Categories
Game

‘Street Fighter 6’ arrives in 2023 with new modes and real-time commentary

A few months after confirming the existence of Street Fighter 6, Capcom has revealed that the upcoming entry to the fighting franchise will come out in 2023 for the PS 5, PS 4, Xbox Series X|S and PC. Like the company promised in its initial teaser, the game will include Luke, a key DLC character for Street Fighter V, as well as fan favorites Ryu and Chun-Li. The video game developer previously described Luke as “a key player in the future of Street Fighter” who would help expand its world. 

Capcom is using its own RE Engine to develop the fighting game, and it says that gives it the capability to make sure finer details shine through, such as the look of individual muscles tensing up. The company also used the RE Engine to for its other popular titles, including Resident Evil Village, Devil May Cry 5 and Monster Hunter: Rise.

One of the features debuting with the title is Real Time Commentary, which will provide easy-to-understand explanations about gameplay for your matches. These commentaries will be voiced by notable Fighting Game Community commentators, starting with Jeremy “Vicious” Lopez and Aru, and they’ll support subtitles in 13 languages.

It will also feature modes from previous entries, along with two new ones called World Tour and Battle Hub. In addition, Capcom is introducing Modern Control Type with Street Fighter 6 to make special moves easier to execute: It will allow you to perform special attacks simply by pressing the button for it along with directional input.

The company has yet to announce an exact release date for it, but you can watch a trailer for the game above.

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Categories
AI

Ryff raises $11.7M for its real-time brand integration

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Ryff has raised $11.7 million for its business of inserting brand advertisements into commercials, live broadcasts, and streams in real time using digital video and animation technology.

Los Angeles-based Ryff runs an advertising business where it places virtual objects in a scene of a movie, TV commercial, TV show, or even live media so that they seem like a natural part of the environment. CEO Roy Taylor said in an interview that Ryff rewrote the rules of product placement using proprietary AI technology, which can insert products into fully mastered and edited content.

Product placement is the advertising tactic of placing a branded object, like a bottle of Coca-Cola, in a scene in a movie or a TV show. But Ryff can put a realistic 3D-animated branded object into a scene after the fact, depending on whether an advertiser wants to become a sponsor.

Big money

Roy Taylor (left) of Ryff and Paul Feinstein of Audent.

Above: Roy Taylor (left) of Ryff and Paul Feinstein of Audent.

Image Credit: Ryff

Audent Global Asset Management led the round, with participation from Vulcan and Mac Ventures.

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Paul Feinstein, managing partner and chief investment officer of Audent Global Asset Management, said in an interview with GamesBeat that his investment firm was instantly drawn to Ryff because they believe it has the potential to dramatically disrupt the product placement industry for digital media. Ryff has worked with brands such as Coca-Cola, Diageo, and WPP/Group M.

“Ryff seamlessly integrates product into scenes, not just television and motion pictures, but anything digital,” Feinstein said. “That’s fascinating for me because I believe that’s where advertising is going. Anything that stands out as disruptive gets my attention. Roy has come up with a way for how advertising is going to be done in the future.”

He added, “Sports is just one area. Think about the influencers, digital media, whether it’s Instagram, Snapchat, where the medium dollars are going to right now. So that creates another opportunity.”

Feinstein said he is drawn to people who disrupt the status quo like Taylor, who contributed to the success of both Nvidia and Advanced Micro Devices.

“I have a lot of high profile entertainment-related clients, and so this was a really good fit,” Feinstein said.

Taylor said the rapid evolution of technology has transformed media and entertainment into an experience-led industry. No longer will viewers tolerate interruptions into their entertainment or want to see messages which are not either culturally or contextually relevant to them.

For the first time, brands and their agencies can tailor messages directly to consumers through the implementation of very large-scale, local and global content brand integration. The Ryff platform allows brands both large and small to discover and build messages and campaigns that deliver results that outperform all other kinds of advertising.

Spheera

Above: The people are real. The Bailey’s is not.

Image Credit: Ryff

Ryff embeds product placement imagery into the content that is not only contextual but also drives positive emotions from audiences, the company said. The imagery embedded can take the form of simple branded objects or signage all the way through to sophisticated interactive and dynamic ads.

Brand references appear as if they were filmed in the original production and can be tailored for audience specificity according to a range of variables including the individual viewer, platform (e.g., traditional broadcast, web, or mobile), geography, date, and demographic profile.

Taylor said the company has a 10,000-hour library of content available for brands. And he said the company is working on Spheera, a platform for the creator economy aimed specifically at every type of entertainment.

Taylor said he plans to make a significant investment in new Nvidia AI hardware.

“Rendering is only one part of what we do when we calculate the scale for an object and its placement,” Taylor said. “We have to calculate scale on any kind of scene, and now we have got to the point where it is in real time. That’s what we call the ingestion process.”

Once it’s ingested, the content goes into a “data lake.” An image will have thousands of data points and metadata per frame. Using that, the company can build out individual promotions or campaigns. Advertisers can switch a product in mid-campaign if it isn’t doing well.

“Increasingly, we use the term brand integration instead of product placement,” Taylor said. “We can do brand integration in real time with Spheero for things like livestreaming.”

Creator economy

Disrupting traditional media is a big task. But the creator economy could be even bigger, Taylor said. Spheera will be a “direct-to-creator platform” where brands and go and find out what people are watching and build campaigns for digital brand integration, he said.

There can be “guard rails” to protect both the brand and the creator, and both can be rewarded for their work in real time, Taylor said. Perhaps AI could be used to help set those guard rails.

“We started to realize there is something much bigger going on here,” he said. “The creator economy isn’t just nice marketing speak. It’s profound and it’s going to happen. You can monetize streams through intelligent brand integration. There is a very large scope for additional work for AI.”

Taylor said that Ryff will promote the ethical use of AI, and it will stay away from “deep fakes.” I suppose that means Ryff shouldn’t place an influencer in a place that they’ve never actually been, for the sake of a promo.

“We just won’t touch it,” he said.

He noted the guy who skateboards to the sound of a Fleetwood Mac song on TikTok while drinking a bottle of Ocean Spray.

“That guy made a little money, but he made hundreds of millions of dollars for Ocean Spray,” Taylor said. “With platforms like TikTok and Twitter, we can help people monetize. I think what we’re doing is going to support creators everywhere.”

Previous investors in Ryff include Valor Equity Partners, and Moneta Ventures. The company has about 30 people and its hiring.

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Categories
AI

SugarCRM taps real-time sentiment analysis for customer service

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SugarCRM has boosted the artificial intelligence (AI) capabilities of its SugarPredict engine for marketing and sales intelligence, adding a sentiment analysis tool that determines prospects’ “emotional state and intent.”

SugarPredict is available on the Cupertino, California-based company’s Sugar Sell and Sugar Market platforms for automating and assisting with sales and marketing tasks. It’s also embedded in SugarCRM’s SugarLive multichannel customer communications application, the company said in a statement.

SugarLive is “designed to enable sales and service personnel to track the details of each customer interaction as it’s happening and effortlessly access customer information across all touch points and channels at the exact moment it’s needed,” SugarCRM said.

The omnichannel SugarLive customer service tool is available to SugarCRM customers with Sugar Serve licenses and provides real-time intelligence to customer service agents who are interacting with prospects online or on the phone.

Sentiment analysis works by using natural language processing (NLP) and AI to score the state of mind of a prospective or existing customer to determine best next steps, the company said. These could include escalating an interaction to a supervisor, presenting a “save-the-sale” offer, or moving to an upsell attempt.

Making the best first impression

SugarCRM chief technology officer Rich Green said sentiment analysis is intended to help SugarPredict users better handle crucial first interactions with prospective customers in the marketing process.

“You rarely get a second chance to make a great impression with a customer; it’s profoundly important to get each and every interaction right and connect on a deeply human level,” he said. “Sales and service professionals are under a great deal of pressure, as a customer’s business can be won or lost in a single misstep.”

Sentiment analysis is the latest in a series of AI-driven, marketing-centric improvements to SugarPredict. SugarCRM first released the software in January as a sales tool for SugarCRM’s Sugar Sell users. SugarPredict for sales is billed as an automated cloud software solution that can intelligently enrich the customer data entered into CRM systems to ensure quality and consistency.

In May, SugarCRM made SugarPredict available to users of its Sugar Market marketing intelligence platform to help marketing teams with automated predictive lead scoring.

Sugar Market is a customer lead-cultivating platform that helps marketing teams with tasks like quantifying how website visitors interact with digital marketing materials; assisting in the creation of emails, landing pages, and forms; qualifying leads with lead-scoring models; and aligning with sales via automated hand-offs of qualified leads.

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Categories
Game

Tool around in a real-time generated AI version of ‘GTAV’

Last month, you may have seen that a group of researchers created a machine learning system that could transform the presentation of Grand Theft Auto V into something that looks almost photorealistic. It turns out, at about the same time, another group of AI enthusiasts were working on something even more impressive involving Rockstar’s open world title. On Friday, YouTuber Harrison Kinsley shared a video showing off GAN Theft Auto, a neural network that can generate a playable stretch of Grand Theft Auto V’s game world on its own.

Kinsley and collaborator Daniel Kukieła made GAN Theft Auto with GameGAN, which last year recreated Pac-Man by watching another AI play through the game. GameGAN, as the name suggests, is a generative adversarial network. Every GAN consists of two competing neural networks: a generator and a discriminator. The generator is trained on a sample dataset and then told to produce content based on what it saw. The discriminator, meanwhile, will compare the output of the generator with the original dataset, in the process “coaching” its counterpart to output content that is closer and closer to the source material.

“Every pixel you see here is generated from a neural network while I play,” Kinsley says in the video. “The neural network is the entire game. There are no rules written here by us or the [RAGE] engine.”

Training a GAN is a very GPU intensive task. NVIDIA loaned Kinsley a DGX Station A100 computer to make the project a reality. The system comes with four of the company’s A100 GPUs and a 64-core AMD server CPU. Kinsley and Kukieła used all of that computing power to run 12 rules-based AI simultaneously. Those programs would drive the same stretch of highway, collecting the data the neural network needed to start generating its own game world. The two also developed a supersampling AI to clean up the output of the neural network so it wouldn’t look so pixelated.

As you can see from the video, the network models a surprising number of systems from the game. As the car moves, so does the shadow underneath it and the reflection of the sun on its back windshield. The mountains in the distance even get closer as well. That’s not something Kinsley necessarily expected the AI would do when he and Kukieła first started training the AI.

There’s also a dream-like quality to the gameplay and that’s thanks in part to the fact the neural network doesn’t replicate every aspect of GTA V perfectly. For one, collisions give it trouble. Kinsley says there was one instance where he saw an oncoming police cruiser split into two just as it was about to crash with his car.

If you want to try GAN Theft Auto for yourself, Kinsley and Kukieła have uploaded the project to GitHub. They say most computers should be able to run the demo. 

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Categories
AI

Realtime Robotics raises $31.4M to help industrial robots plan their moves

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Realtime Robotics, a company developing technology that enables robots to alter their motions in dynamic, fast-moving environments, has raised $31.4 million in a series A round of funding.

Founded out of Boston in 2016, Realtime Robotics said that it has developed a processor capable of creating “collision-free motion plans” in milliseconds, helping industrial robots and other autonomous vehicles plan their every move and alter course if needed.

Unstructured

While Realtime Robotics caters to structured environments where object locations and positions are known, it’s unstructured environments and unpredictable workspaces where things get particularly interesting.

In situations where other robots, moving machinery, static objects, and humans coexist, this can prove challenging for robots tasked with a particular job — if a robotic arm can move in any number of directions while simultaneously rolling along a factory floor, how will it react to a forklift truck that shoots out of nowhere? Or how can it safely collaborate with other robots in the same space without banging into each other?

That is what Realtime Robotics is setting out to solve, enabling companies to automatically generate a “network of potential motion plans” that adapt to changing environments instantly, removing the engineering complexity involved in humans having to manually configure all possible variations themselves.

Above: Realtime Robotics: Dynamic environments and adaptive motion planning

There is more than enough evidence of the profound impact that AI and automation is already having on assembly lines, ecommerce warehouses, and other verticals. The industrial automation market was pegged as a $164 billion industry in 2020, a figure that’s forecast to nearly double within six years. What Realtime Robotics and its ilk are striving to achieve is to bring human-level perception and reactions to machines that operate in dynamic or hazardous environments — anticipating their next move before they need to make it.

Prior to now, Realtime Robotics had raised around $16 million, and with another $31.4 million in the bank, the company said that it plans to expand its reach into warehouse logistics automation. It added that it also plans to continue on its existing trajectory, which has so far been focused on the automotive industry, enabling it to attract companies such as Ford to its roster of early partners, while Hyundai, Toyota, and Mitsubishi have all previously invested in Realtime Robotics too.

Investors in Realtime Robotics’ series A round included newcomers HAHN Automation, SAIC Capital Management, Soundproof Ventures, Heroic Ventures, alongside existing backers Toyota AI Ventures, Sparx Asset Management, Omron Ventures,  Scrum Ventures, and Duke Angels.

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Categories
Tech News

This Google Analytics tool automates real-time data through email and Slack

TLDR: With a lifetime GA Insights subscription, you can monitor your website analytics at all time and receive alerts the minute there’s a potential problem.

There are currently over 2 billion potential customers for your online business out there right now on the web. The money they spend online accounts for almost 20 percent of the world’s retail sales totals. And since nearly half of all U.S. small businesses don’t have a website, you can capitalize big time by being one of the ones that do.

But are you sure you’re ringing every dollar you can out of your digital business? Have you seriously climbed inside your web data to see exactly how and why your online customers are making the decisions they’re making about spending money with you? And maybe most importantly, if you ran into a serious website problem right now that put a dent in your sales figures, how long would it be before your company realized the problem?

GA Insights ($79.99, from TNW Deals) is a business tool that not only consolidates what you need to know about your online business into a simple, easily digestible form, but it can even be the canary in the coalmine, giving you a critical heads up if your operation hits an online snag.

Deployed with more than 10,000 users from companies like Ikea, Spotify, HTC, Pizza Hut, and Soft Bank, GA Insights is watching your website analytics 24/7/365 like a hawk. Through the GA Insights business health dashboard, users can get an eyeful of their key metrics at any given moment, including bounce rates, goals, sales, ad spends, page load times and more.

Armed with that data, GA Insights can blast daily, weekly, and monthly reports to you and your team to make sure everybody stays in the loop on what’s important. Users can even define custom minimum or maximum thresholds on any of those numbers — or just let GA Insight’s super-smart AI set those thresholds for you.

And if something should nudge any of those metrics outside their prescribed limits, GA Insights will immediately alert you and other vital employees to the problem with emergency alerts sent via Slack, MS Teams, or by email.

All you need is a Google Analytics account to take full advantage of a lifetime Reports Plan subscription to the GA Insights Business SaaS Tool, a more than $1,700 value. Right now, you can get it for only $79.99.

Prices are subject to change.



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Categories
AI

Optimizely adds real-time customer data tracking with Zaius acquisition

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Optimizely, a provider of tools for building websites and applications that drive digital experiences, today announced it has acquired Zaius to add a customer data platform (CDP) to its portfolio. Terms of the acquisition were not disclosed.

The addition of a CDP that tracks user behavior in real time will enable organizations to create digital experiences that are personalized down to the individual level, Optimizely CEO Alex Atzberger told VentureBeat.

Zaius is able to accomplish that by creating a predictive model using a neural network that analyzes the data an organization collects about each user to determine, for example, what products they are likely to purchase next. Atzberger said this insight makes it possible to surface a highly personalized online experience in real time.

That specific capability is what attracted Optimizely to Zaius, Atzberger said, adding, “Not all CDPs are structured the same.”

Vendors offering CDPs range from Salesforce, Adobe, Microsoft, Teradata Oracle, and SAP to a host of smaller rivals, such as Segment and Emarsys. However, Atzberger said a CDP platform that is integrated with the web platform organizations are employing to build digital experiences offers a strategic advantage over a CDP that might be integrated with a customer relationship management (CRM) application.

Zaius also comes with 50 prebuilt connectors for pulling data into the platform, in addition to providing over 120 predictive models for analyzing user behaviors, Atzberger noted.

Optimizely has gained traction via a platform for building websites and applications that incorporates feature flagging, also known as feature toggles. This software development technique enables specific functionality to be turned on and off during runtime without deploying new code. That capability makes it easier to either test certain capabilities on a segment of end users or only make specific features of an application available to a subset of those end users.

By acquiring Zaius, the company can extend that capability all the way down to a specific end user as part of a larger digital experience management (DEM) strategy, Atzberger said.

As organizations continue to invest heavily in digital business transformation initiatives to engage customers directly online, interest in CDPs has risen. Most recently, digital experience management software provider Sitecore acquired Boxever to gain a CDP. It’s not clear whether we can expect a wave of CDP mergers and acquisitions, but there are a host of standalone CDP platform providers. Many organizations are looking to consolidate the number of vendors they need to engage to drive their digital business transformation initiatives as part of an effort to reduce integration costs.

In the meantime, a report MarketDigits published last week predicts the CDP market will grow from $2.4 billion in 2020 to $10.3 billion by 2026, representing a compound annual growth rate of 34%. In general, CDP is considered a type of data warehouse that includes tools to address everything from personalized recommendations using predictive analytics to marketing data segmentation, customer retention and engagement, data monetization, and data enrichment.

Most organizations are data-rich today because of the investments they have made in platforms such as data warehouses, Atzberger noted. However, those same organizations are often “insight poor” because they lack the tools and expertise required to analyze that data within the context of real-time customer engagement, Atzberger added.

It’s too early to say how many organizations will need to invest in a CDP. However, there is a pressing need to harmonize customer data that today resides in multiple application silos, especially as those customers begin losing patience with vendors that don’t understand their preferences.

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Categories
AI

Splice Machine 3.1 enhances support for real-time AI deployments

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Splice Machine, a startup that offers offline and batch analysis tools to power intelligent apps for operational workflows, today launched version 3.1 of its platform. Splice Machine 3.1 introduces new features and functionality to support enterprises with real-time AI projects, including resource elasticity support on Kubernetes, GPU support, and extensions to Spark’s machine learning libraries.

Most companies struggle to develop working AI strategies. According to a recent survey by Rackspace, only 20% of enterprises report having mature AI and machine learning initiatives. Indeed, while Deloitte says 62% of respondents to its corporate October 2018 report deployed some form of AI, roughly 25% of companies see half their AI projects ultimately fail.

Splice says that version 3.1 of its product — which combines database and AI technologies — addresses some of the challenges data science teams encounter while training, validating, and deploying AI systems. For example, it introduces native Spark structured streaming ingestion, a feature that makes streaming resources ostensibly easier to ingest than before. And Splice Machine 3.1 adds new database capabilities including foreign key processing, richer trigger support and improved handling, indexes on expressions, and improved import and export capabilities, as well as DB2 compatibility.

Splice cofounder and CEO Monte Zweben says that the streaming ingestion capability should prove especially useful for industrial accounts connected to distributed control systems, where it’s essential to ingest data in real time as it becomes available. “With 3.1, we have made vital leaps in database capabilities,” Zweben said. “[They’ll] successfully operationalize real-time AI applications and bring machine learning models into production.”

Splice Machine 3.1 also aims to increase the transparency around the data used to create AI and machine learning models at scale. A new feature enables developers to query a database back in time with syntax to a specific date, providing an audit and lineage for a regulator checking for bias or data drift. It builds on Livewire, a product Splice launched last November that draws on live sensor data to predict operational problems to avoid outages and keep machinery up and running.

A number of enterprises in the process of adopting AI struggle with the key stages of data collection, preprocessing, and prep. A recent study found that less than 4% of companies report that the data used to train their AI systems presented no problems, with most data-related problems stemming from how the data was being produced and labeled internally. Biases or errors in the data and a lack of sufficient resources topped the list of data management problems with which businesses most often struggled.

“We are excited to be powering data engineers and data scientists with the tools they need,” Zweben added. “[We believe they’ll] break down the chasms that stop machine learning and AI projects from being successful.”

Splice Machine 3.1 is available as a fully managed cloud service on Amazon Web Services, Azure, and Google Cloud Platform and is also available on-premises.

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