was able to defeat a professional player.”

DeepMind created five versions of their ‘AlphaStar’ AI. Each AI was trained with historic game footage that StarCraft-developer Blizzard has been releasing on a monthly basis.

In order to further improve their abilities, the five AIs were pitted against each other in a league. The leading AI racked up experience that would equate to a human training for around 200 years.

Perhaps needless to say, AlphaStar wiped the floor with human players Grzegorz Komincz and Dario Wunsch.

You can watch AlphaStar taking on the human players below:

The only hope for humans so far is that AlphaStar was trained for a single map and using just the one alien race type of three available in the game. Removed from its comfort zone, it would not perform as well.

Video games have driven more rudimentary AI developments for decades. The advancement shown by AlphaStar could be used to create more complex ‘bots’ that can pose a challenge and help train even the best human players.

This isn’t the first time we’ve seen DeepMind’s AI bots in action – but, in the past, they’ve had a tendency of immediately rushing its opponents with ‘workers’ in a behaviour that Blizzard called “amusing”.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post DeepMind thrashed pro StarCraft 2 players in latest demo appeared first on AI News.

StarCraft 2 is a complex game with many strategies, making it the perfect testing ground for AI. Google’s DeepMind first started exploring how it can use AI to beat the world’s best StarCraft players back in 2016.

In 2017, StarCraft’s developer Blizzard made 65,000 past matches available to DeepMind researchers to begin training bots. Blizzard promised it would make a further half a million games available each month.

We’ve seen DeepMind’s AI bots in action with various degrees of success. The AI had a tendency of immediately rushing its opponents with ‘workers’ in a behaviour that Blizzard called “amusing,” but confessed it had a 50 percent success rate even against StarCraft 2’s AI bots on ‘insane’ difficulty.

Fed with some replays from human players using more complex strategies, the AI began adopting them.

“After feeding the agent replays from real players, it started to execute standard macro-focused strategies, as well as defend against aggressive tactics such as cannon rushes,” Blizzard said.

We’re yet to see these new strategies being used by DeepMind’s AI but it won’t be much longer until we do.

“It’s only been a few months since BlizzCon but DeepMind is ready to share more information on their research,” Blizzard said today.

“The StarCraft games have emerged as a ‘grand challenge’ for the AI community as they’re the perfect environment for benchmarking progress against problems such as planning, dealing with uncertainty, and spatial reasoning.”

You can find a stream of DeepMind’s AI playing StarCraft 2 via StarCraft’s Twitch or Deepmind’s YouTube at 6pm GMT/10am PT/1pm ET on January 24th.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post DeepMind’s AI will show off its new StarCraft 2 skills this week appeared first on AI News.