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AI

Colossal Biosciences spins off Form Bio software, offers platform for advanced, AI-based applications

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Dallas-based Colossal Biosciences, the self-described de-extinction company behind the woolly mammoth and thylacine, today announced that it is spinning off Form Bio, as an independent software company offering a computational life sciences platform that bridges the gap between data and discovery. 

Driven by deep learning artificial intelligence (AI) algorithms, Form Bio empowers life scientists with a software platform for managing large datasets, executing verified workflows, visualizing results and collaborating with their peers. The platform brings these capabilities together in one cohesive unit with a user experience designed to simplify computational work and bolster life science breakthroughs across companies, labs and universities. 

It is designed to be applied across an array of use cases including drug discovery, gene and cell therapy, manufacturing efficiency, academic research and more. Form Bio enters the market with a $30 million series A funding round led by JAZZ Venture Partners with participation from Thomas Tull, Colossal lead investor. 

“When you have a big scientific endeavor like de-extincting a species, you not only need the smartest scientists in the world, you need powerful software, much of which simply hasn’t existed until now,” said Ben Lamm, Colossal cofounder and CEO. “After reviewing everything available on the market, we chose to create our own software solution. Now, we want to share this platform with the broader community to impact other areas of scientific innovation, including human health.”

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Form Bio was created almost in parallel with Colossal, the company that rose to fame for applying CRISPR technology for the purposes of species restoration, critically endangered species protection and the repopulation of critical ecosystems that support the continuation of life on Earth. At the time, the Colossal team recognized the lack of necessary software capabilities in the market and inadequacies of traditional bioinformatics processes to rapidly analyze enormous volumes of data. More importantly, they noticed that the market lacked the tools required by the team at Colossal themselves for comparative genomics and computational biology.

Developed initially to advance de-extinction, Form Bio was a solution that had the potential to address the data deluge while responding to the industry’s acute need for a simple, user-friendly platform to replace mountains of code, cumbersome data wrangling processes and underdeveloped tools. 

Colossal’s team of computation biologists, along with the team of scientists at the Church Lab, and more than 35 geneticists at Colossal’s labs in Boston and Dallas, worked to develop and refine Form Bio’s core platform capabilities for the broader market.

The future of bioengineering: AI and machine learning

“Computer-aided design, fabrication, testing analyses and machine learning are key to the future of bioengineering in general and specifically restoration of endangered and extinct genetic diversity for keystone species in vital ecosystems,” said Colossal cofounder George Church, Professor at Harvard Medical School and MIT, and director of Synthetic Biology at the Wyss Institute in Boston. “Form Bio is the software critical to pave the way. As scientist-engineers, we need these pipelines and look forward to faster breakthroughs in scientific discoveries and applications, now that software has caught up with science.”

The Form platform sits at the intersection of biology and discovery. It brings together core components of data management, workflows, results visualization and collaboration in one cohesive solution. Included as part of Form is an extensive catalog of verified workflows, covering a wide range of scientific use cases from ancient DNA analysis and transcriptomics to AAV gene therapy. 

The platform also serves as a foundation for advanced, AI-based applications tailored to the needs of specific industries and academic fields. “Scientists do like to tinker and do things very bespoke. The platform allows them to upload their own workflows or use the workflows that we’ve already developed and validated. So it gives them the confidence that it is something that they can trust the output of and that if they bring a different set of data in six months to get comparable results,” said Claire Aldridge, Ph.D., chief strategy officer, Form Bio. 

With the launch of Form Bio, Kent Wakeford will transition from his day-to-day role as Colossal’s COO to Form Bio’s co-CEO, where he will work alongside co-CEO Andrew Busey and support Colossal’s CEO and Board of Directors in an executive special advisor role. Former Biolabs COO, Adam Milne, has also joined Colossal as its new Chief Operating Officer. 

“We’ve created one integrated platform that’s been in development for the last 18 months. It allows users to take an idea and move it all the way into production in a way that is more efficient and cost effective. By taking a more open and transparent approach, the platform can use different toolsets, export data in any way they choose, perform computational analysis with our machine learning and AI models, and share the information with peers or other journals. In a nutshell, we aspire to be like the GitHub for science,” Wakeford told VentureBeat.

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AI

Nvidia and Booz Allen develop Morpheus platform to supercharge security AI 

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One of the biggest challenges facing modern organizations is the fact that security teams aren’t scalable. Even well-resourced security teams struggle to keep up with the pace of enterprise threats when monitoring their environments without the use of security artificial intelligence (AI).

However, today at the 2022 Nvidia GTC conference, Nvidia and enterprise consulting firm Booz Allen announced they are partnering together to release a GPU-accelerated AI cybersecurity processing framework called the Morpheus platform. 

[Follow along with VB’s ongoing Nvidia GTC 2022 coverage »]

So far, Booz Allen has used Morpheus to create Cyber Precog, a GPU-accelerated software platform for building AI models at the network’s edge, which offer data ingestion capabilities at 300x the rate of CPUs, and boost AI training by 32x and AI inference by 24x. 

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The new solution will enable public and private sector companies to address some of the cybersecurity challenges around closing the cyberskills gap with AI optimized for using GPUs, enabling much more processing to take place than if it was relying on CPUs. 

Finding threats with digital fingerprinting 

Identifying malicious activity in a network full of devices is extremely difficult to do without the help of automation. 

Research shows that 51% of IT security and SOC decision-makers feel their team is overwhelmed by the volume of alerts, with 55% admitting that they aren’t entirely confident in their ability to prioritize and respond to them. 

Security AI has the potential to lighten the loads of SOC analysts by automatically identifying anomalous — or high-risk — activity, and blocking it. 

For instance, the Morpheus software framework enables developers to inspect network traffic in real time, and identify anomalies based on digital fingerprinting. 

“We call it digital fingerprinting of users and machines, where you basically can get to a very granular model for every user or every machine in the company, and you can basically build the model on how that person should be interacting with the system,” said Justin Boitano, VP, EGX of Nvidia. 

“So if you take a user like myself, and I use Office 365 and Outlook every day, and suddenly me as a user starts trying to log in into build systems or other sources of IP in the company, that should be an event that alerts our security teams,” Boitano said. 

It’s an approach that gives the solution the ability to examine network traffic for sensitive information, detect phishing emails, and alert security teams with AI processing powered by large BERT models that couldn’t run on CPUs alone. 

Entering the security AI cluster category: UEBA, XDR, EDR 

As a solution, Morpheus is competing against a wide range of security AI solutions, from user and entity behavior analytics (UEBA) solutions to extended detection and response (XDR) and endpoint detection and response (EDR) solutions designed to discover potential threats.

One of the organizations competing against Nvidia in the realm of threat detection is CrowdStrike Falcon Enterprise, which combines next-gen antivirus (NGAV), endpoint detection and response, threat hunting, and threat intelligence as part of a single solution to continuously and automatically identify threats in enterprise environments.

CrowdStrike recently announced raising $431 million in revenue during the 2022 fiscal year. 

Another potential competitor is IBM QRadar, an XDR solution that uses AI to identify security risks with automatic root cause analysis and MITRE ATT&CK mapping, while providing analysts with support in the form of automated triaging and contextual intelligence. IBM announced raising $16.7 billion in revenue in 2021. 

With Nvidia recently announcing second quarter revenue of $6.7 billion, and now combining the strength of Nvidia’s GPUs alongside Booz Allen’s expertise, the Morpheus framework stands in a unique position to empower enterprises to conduct greater analytic data processing activities at the edge of the network to help supercharge threat detection. 

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AI

Ironclad’s new contract platform embeds AI to improve business workflows

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Ironclad yesterday unveiled a new version of its contract platform embedded with an AI layer in an effort to improve business workflows throughout the lifecycle of a contract.

Organizations can create contracts 60% faster by automating the contract creation process, according to Jason Boehmig, the company’s CEO and co-founder. They will also have the capability to “slice and dice” all the operational data in previously executed contracts, he said. 

“They have a whole mountain of contracts that existed before they worked with us’’ that are static PDFs, Boehmig told VentureBeat. Ironclad Smart Import uses optical character recognition (OCR) to convert PDF files to DOCX when editing documents. The software scans, indexes, tags, and stores contract data at scale. The new platform is designed to make contracts full-text searchable, automate data extraction, and extract key terms — such as renewal dates. 

This way, a company’s customer support team can reach out to a customer to see if they are going to renew a contract, Boehmig explained. “If you miss that, you lose revenue. “Now, [contracts] … are fully living, breathing documents because of the AI analysis that went into tagging them and making them searchable.”

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The AI functionality also makes it possible to recognize who needs to approve a contract and automatically route it to them, he said. 

The new world of contract creation

Also yesterday, Ironclad launched in beta Ironclad Playbooks, which uses AI-powered clause detection so customers can review and negotiate contracts. Playbooks is designed to automatically analyze contracts and flags areas that require a thorough review and provide suggestions on how to negotiate based on legal-approved guidelines, the company said.

Contract creation in the “old world looks like a human with a checklist reading contracts line by line and checking off boxes and making sure every sentence complies with the checklist,” Cai GoGwilt, co-founder and CTO at Ironclad, told VentureBeat. That person would have to do negotiations using a redlining process and go back and forth over email or write things out and scan them in, he added. “The new world is accelerating that” using AI to “intelligently negotiate and review contracts at scale.”

The software scans every part of the contract and matches it to the organization’s preconfigured playbooks and tracks whether every line in the playbook is in compliance and suggests language that can be swapped in, GoGwilt said. “It’s contextual and empowers the user to make better decisions more quickly.” 

“When you’re dealing with 20- to 30-page vendor contracts, the manual review process takes a massive amount of time – but it’s critical work,” said Charles Hurr, associate general counsel at L’Oréal, in a statement. “Ironclad AI automatically reviews these contracts, flags language and clauses that don’t work for us, and suggests L’Oréal-approved provisions to swap in.”

This cuts the review process from hours to minutes, Hurr said, and improves his team’s efficiency, freeing up time for people to focus on more high-impact work.

“Our goal is to keep legal out of 95% of our contracts, and Ironclad’s AI-driven workflows, permission controls, and analytics get us there,” Catherine Choe, director of legal at Everlaw, said in a statement. “Ironclad has helped our team facilitate growth by dramatically speeding up the contract upload and review process, all while maintaining compliance and mitigating risk.”

AI and analytics

Ironclad said the new AI tools come on the heels of the release of Ironclad Insights, a contract analytics and visualization platform. Because Ironclad automatically captures both metadata and process data, Insights is designed to let users create visualizations of crucial operational and business data to make faster decisions, pinpoint bottlenecks, and present findings in a digestible way for key stakeholders.

Pricing for the new platform is based on the number of users, Ironclad said.
Earlier this year, Ironclad announced it had raised $150 million in Series E financing from Franklin Templeton, a global investment management firm, bringing its total financing to $333 million.

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AI

Teradata takes on Snowflake and Databricks with cloud-native platform

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Database analytics giant Teradata has announced cloud-native database and analytics support. Teradata already had a cloud offering that ran on top of infrastructure-as-a-service (IaaS) infrastructure, enabling enterprises to run workloads across cloud and on-premise servers. The new service supports software-as-a-service (SaaS) deployment models that will help Teradata compete against companies like Snowflake and Databricks.

The company is launching two new cloud-native offerings. VantageCloud Lake extends the Teradata Vantage data lake to a more elastic cloud deployment model. Teradata ClearScape Analytics helps enterprises take advantage of new analytics, machine learning and artificial intelligence (AI) development workloads in the cloud. The combination of cloud-native database and analytics promises to streamline data science workflows, support ModelOps and improve reuse from within a single platform. 

Teradata was an early leader in advanced data analytics capabilities that grew out of a collaboration between the California Institute of Technology and Citibank in the late 1970s. The company optimized techniques for scaling analytics workloads across multiple servers running in parallel. Scaling across servers provided superior cost and performance properties compared to other approaches that required bigger servers. The company rolled out data warehousing and analytics on an as-a-service basis in 2011 with the introduction of the Teradata Vantage connected multicloud data platform.

“Our newest offerings are the culmination of Teradata’s three-year journey to create a new paradigm for analytics, one where superior performance, agility and value all go hand-in-hand to provide insight for every level of an organization,” said Hillary Ashton, chief product officer of Teradata.

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Cloud-native competition

Teradata’s first cloud offerings ran on specially configured servers on cloud infrastructure. This allowed enterprises to scale applications and data across on-premise and cloud servers. However, the data and analytics scaled at the server level. If an enterprise needed more compute or storage, it had to provision more servers. 

This created an opening for new cloud data storage startups like Snowflake to take advantage of new architectures built on containers, meshes and orchestration techniques for more dynamic infrastructure. Enterprises took advantage of the latest cloud tooling to roll out new analytics at high speed. For example, Capital One rolled out 450 new analytics use cases after moving to Snowflake. 

Although these cloud-native competitors improved many aspects of scalability and flexibility, they lacked some aspects of governance and financial controls baked into legacy platforms. For example, after Capital One moved to the cloud, it had to develop an internal governance and management tier to enforce cost controls. Capital One also created a framework to streamline the user analytics journey by incorporating content management, project management and communication within a single tool. 

Old meets new

This is where the new Teradata offerings promise to shine. It promises to combine the new kinds of architectures pioneered by cloud-native startups with the governance, cost-controls and simplicity of a consolidated offering. 

“Snowflake and Databricks are no longer the only answer for smaller data and analytics workloads, especially in larger organizations where shadow systems are a significant and growing issue, and scale may play into workloads management concerns,” Ashton said. 

The new offering also takes advantage of Teradata’s various R&D into smart scaling, allowing users to scale based on actual resource utilization rather than simple static metrics. The new offering also promises a lower total cost of ownership and direct support for more kinds of analytics processing. For example, ClearScape Analytics includes a query fabric, governance and financial visibility. This also promises to simplify predictive and prescriptive analytics. 

ClearScape Analytics includes in-database time series functions that streamline the entire analytics lifecycle, from data transformation and statistical hypothesis tests to feature engineering and machine learning modeling. These capabilities are built directly into the database, improving performance and eliminating the need to move data. This can help reduce the cost and friction of analyzing a large volume of data from millions of product sales or IoT sensors. Data scientists can code analytics functions into prebuilt components that can be reused by other analytics, machine learning, or AI workloads. For example, a manufacturer could create an anomaly detection algorithm to improve predictive maintenance. 

Predictive models require more exploratory analysis and experimentation. Despite the investment in tools and time, most predictive models never make it into production, said Ashton. New ModelOps capabilities include support for auditing datasets, code tracking, model approval workflows, monitoring model performance and alerting when models become non-performing. This can help teams schedule model retraining when they start to lose accuracy or show bias.

“What sets Teradata apart is that it can serve as a one-stop shop for enterprise-grade analytics, meaning companies don’t have to move their data,” Ashton said. “They can simply deploy and operationalize advanced analytics at scale via one platform.”

Ultimately, it is up to the market to decide if these new capabilities will allow the legacy data pioneer to keep pace or even gain an edge against new cloud data startups. 

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Game

Samsung’s new Odyssey monitors have its Gaming Hub and Smart Platform built in

We’re starting to see Samsung’s Gaming Hub pop up on more TVs and monitors after the company . Its new Odyssey gaming monitors are the latest models to include the feature, which allows for swift access to a host of cloud gaming services. In fact, the Odyssey G70B and G65B are the company’s first monitors with both Gaming Hub and Smart Platform baked in.

Samsung says the displays offer a way to set up a home office environment without necessarily having a PC on hand. They’re compatible with and . You can also mirror a smartphone to the displays and stream shows and movies from the likes of Netflix and Amazon Prime. Both monitors have a far field voice microphone and voice assistant functions.

The G70B will be available in 28- and 32-inch formats. It has a 144Hz refresh rate and 1ms response time, with a Ultra HD resolution and flat IPS display. It’s certified as and it supports . The G65B also has FreeSync Premium Pro to go along with its QHD curved display. It will have 27- and 32-inch options, a 240Hz refresh rate and 1ms response time. Both monitors include , which offers features such as a zoom-in mode and easy access to YouTube walkthroughs for part of a game you may be struggling with.

Samsung notes that the giant Odyssey Ark monitor also includes Gaming Hub. You’ll from the likes of , NVIDIA GeForce Now, Google Stadia, Utomik and Amazon Luna (the latter’s only available in the US) without any additional hardware other than a compatible controller. The rotatable, 55-inch curved display allows you to view three different apps and inputs simultaneously, so you can stream a game while watching YouTube at the same time.

The Odyssey Ark, which starts at $3,500, is available to pre-order now. Samsung will start offering the G70B and G65B later this year. If you happen to be at , you can check out the displays in person at Samsung’s booth.

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AI

AI and computer vision powers growing shop-and-go platform

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AI and computer vision were not necessarily top-of-mind for Sodexo, a food and facilities management company that runs over 400 university dining programs, which was looking for a future-forward, seamless experience to offer students instead of the usual buffet meal options.

All the company knew is that they wanted something like Amazon Go’s cashierless, shop-and-go stores. That is, where shoppers can walk in, pick items off the shelves, and leave without standing in line at the cashier or suffering through swiping codes at the self-checkout. 

“Students today want things they can partially or fully prepare in their room or apartment, with organic, highly-local options,” said Kevin Rettle, global vice president product development and digital innovation at Sodexo. “We also wanted to remove friction, but many solutions still require the interaction of the guest with a cashier – this generation really doesn’t want to talk to a lot of people in their service interactions.” 

For the University of Denver, Sodexo chose the San Jose-based AiFi, which offers a frictionless and cashierless AI-powered retail solution. Its flexibility (the company says it can deploy two stores per week) and diverse locations (sports stadiums, music festivals, grocery store chains, college campuses and more) make it unique, explained Steve Gu, who cofounded AiFi in 2016 with his wife, Ying Zheng. Both Gu and Zheng have Ph.D.s in computer vision and spent time at Apple and Google.

AiFi, which is powered only by cameras and computer vision technology, announced today that it now boasts a total of 80 checkout-free stores worldwide, partnering with retailers including Carrefour, Aldi, Loop and Verizon. It has also opened 53 Zabka stores in Poland and 2 NFL stores. Gu maintains this is an industry benchmark for how this technology can scale in a way that Amazon Go, which has more than 42 stores, cannot.

Cameras and computer vision, not sensors

Amazon Go’s stores are retrofitted with specialized cameras, sensors, and weighted shelves, Gu explained. “That makes the solution very expensive and hard to scale,” he said. Instead, AiFi uses the “cheapest-possible off-the-shelf cameras,” combined with what he says is the real power: Computer vision. 

AiFi deploys sophisticated AI models through a large number of cameras placed across the ceiling, Gu said, in order to understand everything happening in the shop. Cameras track customers throughout their shopping journey, while computer vision recognizes products and detects different activities, including putting items onto or grabbing items off the shelves.

Beneath the platform’s hood are neural network models specifically developed for people-tracking as well as activity and product recognition. AiFi also developed advanced calibration algorithms that allow the company to re-create the shopping environment in 3D.

AiFi also leverages simulated datasets. “We spend quite a lot of effort building those simulated environments so we can train the AI algorithms and the models inside them,” Gu said. “That really helps us develop those models faster and make them more scalable.” 

In a simulated world, he explained, you can easily adjust human shapes and characteristics, as well as the shelf layout and the look of the product. You can create a cluttered, crowded store environment or one that is neat and orderly. “Things that cannot be done in the real world can be easily done in a simulated world,” he said. “The AI can learn about those scenarios and will then be able to perform or outperform in a real setting.” 

Computer vision that is constantly evolving

AiFi’s system is evolving and will improve over time, Gu continued, citing current challenges including the ability for the platform to recognize small items such as gum or lipstick.

“If they are not placed in the right place, it’s very hard for the computer vision to discern what it is,” he said. There are also issues related to items with similar looks and textures. “If they are placed together in adjacent spaces it sometimes causes confusion for the cameras and computer vision to recognize these products,” he said. “But the good thing is that it’s not purely based on the visual texture – you also have the 3D scene geometry, the location, the context as well.”  

There also are current limitations to the size of the store and the number of people it can track. “The question is can the solution also be scalable to super centers of 100,000 square feet?” he said. “Also, the system is able to track hundreds of people shopping simultaneously in a shop environment. But in order for that to further scale, to track thousands of people, with very complex shopping behavior, that’s something that is still a work in progress.”

To enter an AiFi-powered store, shoppers don’t need a biometric scan or an AiFi app — they can swipe a credit card or use the retailer’s app. At the University of Denver, for example, Sodexo wanted a partner that was agnostic to the front end. “We were able to use our wallet and payment processing, and tie the AiFi technology, the cameras, and the AI into our system,” said Rettle.

Consumer adoption is key

“From a product ownership perspective, you always kind of hold your breath. Is it going to work?” he said. But ultimately, at the University of Denver the students immediately took to the AiFi concept.

“We didn’t have to teach any of the students what to do,” he said. “They get it without having a bunch of prompts.”

Critics in the retail space also predicted the AiFi technology would be a “loss-prevention nightmare — that the students will figure out how to game the system,” Rettle said. Instead, the current accuracy rate for the AiFi solution is 98.3% and the shrink rate (what shoppers walk out without paying for) has actually declined, he said.

Some products don’t quite work yet with AiFi’s solution, Rettle admits, including college and fan “swag.” “The platform still has to understand consumer behavior around that, which will certainly evolve with the technology,” he said.

Rettle also said he doesn’t envision a campus or stadium that could shift to 100% autonomous retail. “For us it’s something that complements,” he said. “But I see a strong future in terms of being able to continue to deploy and drive ubiquity with the solution based on consumer acceptance.”

For Gu, AiFi’s potential is “huge,” with over a dozen new stores in the works and a growing partnership with Microsoft as an independent software vendor partner (AiFi runs its solution on Azure). “You’re going to see a lot of autonomous retail in a variety of verticals — not just stadiums, festivals and universities, but offices, movie theaters and other spaces,” he said.

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AI

AI Dungeon’s creator Latitude launches new Voyage game platform

Latitude, the startup behind text game AI Dungeon, is expanding into a new artificial intelligence-powered game platform called Voyage. The company announced the closed beta on Friday, opening a waitlist for current AI Dungeon users. It’s the next step for a company that began with a university hackathon project, but that ultimately hopes to help other people create their own games using trained AI models.

AI Dungeon, which launched as AI Dungeon 2 in 2019, is powered by OpenAI’s GPT-2 and GPT-3 text generation algorithms. To start, you generate some introductory text or write your own adventure setup. Then you can enter any command you want and a Dungeons & Dragons-style virtual game master will improvise some text describing the outcome. It’s very weird and a lot of fun, but it’s light on traditional game mechanics — more like an interactive fiction engine.

Voyage features more structured games. There’s a Reigns-inspired experiment called Medieval Problems, where you’re the ruler of a kingdom and enter freeform text commands for your advisors, then see the outcome reflected in success ratings. It’s still a lot like AI Dungeon, but with a clearer framing for what you’re supposed to do and a system for evaluating success — although after playing with the game, that system seems pretty forgiving and more than a bit random.

An image from the party game Pixel This

An image from the party game Pixel This

Pixel This, meanwhile, is a party game where one person enters a phrase, the AI generates a pixelated picture of it, and that image slowly increases in resolution until another player guesses it. It’s a bit like the art app Dream paired with a Pictionary-style mechanic.

Latitude CEO Nick Walton describes Voyage as a natural evolution for Latitude. For the company, “AI games are kind of restarting at the beginning” — with text adventures reminiscent of Zork or Colossal Cave Adventure. “Now we’re moving into 2D images where you’ve got some level of visuals in.” AI Dungeon, which is included in Voyage, recently added AI-produced pictures created with the Pixray image generator.

The eventual goal is to add game creation tools, not just games, to Voyage. “Our long-term vision is enabling creators to make things that are dynamic and alive in a way that existing experiences aren’t, and also be able to create things that would have taken studios of a hundred people in the past,” says Walton. There’s no precise roadmap, but Latitude plans to spend the first half of next year working on the system.

Creative tools could help Voyage find a long-term business plan. AI Dungeon is currently free for a set of features powered by GPT-2 and subscription-based for access to the higher-quality GPT-3 algorithm. Following the Voyage beta, Latitude plans to introduce a subscription for it as well.

But Voyage’s new games don’t yet have the versatility or replayability of AI Dungeon — they’re still clearly the products of a company trying to crack games based on machine learning. “This approach is one of the things that I think is going to be really beneficial in terms of being able to iterate and find out what experiences people enjoy,” Walton says. “With traditional games, you can kind of take existing models and create a game that you’re pretty sure people will enjoy. But this space is so different, and it’s hard to necessarily know.” The question is how much people will want to pay to be part of that process.

As Latitude’s mission expands, it will likely need to exercise caution with OpenAI’s application programming interface (API). The organization approves GPT-3 projects on an individual basis, and projects must adhere to content guidelines intended to prevent misuse. Latitude has struggled with these restrictions in the past, since AI Dungeon gives users a lot of freedom to shape their own stories — resulting in some users creating disturbing sexual scenarios that alarmed OpenAI. (It’s also dealt with security issues around user commands.) The startup spent months working on filter systems that accidentally blocked more innocuous fictional content before striking a deal where some user commands would be sent to a non-OpenAI algorithm.

Pixel This and Medieval Problems are more closed systems with fewer obvious moderation risks, but introducing creative tools risks chipping away at OpenAI’s control of GPT-3, which may pose its own set of issues. Walton says that over time, Latitude hopes to shift more of its games onto other algorithms. “We will have some more structure and systems so that it’s not just directly consuming the [OpenAI] API in the same way. And at the same time, I think most of our models will probably be ones that we host ourselves,” he says. That includes models based on emerging open source projects — which have had trouble competing with OpenAI’s work but have advanced since their early days. “I don’t think that gap will be around that long,” says Walton.

Lots of games use procedural generation that remixes developer-created building blocks to create huge quantities of content, and most video game “AI” is a comparatively simple set of instructions. A company like Latitude, by contrast, uses algorithms that are trained to produce text or images fitting a pattern from a data set. (Think of them as super-advanced autocomplete systems.) Right now that can make the resulting experiences highly unpredictable, and their absurdity is often part of their charm — outside gaming, other companies like NovelAI have also harnessed text generation for creative work.

But Latitude is still figuring out how to make systems where players can expect fair and consistent outcomes. Text generation algorithms don’t have any built-in sense of whether an action succeeds or fails, for instance, and systems for making those judgments may not agree with normal human intuition. Image generation algorithms are great for producing weird art, but in a game like Pixel This, players can’t necessarily predict how recognizable a given picture will be.

For now, Latitude’s solution is to lean into the chaos. “If you try and make something super serious with AI where people are going to expect to have a high level of coherence, it’s going to have a hard time, at least until the technology gets better,” Walton says. “But if you kind of embrace that aspect of it and kind of let it be crazier and wacky, then I think you make a fun experience and people get delighted by those surprises.”

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AI

Iterate integrates more visual tools with Interplay 7 low-code platform update for AI

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Multiple companies are vying for a piece of share in the competitive – and growing – low-code AI and ML tools market. The basic concept behind low code is about making it easier for users to build applications without needing to dig into source code development tools. Building out AI and ML integrations is a growing area and the space where San Jose, California-based Iterate is playing. 

The company, which is releasing its Interplay 7 platform today, is firmly focused on AI. The release integrates a new workflow engine that enables users to work with multiple AI nodes to load data for building applications and machine learning models. It also provides streamlined integration with Google Vertex AI and AWS Sagemaker for users that want to send trained data models to those services for additional processing. The overall goal is to help larger companies with a modular approach to building AI/ML powered applications.

“We have realized that  a lot of large companies we work with have many web engineers, but they actually don’t have machine learning backend skills,” Brian Sathianathan, CTO at Iterate told VentureBeat.

Competitive marketplace for low code shows promise for AI

The overall market for low-code development tools is forecast to be worth $16.8 billion in 2022 according to a Gartner research forecast. Gartner expects the total market for low code to reach $20.4 billion by 2023.

Looking out even further, Gartner has forecast that it expects a whopping 70% of all new applications developed by companies will use some form of low-code approach by 2025. In contrast, the analyst firm reported that in 2020 fewer than 25% of organizations were using low-code to build new applications.

The vendor marketplace for low-code technologies is crowded: Sstartup Sway.ai launched its no-code platform for helping users to build AI powered applications in February. In the same month, startup Mage launched its low-code AI dev tool which helps organizations to generate models. Cogniteam announced its latest low-code AI updates on May 9, with a focus on robotics. Other established vendors in the low-code space include Appian and Mendix, both of whom have varying degrees of AI enablement capabilities. 

“Low-code represents the primary growth area for AI-driven application development atop a complicated web of services and data sources,” Jason English, principal analyst at Intellyx LLC told VentureBeat. “Bringing down the technical barrier to entry allows business expertise to be applied for ML training, automation and inference tasks.”

How Iterate aims to differentiate

Brian Sathianathan, CTO at Iterate, told VentureBeat that among the ways his company’s platform differentiates against other low-code technologies in the marketplace is with specific industry use case templates.

For example, rather than just providing a generic tool to help users connect to data sources and build out an AI-enabled application, Interplay provides templates for industry verticals including retail, healthcare, automotive and oil and gas industries. Sathianathan said an organization will typically engage with Iterate to solve a specific use case, though the platform can also be customized to build out applications beyond the available templates as well.

In the Interplay 7 release, Iterative is also improving its data preparation capabilities with a visual tool to clean the data so that it is useful for machine learning training operations. In prior releases. Sathianathan said that data cleaning was not a visual process. 

A primary theme with the Interplay update is to Improve the intersection of what end users see overall. It now integrates with the popular Figma tool that is used to mock up interface designs. Sathianathan said that at large organizations, customer-facing interfaces are often designed by agencies that, more often than not, use Figma. The integration enables Figma designs to be imported into Interplay 7, which can then be connected into the AI backend to build applications.

Looking forward to future releases, Sathianathan said he’s looking to expand the AI capabilities his platform can support for the development of AI techniques such as digital twins.

“Today we support the big four use cases of AI including regression, classification, clustering and image recognition,” he said. “In the future we’re going to add additional capabilities,” he said.

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AI

Hyperscience buys Boxplot to process and store data in one platform

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Hyperscience, a New York-based machine learning company that enables human-centric enterprise automation, today announced the acquisition of Boxplot, a Berlin, Germany-based startup that provides a graph data modeling tool. Boxplot claims it enables companies to visualize and better understand their customers’ data and explore how it relates to other data across their organization.

A report by Gartner shows the challenge of unstructured data in enterprises, with 80% to 90% of all enterprise data in an unstructured format. Gartner’s Magic Quadrant for Distributed File Systems and Object Storage also estimates large enterprises will triple their unstructured data capacity stored as file or object storage on-premises, at the edge, or in the public cloud by 2026. Gartner also reports that end-users further indicate a 30% year-over-year growth in unstructured data.

With more unstructured data in the world today, enterprises are seeking modern data tools to stay above the dark data problem. Hyperscience says its machine learning platform empowers organizations to read and understand unstructured data at scale — causing a tenfold acceleration of their processing times. The company claims that the combination of both technologies from Hyperscience and Boxplot will drive faster and improved decisions for organizations — such as how to price insurance, who to give a mortgage to, which invoices to pay out, and where fraud exists.

In a press release, Hyperscience reported that the deal marks the company’s first acquisition. In an interview with VentureBeat, Peter Brodsky, CEO, and cofounder of Hyperscience, shared more context on the acquisition and detailed the complex capabilities and intricacies of both companies’ technologies.

Elevating organizational agility and improving customer experience

An article published earlier this year by McKinsey highlights quick answers to strategic questions in the decision-making process as “one of the biggest advantages of an automated, data-driven AI system.” Brodsky said. The goal of Hyperscience’s acquisition of Boxplot is to enable the company to offer a single platform that processes and stores data — a solution that he claims will “elevate organizational agility and improve customer experience across enterprises.”

To foster the company’s vision of human-centered automation, Brodsky noted that Hyperscience wants to help companies turn antiquated business processes into modern, highly automated, flexible digital assembly lines that are deeply human-centric.

“Our human-centered automation means we want people involved every step of the way. We embrace the way people work, so our automation bends to the needs of people rather than the other way around,” Brodsky said. “What Boxplot, the company that we are acquiring, does is that it stores data, not how machines are typically used to store data, but in the way that data actually is in the real world. And so, it’s a much closer representation of our human-centric automation. And that’s really the magic fit between the two companies: We do the processing, while they do the modeling and storage of the data.”

Hyperscience asserts that it will help companies deliver better outcomes to their customers through data organization. “Organizing data into customer-defined graphs (i.e., networks of information, where each piece of data is linked by a relationship to other pieces of data) is important when an organization wants to deploy multiple business processes, as this data will often be shared,” the company’s press release stated. “For example, when processing an insurance claim, it’s important to retrieve the claimant’s policy. That claim needs to be linked to a person, who is in turn linked to a policy.”

Many customers currently perform that lookup manually by diving in and out of various record systems, the company noted, adding that “Boxplot will provide the backplane for business process interoperability, while simultaneously making Hyperscience a system of record, enabling more automation and better machine learning performance.”

A human-centric differentiation for data storage

Brodsky noted that the company’s human-centric focus is precisely what differentiates it from its competitors in the industry. Rather than make people adhere to the way machines work, Hyperscience has gone the other way, programming machines to operate on human-readable data including emails, Microsoft Word documents, PDFs, and more.

“This is how people in businesses communicate with each other and do work. Rather than have people sitting inside a database or a CRM, we let people work the way they work, and the automation we provide works with people’s exact kind of data,” Brodsky said. “The key observation we’ve made is that it’s very hard to change people. It’s much easier to build AI that works the same way or in comparable ways as people do.”

According to Brodsky, Hyperscience’s machine-centric competitors fall into two categories:

  1. Companies that provide retrofit automation, layering automation on top of business processes they take from existing technologies.
  2. Companies that try to deal with human content using legacy technologies like OCR and speech recognition, to get out of the human layer and immediately back into the machine layer.

“Our technology is free from the traditional way of approaching data storage and retrieval, which is typically tables and columns, and very clumsy CRM that’s often not flexible,” he added.

One of Hyperscience’s largest competitors is  DataBank, a Pennsylvania-based technology company that “provides automation business intelligence and enterprise software consulting.” Another major competitor is Decisions, “a company offering a business rule automation and workflow management platform that helps in business optimization.” Some other competitors include Pipefy, Akkio, and Amazon Textract.

The synergy between Hyperscience and Boxplot 

Boxplot is similar to Hyperscience, except instead of processing data, it focuses on data storage, modeling the relationships between the data — according to Brodsky. “Where the synergy lies between what we’ve built and what Boxplot has built is that we’re very good at understanding data, but we don’t record it in its natural form in any way. What Boxplot enables us to do is structure data and preserve it with other pieces of software it can easily operate on.”

“Boxplot fits into our vision for how automation will play out in the future. This acquisition represents an important step forward for us and for the market. The team coming on board will fit well into Hyperscience, and continue to drive us forward to our human-centered automation vision,” Brodsky said.

“Hyperscience and Boxplot complement each other’s technological innovations and like-minded vision for organizations of the future. Their market-leading automation and machine learning capabilities for data structuring, combined with our graph-based enterprise operating system, will be a powerful combination,” Fabian Schmidt-Jakobi, CEO and cofounder of Boxplot,  said in the press release.”

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Automated accounts payable platform Tipalti raises $270M

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Tipalti, a platform used by major enterprises to automate common accounts payable tasks, has raised $270 million in a series F round of funding, valuing the company at a cool $8.3 billion.

Accounts payable (AP) refers to any money owed by a company to its various suppliers. Processing and reviewing all internal and external transactions (e.g., paying invoices and reimbursing expenses), ensuring that all liabilities are met, is a resource-intensive process — one that typically requires a lot of manual data capture and management across various internal systems.

The accounts payable software market was pegged as a $8.77 billion market last year, a figure that’s predicted to more than double within seven years. And with Tipalti’s valuation more than quadrupling from the $2 billion at its previous fundraise last year, this serves to underscore the size of the pie Tipalti is chasing. Cofounder and CEO Chen Amit said that Tipalti’s target market constitutes nearly 700,000 companies, with only 4% of that currently penetrated.

“The addressable market is large, as solutions for payables and finance operations are not widely adopted, and none are as integrated as Tipalti’s approach,” Amit told VentureBeat. “Many organizations still struggle with manual processes. The pandemic’s impact on remote work and scalability issues also accelerated the need to turn finance processes into digital workflows — a trend that will not be reversible.”

Payments automation

Founded in 2010, Tipalti offers tools that enable companies such as Twitter, GoDaddy, and Twitch to automate most of their AP tasks, spanning invoice management, supplier management, a purchase order (PO) matching, payment reconciliation, tax compliance, fraud detection, and more. With invoices, for example, suppliers can upload their bills either through Tipalti’s portal or by email, and track the progress online. On the AP (i.e., payer’s) side, optical character recognition (OCR) serves to remove manual data entry, so that the details within all invoices are automatically extracted ready for review.

This automated workflow includes various smarts such as duplicate invoice alerts, which help ensure that a company doesn’t inadvertently pay the same invoice twice. And Tipalti also leans on machine learning (ML) to improve over time, so that if it detects frequent manual data overrides carried out by someone in AP, it will apply that similar logic to future invoices.

Elsewhere, Tipalti also uses historical and real-time data to carry out risk checks on payees — this includes establishing whether they are connected in any way to other blocked payees, for example, or whether there are multiple different accounts with the same associated payment or contact details.

High-velocity enterprises

Automation is playing an increasingly bigger role in the financial services and software sphere, with countless companies getting in on the act. Back in October, Stripe acquired Recko, a platform that automates the payments’ reconciliation process by comparing internal accounting records against external bank statements to ensure there are no discrepancies. And in the past year, we’ve seen businesses such as automated spend-management platform Ramp raise gargantuan sums at billion-dollar valuations.

Tipalti, for its part, had raised some $295 million before now, including its $150 million series E round last October. Today, the San Mateo, California-based company claims that it’s processing more than $28 billion in annual payments for two thousand-plus customers, representing a 100% year-on-year growth.

According to Amit, Tipalti’s focus is more on fast-growing, “high-velocity” enterprises, because they don’t want or have the kind of expenses and resources that larger enterprises typically consume on maintaining complex architectures, often constituting a mix of custom integrations and IT outlays.

“The key challenge our customers face is that they themselves would rather focus on something else — the product, sales, customer experience, and so on — than on the back-office and suppliers,” Amit explained. “And the back office must keep up with and enable the front office’s growth goals. They’re more modern in thinking, and adopt best-in-class, highly scalable solutions that don’t require a lot of maintenance.”

With another $270 million in the bank from backers including lead investor G Squared and funds managed by Morgan Stanley’s Counterpoint Global, the company is well-positioned to “accelerate its product roadmap” and global expansion plans. This will include rolling out new ways to manage spending through a corporate credit card, as well as a feature that will help teams “use invoices as a point of social engagement,” according to Amit. This will be less about morphing into a social network than it will be about making it easier to glean answers from across an organization around “specific areas of spend.”

Looking further into the future, Amit said that the company plans to look beyond accounts payable. “We’ll be developing more product offerings that improve finance operations even more — right now, we’re focused on accounts payable as it is the least efficient process in finance, but we’re also expanding into other areas with the same approach,” Amit explained.

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