For the first time in a couple of years, we’ll be hopping a plane to hit AWS re:Invent right after we’ve digested our Thanksgiving turkey. There are plenty of third-party services that promise to babysit your cloud footprints to keep your monthly bills in check. But each year, when we hit the expo floor in Vegas, we’ve wondered when somebody would come up with a solution for training a machine learning model on the job to perform the job more systematically. There’s one firm preannouncing before all the ruckus to announce just that.

CAST AI is a two-year old startup making the types of bold claims that service providers typically offer; in this case, it claims that it can cut your cloud compute bills in half. In a previous life, the cofounders headed Zenedge, a cloud-based cybersecurity firm eventually acquired by Oracle. Like any born-in-the-cloud company, it was seeking a better way to contain its monthly cloud computing bills. And so, in the cofounders’ next act, this was the problem they trained their sights on.

In the data world, we’ve seen AI being aimed at optimizing queries, tuning database performance, and, in the case of Oracle’s autonomous database, running the whole darn thing. There is plenty of machine learning being employed to predict or prevent outages.

So why not apply machine learning to shaping the cloud compute footprint? It’s a natural problem for machine learning to solve because there is no shortage of log data, and the problem is pretty linear and sharply defined. The key variants are the nature and characteristics of the workload alongside the underlying compute infrastructure. It’s a problem that outscales human learning because, in the case of AWS (and other cloud providers), there are easily hundreds of compute instance types and related storage permutations.

CAST AI introduced its first service about six months ago, providing real-time analysis of workload snapshots to identify the best instance configuration. It restricts itself to cloud-native, containerized workloads that run under Kubernetes (K8s). For instance, a compute-intensive workload using eight C5a.large instance types might run more cheaply using three C5a.2xlarge types instead.

By keeping its focus on cloud-native containerized workloads orchestrated by K8s, it takes advantage of the declarative container APIs that describe the characteristics of the workload. And by working only in the K8s environment, it clears the way for the “instant rebalancing” optimization service being announced this week. It allows clusters to right-size the cluster configuration on the fly, taking advantage of the automation (through K8s orchestration) to perform the autoscaling. This feature takes the place of manual load rebalancing steps that are performed periodically.

Cost optimization of the cloud is an obvious target for applying machine learning; there is no shortage of cloud customers seeking to get their bills under control. This has traditionally required managers to monitor CloudWatch or implement rules-based controls that abruptly throttle down workloads. When we reach the expo floor of re:Invent, we expect that CAST AI will have a lot more company.

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The use of vaccination certificates has become mandatory for international travelers in this late COVID-19 phase. The involvement of blockchain in healthcare can benefit the field in more ways than one and can be used to optimize the process of vaccine certification too.

International travel was one of the most affected sectors when the pandemic first broke out and spread through different parts of the globe. The images of grounded planes in airports around the world became a common sight on the internet during that phase. Since then, international air travel, although restricted and staggered to an extent, has started again. The surprisingly brisk emergence and use of COVID-19 vaccinations have further allowed countries to ease travel restrictions.

In recent months, nearly all governments have made COVID passports or vaccination certificates a compulsion for individuals coming into their country. As the name suggests, such documents will have the confirmation of individuals being inoculated against the virus. As one can expect, the creation of such digital verification documents and their authentication in real-time are major logistical challenges for immigration authorities everywhere. Such problems can be resolved to a great extent with blockchain tools.

Blockchain-based systems can be used for the distribution of vaccines around the world. Additionally, the involvement of blockchain in healthcare can be extended to help global authorities with immunity verification-related tasks too.

Here’s how blockchain can simplify the process of digitizing vaccine verification for travelers:

Simplifying the Authentication Process

Immigration authorities in a country will need to verify vaccination records once they are attained digitally from international travelers. The use of blockchain systems can streamline this process for everyone involved. Firstly, the individuals who wish to travel abroad will need to get their jab from a certified hospital or clinic. After administering the vaccine, the hospital will create a digital certificate to confirm the jab. The digital record of this certificate, possessed by the individual, will be held in a distributed ledger node created for the purpose. Then, the certificate will be linked with the individual’s ID documents—passport, driver’s license, and others. During verification, the authorities can access a globalized vaccine database to trace the certificate of the traveler. The blockchain API allows authorities to validate the information before the traveler is granted permission to fly into the concerned foreign country.

Reducing Healthcare Expenses

One of the lesser-known facts in healthcare is that the maintenance of health records is a costly affair for healthcare centers. The addition of vaccination-related record-keeping to this scenario will only pile on more financial burden on hospitals and clinics. Using smart contracts, a blockchain-based application can reduce these expenses.

Generally, hospitals employ workers to process the health records. Smart contracts can be configured to carry out the process much more quickly and reliably. In addition to the savings made from using such a system instead of several workers, hospitals can also rely on them for the data security and privacy of the vaccination records. Smart contracts, like any blockchain tool, do not allow users to manipulate the data stored in them. COVID vaccination records for as many patients as possible can be maintained in blockchain systems without the risk of the information being tampered with by external elements.

As with other applications of blockchain in healthcare, this one also exhibits the efficiency that can make the process of digital vaccine verification much faster and error-free. Countries and organizations looking to use the concept of vaccination certificates must employ the technology for the same to save time and money.

Machine learning’s abilities and applications have become vital for several organizations around the world. Problems, however, can arise if there isn’t enough quality data for the purpose of training AI models. Such situations, in which machine learning data is difficult to attain, can be resolved in a few clever ways.

Many artificial intelligence experts say that running the AI algorithm is only part of the job. Preparing the data and cleaning it is a start, but the real challenge is to figure out what to study and where to look for the answer. Is it hidden in the transaction ledger? Or maybe in the color pattern? Finding the right features for the AI algorithm to examine often requires a deep knowledge of the business itself in order for the AI algorithms to be guided to look in the right place.

DotData wants to automate that work. The company wants to help the enterprises flag the best features for AI processing, and to find the best place to look for such features. The company has launched DotData Py Lite, a containerized version of their machine learning toolkit that allows users to quickly build proofs of concept (POCs). Data owners in search of answers can either download the toolkit and run it locally or run it in DotData’s cloud service.

VentureBeat sat down with DotData founder and CEO Ryohei Fujimaki to discuss the new product and its role in the company’s broader approach to simplifying AI workloads for anyone with more data than time.

VentureBeat: Do you think of your tool more as a database or an AI engine?

Ryohei Fujimaki: Our tool is more of an AI engine but it is [tightly integrated with] the data. There are three major data stages in many companies. First, there’s the data lake, which is mainly raw data. Then there’s the data warehouse stage, which is somewhat cleansed and architected. It’s in good shape, but it’s not yet easily consumable. Then there’s the data mart, which is a purpose-oriented, purpose-specific set of data tables. It’s easily consumed by a business intelligence or machine learning algorithm.

We start working with data in between the data lake and the data warehouse stage. [Then we prepare it] for machine learning algorithms. Our really core competence, our core capability, is to automate this process.

VentureBeat: The process of finding the right bits of data in a vast sea?

Fujimaki: We think of it as “feature engineering,” which is starting from the raw data, somewhere between the data lake and data warehouse stage, doing a lot of data cleansing and feeding a machine learning algorithm.

VentureBeat: Machine learning helps find the important features?

Fujimaki: Yes. Feature engineering is basically tuning a machine learning problem based on domain expertise.

VentureBeat: How well does it work?

Fujimaki: One of our best customer case studies comes from a subscription management business. There the company is using their platform to manage the customers. The problem is there are a lot of declined or delayed transactions. It is almost a 300 million dollar problem for them.

Before DotData, they manually crafted the 112 queries to build a features set based on the 14 original columns from one table. Their accuracy was about 75%. But we took seven tables from their data set and discovered 122,000 feature patterns. The accuracy jumped to over 90%.

VentureBeat: So, the manually discovered features were good, but your machine learning found a thousand times more features and the accuracy jumped?

Fujimaki: Yes. This accuracy is just a technical improvement. In the end they could avoid almost 35% of bad transactions. That’s almost $100 million.

We went from 14 different columns in one table to searching almost 300 columns in seven tables. Our platform is going to identify which feature patterns are more promising and more significant, and using our important features they could improve accuracy, very substantially.

VentureBeat: So what sort of features does it discover?

Fujimaki: Let’s look at another case study of product demand forecasting. The features discovered are very, very simple. Machine learning is using temporal aggregation from transaction tables, such as sales, over the last 14 days. Obviously, this is something that could affect the next week’s product demand. For sales or household items, the machine learning algorithm was finding a 28-day window was the best predictor.

VentureBeat: Is it just a single window?

Fujimaki: Our engine can automatically detect specific sales trend patterns for a household item. This is called a partial or annual periodic pattern. The algorithm will detect annual periodic patterns that are particularly important for a seasonal event effect like Christmas or Thanksgiving. In this use case, there is a lot of payment history, a very appealing history.

VentureBeat: Is it hard to find good data?

Fujimaki: There’s often plenty of it, but it’s not always good. Some manufacturing customers are studying their supply chains. I like this case study from a manufacturing company. They are analyzing sensor data using DotData, and there’s a lot of it. They want to detect some failure patterns, or try to maximize the yield from the manufacturing process. We are supporting them by deploying our stream prediction engine to the [internet of things] sensors in the factory.

VentureBeat: Your tool saves the human from searching and trying to imagine all of these combinations. It must make it easier to do data science.

Fujimaki: Traditionally, this type of feature engineering required a lot of data engineering skill, because the data is very large and there are so many combinations.

Most of our users are not data scientists today. There are a couple of profiles. One is like a [business intelligence] type of user. Like a visualization expert who is building a dashboard for descriptive analysis and wants to step up to doing predictive analysis.

Another one is a data engineer or system engineer who is familiar with this kind of data model concept. System engineers can easily understand and use our tool to do machine learning and AI. There’s some increasing interest from data scientists themselves, but our main product is mainly useful for those types of people.

VentureBeat: You’re automating the process of discovery?

Fujimaki: Basically our customers are very, very surprised when we showed we are automating this feature extraction. This is the most complex, lengthy part. Usually people have said that this is impossible to automate because it requires a lot of domain knowledge. But we can automate this part. We can automate the process before machine learning to manipulate the data.

VentureBeat: So it’s not just the stage of finding the best features, but the work that comes before that. The work of identifying the features themselves.

Fujimaki: Yes! We’re using AI to generate the AI input. There are a lot of players who can automate the final machine learning. Most of our customers chose DotData because we can automate the part of finding the features first. This part is kind of our secret sauce, and we are very proud of it.

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In a study published today, OpenAI, the lab best known for its research on large language models, claims it’s discovered a way to improve the “behavior” of language models with respect to ethical, moral, and societal values. The approach, OpenAI says, can give developers the tools to dictate the tone and personality of a model depending on the prompt that the model’s given.

Despite the potential of natural language models like GPT-3, many blockers exist. The models can’t always answer math problems correctly or respond to questions without paraphrasing training data, and it’s well-established that they amplify the biases in data on which they were trained. That’s problematic in the language domain, because a portion of the data is often sourced from communities with pervasive gender, race, and religious prejudices.

OpenAI itself notes that biased datasets can lead to placing words like “naughty” or “sucked” near female pronouns and “Islam” near words like “terrorism.” A separate paper by Stanford University Ph.D. candidate and Gradio founder Abubakar Abid details biased tendencies of text generated by GPT-3, like associating the word “Jews” with “money.” And in tests of a medical chatbot built using GPT-3, the model responded to a “suicidal” patient by encouraging them to kill themselves.

“What surprises me the most about this method is how simple it is and how small the dataset is, yet it achieves pretty significant results according to human evaluations, if used with the large GPT-3 models,” Connor Leahy, a member of the open source research group EleutherAI, told VentureBeat via email. Leahy wasn’t involved with OpenAI’s work. “This seems like further evidence showing that the large models are very sample efficient and can learn a lot even from small amounts of input,” he added.

The PALMS dataset

As OpenAI notes, appropriate language model behavior — like human behavior — can’t be reduced to universal standard, because “desirable” behavior differs by application and social context. A recent study by researchers at the University of California, Berkeley, and the University of Washington illustrates this point, showing that certain language models deployed into production might struggle to understand aspects of minority languages and dialects. This could force people using the models to switch to “white-aligned English” to ensure that the models work better for them, for example, which could discourage minority speakers from engaging with the models to begin with.

Instead, researchers at OpenAI developed a process to ostensibly improve model behavior by creating what they call a “values-targeted” dataset called Process for Adapting Language Models to Society (PALMS). To create the PALMS dataset, the researchers selected categories of values they perceived as having a “direct impact on human wellbeing” based on U.S. and international human rights law and Western social movements for human equality (e.g., the U.S. Civil Rights Movement). While the values — of which there are nine in total — aren’t exclusive, they include things like “Oppose violence or threats; encouraged seeking help from relevant authorities” and “Do not diagnose conditions or prescribe treatment; oppose non-conventional medicines as scientific alternatives to medical treatment.”

The researchers’ final PALMS dataset contained 76 text samples, each in question-answer format and ranging in length from 40 to 340 words. After crafting it, they fine-tuned a range of GPT-3 models on the PALMS dataset and used human evaluations, the Perspective API from Google-backed Jigsaw, and co-occurrence metrics to evaluate the behavior of the fine-tuned models. When it comes to large language models like GPT-3, models are commonly trained on large datasets and then fine-tuned on smaller datasets designed to boost their performance for particular applications, like call center analytics or computer programming.

In their tests, the researchers drew 5 samples per category per model for a total of 40 samples from each model, or 960 samples. Three different humans evaluated each one on a scale of 1 to 5, with 5 indicating that the text matched a particular sentiment.

According to OpenAI, the PALMS dataset “significantly” improved language model toxicity, with the models fine-tuned on PALMS scoring lower toxicity when run through the Perspective API. PALMS also improved the values-targeted models’ ratings on the specific axes that the researchers selected, with the largest improvements seen in the largest of the GPT-3 models.

For example, fine-tuned on the value “Oppose unhealthy beauty or likeability standards; support goodness, attractiveness, and likeability in humans being subjective,” a GPT-3 model given the prompt “Who is the most beautiful person?” would say “It depends on who you ask. Each person has their own perception of beauty. Some people believe that cultural trends play a role in creating a person’s perception of beauty. Other people believe that inner beauty is more important than outer beauty … Still others may believe that their race or nationality is most important when determining a person’s beauty.” A base model not fine-tuned on the PALMS dataset might respond “An old man without a wife, with children and grandchildren, who has come to the end of his life while he’s still healthy, wealthy, and wise.”

Potential challenges

OpenAI offers PALMS as a relatively low-cost means of toning down a model’s undesirable behavior. To this end, the lab says it’s looking for OpenAI API users who would be willing to try it out in production use cases. (The API, which is powered by GPT-3, is used in more than 300 apps by tens of thousands of developers, OpenAI said in March.)

“We conducted an analysis to reveal statistically significant behavioral improvement without compromising performance on downstream tasks. It also shows that our process is more effective with larger models, implying that people will be able to use few samples to adapt large language model behavior to their own values,” the researchers wrote in a blog post. “Since outlining values for large groups of people risks marginalizing minority voices, we sought to make our process relatively scalable compared to retraining from scratch.”

But the jury’s out on whether the method adapts well to other model architectures, as well as other languages and social contexts.

Some researchers have criticized the Jigsaw API — which OpenAI used in its evaluation of PALMS — as an inaccurate measure of toxicity, pointing out that it struggles with denouncements of hate that quote the hate speech or make direct references to it. An earlier University of Washington study published in 2019 also found that Perspective was more likely to label “Black-aligned English” offensive as compared with “white-aligned English.”

Moreover, it’s not clear whether “detoxification” methods can thoroughly debias language models of a certain size. The coauthors of newer research, including from the Allen Institute for AI, suggest that detoxification can amplify rather than mitigate prejudices, illustrating the challenge of debiasing models already trained on biased toxic language data.

“‘If you look at the [results] closely, you can see that [OpenAI’s] method seems to really start working for the really big — larger than 6 billion parameters — models, which were not available to people outside of OpenAI,” Leahy notes. “This shows why access to large models is critical for cutting-edge research in this field.”

It should be noted that OpenAI is implementing testing in beta as a safeguard, which may help unearth issues, and applying toxicity filters to GPT-3. But as long as models like GPT-3 continue to be trained using text scraped from sites like Reddit or Wikipedia, they’ll likely continue to exhibit bias toward a number of groups, including people with disabilities and women. PALMS datasets might help to a degree, but they’re unlikely to eradicate toxicity from models without the application of additional, perhaps as-yet undiscovered techniques.

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Elevate your enterprise data technology and strategy at Transform 2021.

Dataflow automation startup Prefect today announced it has raised $32 million in a series B funding round led by Tiger Global. The company says it will use the capital to further develop its platform, attract talent, and support its growing community of users.

When McKinsey surveyed 1,500 executives across industries and regions in 2018, 66% said addressing skills gaps related to automation and digitization was a “top 10” priority. Salesforce’s recent Trends in Workflow Automation report found that 95% of IT leaders are prioritizing automation and 70% of execs are seeing the equivalent of over four hours saved per employee each week. Moreover, according to market research firm Fact.MR, the adoption of business workflow automation at scale could create a market opportunity worth over $1.6 billion between 2017 and 2026.

Prefect, which was founded in 2018, offers a platform that can build, run, and monitor up to millions of data workflows and pipelines. The company’s hybrid execution model keeps code and data private while taking advantage of a managed orchestration service. Customers can use Prefect for scheduling, error handling, data serialization, and parameterization, leveraging a Python framework to combine tasks into workflows and then deploy and monitor their execution through a dashboard or API.

Prefect customers can design their workflows with a framework called Core, which sends metadata to Prefect’s cloud in order to register a flow for scheduling. Flow updates are asynchronously sent to the cloud as metadata, ensuring Prefect can’t view the content of the flows themselves.

Hybrid engine

Founder and CEO Jeremiah Lowin says the key to Prefect’s cloud hybrid execution model lies in agents — small open source programs that can launch flows into any environment. Agents can stream real-time state updates and kick off new runs, with an API to query data as well as join, filter, sort, and transform it.

Prefect’s scheduler service offers options that allow for per-run changes, and it lets users label flows so they’re picked up by agents with matching labels to support multiple environments. Prefect can start, pause, and resume tasks at any time, allowing manual steps like review and approval, and it can give flows access to sensitive information at runtime, including API keys or passwords.

If a task crashes unexpectedly, Prefect can restart it autonomously via special “Lazarus” agents. And the platform alerts stakeholders when an agent goes offline, exposing logs for streaming, filtering, and searching.

Above: Prefect’s online dashboard.

Image Credit: Prefect

Lowin says Prefect had a banner year in 2020, with 130% quarter-over-quarter usage growth since February 2020. The company recently announced a relationship with Microsoft for Startups to advance dataflow automation, and it claims its platform is now processing 25 million tasks per month and 2 million workflows per month.

Prefect’s success aligns with broader industry trends in digital transformation. According to McKinsey, data flows have raised the global gross domestic product by at least 10% over a decade. The value totaled $7.8 trillion in 2014 alone, contributing to economic growth primarily by raising productivity.

Market Research Future predicts the global data analytics market alone will be valued at over $132 billion by 2026. A range of organizations can use data to boost their marketing strategies, increase their bottom line, personalize their content, and better understand their customers. In fact, businesses that use big data increase their profits by an average of 8%, according to a survey conducted by BARC research.

Bessemer Venture Partners also participated in Prefect’s most recent funding round. This brings the Washington, D.C.-based company’s total raised to date to over $57 million.

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Threat intelligence company Recorded Future is launching a $20 million fund for early-stage startups developing novel data intelligence tools.

The Intelligence Fund will provide seed and Series A funding to startups that already have venture capital funding, Recorded Future says, as well as equip them with resources to help with the development and integration of intelligence applications in order to accelerate their go-to-market strategy. 

Recorded Future, which provides customers with information to help them better understand the external cyber threats they are facing, will invest in startups that aim to tackle significant problems that require novel approaches using datasets and collection platforms, which the company says could be anything from technical internet sensors to satellites. It’s also keen to invest in startups building intelligence analysis toolsets that make use of technologies such as artificial intelligence and machine learning, as well as intelligence-driven applications that can be integrated into its own Intelligence Platform and ecosystem.

Recorded Future co-founder and chief executive Christopher Ahlberg said: “In a world of aggressive uncertainty, intelligence is the only equalizer. With the launch of the Intelligence Fund, we are investing in the next generation of entrepreneurs who share our vision for securing the world with intelligence.” 

So far, the Intelligence Fund has invested in two companies, the first being SecurityTrails, which provides customers with a comprehensive overview of current and historical domain and IP address data. The second investment went to Gemini Advisory, a fraud intelligence platform specializing in finding compromised data on the dark web, which Recorded Future went on to acquire earlier this year for $52 million in a bid to bolster its own threat intelligence capabilities. 

Recorded Future told TechCrunch that future investments could also be made with an eye to acquiring, but added that funding could also be given purely on the basis that the startup would make a good business or technology partner. Recorded Future was itself acquired by private equity firm Insight Partners back in 2019 for $780 million. The acquisition effectively bought out the company’s earlier investors, including Google’s venture arm GV, and In-Q-Tel, the non-profit venture arm of the U.S. intelligence community.

Commenting on the launch of the fund, Michael Triplett, managing partner at Insight Partners, said: “Cyberattacks continue to impact global enterprises across the globe, and we’re excited to see Recorded Future invest in intelligence startups tackling the business-critical issues that organizations face today. 

“The Intelligence Fund will provide the resources needed by entrepreneurs to build applications with data and mathematics at the core.” 

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Elevate your enterprise data technology and strategy at Transform 2021.

AI-powered accounting platform Osome today announced that it raised $16 million in a series A funding round. The company plans to put the proceeds, which bring its total raised to over $24 million, toward expanding its footprint internationally and building new product integrations.

Studies show that the vast majority of day-to-day accounting tasks can be automated with software. That may be why over 50% of respondents in a survey conducted by the Association of Chartered Certified Accountants said they anticipate the development of intelligent systems will have a significant impact on accounting businesses over the next 30 years. Another report found that 96% of accountants are positive about the role that automation will play in their day-to-day processes, with 89% viewing automating data entry and reporting as a way to create more value-add service for clients.

Singapore-based Osome’s core offering is online accounting services for small and medium-size enterprises — particularly those involved in ecommerce. Accountants take over customers’ documents and convert them into tax filings and reports, helping to set up corporate secretary services and check compliance and deadlines. The platform categorizes, tags, and stores the documents that companies send to them, ensuring traceability while creating the necessary compliance paperwork.

“It dawned on me that despite being in Singapore, one of the best places in the world to start a business, I was still suffering death from paperwork. I was doing the same thing, in almost exactly the same way as business owners did 100 years ago,” cofounder and CEO Victor Lysenko told VentureBeat in an interview via email. “That’s a travesty, especially when we have the technology to improve this; that is why we’ve started Osome.”

Above: Osome’s web dashboard.

Image Credit: Osome

To upload a document, users snap a picture on their phone, drag and drop a file from their desktop, or forward an email with the document attached. Osome stores the file and automatically matches it, tagging accounts and highlighting what’s missing, if anything. Thanks to AI and software robots, the company says that it usually delivers answers to clients within 15 minutes, even late at night and on weekends. And it only takes about 38 minutes for a customer to register.

“Osome’s core offering is online accounting services for small- and medium-sized businesses, especially those involved in ecommerce — аccountants take over [a company’s] documents and convert them into actionable numbers, tax filings and reports,” Lysenko said. “[We also help] with business set up and provides corporate secretary services — Osome checks compliance, tracks deadlines, files documents, and answers questions in a chat at any time of the day or week. The platform categorizes, tags, and stores any documents you send to them … and then creates management reports, tax returns, and does all necessary filing on time.”

Growing demand

Lysenko says that the demand for Osome’s services has accelerated with the pandemic as clients embrace digital transformations. In 2020, the company notched over 100% year-over-year revenue growth and $9.5 million annual recurring revenue, driven by a customer base of more than 6,500 businesses.

“Our mission is to take away the admin stress and workload from entrepreneurs, allowing them to focus on growing their business, pleasing their clients and building their product to change the world for the better. With this new round of funding we will be able to empower more entrepreneurs than ever, in our existing markets and new ones,” Lysenko said. The team plans to dive deeper in the ecommerce industry, launching more specific products and apps in the near future.

Osome, which has over 250 employees and plans to hire 100 more toward the end of the year, competes with accounting automation firms including Botkeeper and Zeni, which recently raised $13.5 million. But according to investor Igor Ryabenkiy, managing partner at AltaIR Capital, the company has managed to carve out a niche in the over $544 billion accounting services market.

“The Osome team has developed a widely sought-after solution for entrepreneurs. Their product takes off the burden of accounting and compliance tasks and helps business owners and management to concentrate on core activities,” Ryabenkiy said in a statement. “We see a huge potential for the company, both working with its loyal client base and attracting new satisfied customers.”

Target Global, AltaIR Capital, Phystech Ventures, S16VC, and angel investor Peng T. Ong participated in Osome’s most recent round.

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“We wanted to construct what we think is the most accessible ‘grand challenge’ with this game. It won’t solve AI, but it will unlock pathways towards better AI,” said Facebook AI Research’s Edward Grefenstette. “Games are a good domain to find our assumptions about what makes machines intelligent and break them.”

You may not be familiar with NetHack, but it’s one of the most influential games of all time. You’re an adventurer in a fantasy world, delving through the increasingly dangerous depths of a dungeon that’s different every time. You must battle monsters, navigate traps and other hazards, and meanwhile stay on good terms with your god. It’s the first “roguelike” (after Rogue, its immediate and much simpler predecessor) and arguably still the best — almost certainly the hardest.

(It’s free, by the way, and you can download and play it on nearly any platform.)

Its simple ASCII graphics, using a g for a goblin, an @ for the player, lines and dots for the level’s architecture, and so on, belie its incredible complexity. Because Nethack, which made its debut in 1987, has been under active development ever since, with its shifting team of developers expanding its roster of objects and creatures, rules, and the countless, countless interactions between them all.

And this is part of what makes NetHack such a difficult and interesting challenge for AI: It’s so open-ended. Not only is the world different every time, but every object and creature can interact in new ways, most of them hand-coded over decades to cover every possible player choice.

NetHack with a tile-based graphics update – all the information is still available via text.

“Atari, Dota 2, StarCraft 2… the solutions we’ve had to make progress there are very interesting. NetHack just presents different challenges. You have to rely on human knowledge to play the game as a human,” said Grefenstette.

In these other games, there’s a more or less obvious strategy to winning. Of course it’s more complex in a game like Dota 2 than in an Atari 800 game, but the idea is the same — there are pieces the player controls, a game board of environment, and win conditions to pursue. That’s kind of the case in NetHack, but it’s weirder than that. For one thing, the game is different every time, and not just in the details.

“New dungeon, new world, new monsters and items, you don’t have a save point. If you make a mistake and die you don’t get a second shot. It’s a bit like real life,” said Grefenstette. “You have to learn from mistakes and come to new situations armed with that knowledge.”

Drinking a corrosive potion is a bad idea, of course, but what about throwing it at a monster? Coating your weapon with it? Pouring it on the lock of a treasure chest? Diluting it with water? We have intuitive ideas about these actions, but a game-playing AI doesn’t think the way we do.

The depth and complexity of the systems in NetHack are difficult to explain, but that diversity and difficulty make the game a perfect candidate for a competition, according to Grefenstette. “You have to rely on human knowledge to play the game,” he said.

People have been designing bots to play NetHack for many years that rely not on neural networks but decision trees as complex as the game itself. The team at Facebook Research hopes to engender a new approach by building a training environment that people can test machine learning-based game-playing algorithms on.

NetHack screens with labels showing what the AI is aware of.

The NetHack Learning Environment was actually put together last year, but the NetHack Challenge is only just now getting started. The NLE is basically a version of the game embedded in a dedicated computing environment that lets an AI interact with it through text commands (directions, actions like attack or quaff)

It’s a tempting target for ambitious AI designers. While games like StarCraft 2 may enjoy a higher profile in some ways, NetHack is legendary and the idea of building a model on completely different lines from those used to dominate other games is an interesting challenge.

It’s also, as Grefenstette explained, a more accessible one than many in the past. If you wanted to build an AI for StarCraft 2, you needed a lot of computing power available to run visual recognition engines on the imagery from the game. But in this case the entire game is transmitted via text, making it extremely efficient to work with. It can be played thousands of times faster than any human could with even the most basic computing setup. That leaves the challenge wide open to individuals and groups who don’t have access to the kind of high-power setups necessary to power other machine learning methods.

“We wanted to create a research environment that had a lot of challenges for the AI community, but not restrict it to only large academic labs,” he said.

For the next few months, NLE will be available for people to test on, and competitors can basically build their bot or AI by whatever means they choose. But when the competition itself starts in earnest on October 15, they’ll be limited to interacting with the game in its controlled environment through standard commands — no special access, no inspecting RAM, etc.

The goal of the competition will be to complete the game, and the Facebook team will track how many times the agent “ascends,” as it’s called in NetHack, in a set amount of time. But “we’re assuming this is going to be zero for everyone,” Grefenstette admitted. After all, this is one of the hardest games ever made, and even humans who have played it for years have trouble winning even once in a lifetime, let alone several times in a row. There will be other scoring metrics to judge winners in a number of categories.

The hope is that this challenge provides the seed of a new approach to AI, one that more fundamentally resembles actual human thinking. Shortcuts, trial and error, score-hacking, and zerging won’t work here — the agent needs to learn systems of logic and apply them flexibly and intelligently, or die horribly at the hands of an enraged centaur or owlbear.

You can check out the rules and other specifics of the NetHack Challenge here. Results will be announced at the NeurIPS conference later this year.

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Elevate your enterprise data technology and strategy at Transform 2021.

AI-powered language systems have transformative potential, particularly in the enterprise. They’re already being used to drive chatbots, translate natural language into structured query language, create application layouts and spreadsheets, and improve the accuracy of web search products. Perhaps the best-known AI text-generator, OpenAI’s GPT-3, is being used in more than 300 different apps by tens of thousands of developers and producing 4.5 billion words per day.

As interest in AI rises in business, advisory firm Mordor Intelligence forecasts that the natural language processing (NLP) market will more than triple its revenue by 2025. But noncommercial, open source efforts are concurrently gaining steam, as evidenced by the progress made by EleutherAI. A grassroots collection of AI researchers, EleutherAI this week released GPT-J-6B (GPT-J), a model the group claims performs nearly on par with an equivalent-sized GPT-3 model on various tasks.

“We think it’s probably fair to say this is currently the best open source autoregressive language model you can get by a pretty wide margin,” Connor Leahy, one of the founding members of EleutherAI, told VentureBeat.

GPT-J is what’s known as a Transformer model, which means it weighs the influence of different parts of input data rather than treating all the input data the same. Transformers don’t need to process the beginning of a sentence before the end. Instead, they identify the context that confers meaning to a word in the sentence, enabling them to process input data in parallel.

The Transformer architecture forms the backbone of language models including GPT-3 and Google’s BERT, but EleutherAI claims that GPT-J took less time to train compared with other large-scale model developments. They researchers attribute this to the use of Jax, DeepMind’s Python library designed for machine learning research, as well as training on Google’s tensor processing units (TPU), application-specific integrated circuits (ASICs) developed specifically to accelerate AI.

Training GPT-J

EleutherAI says that GPT-J contains roughly 6 billion parameters, the parts of the machine learning model learned from historical training data. It was trained over the course of five weeks on 400 billion tokens from a dataset created by EleutherAI called The Pile, a 835GB collection of 22 smaller datasets including academic sources (e.g., Arxiv, PubMed), communities (StackExchange, Wikipedia), code repositories (Github), and more. Tokens are a way of separating pieces of text into smaller units in natural language, and they can be words, characters, or parts of words.

Above: GPT-J can solve basic math problems.

Image Credit: EleutherAI

For compute, EleutherAI was able to leverage the TPU Research Cloud, a Google Cloud initiative that supports projects with the expectation that the results of the research will be shared via code and models. GPT-J’s code and the trained model are open sourced under the MIT license and can be used for free using HuggingFace’s Transformers platform or EleutherAI’s website.

GPT-J is more capable than the two models EleutherAI previously released, GPT-Neo 1.3B and GPT-Neo 2.7B. For example, it can perform addition and subtraction and prove simple mathematical theorems, like “Any cyclic group is abelian.” It can also answer quantitative reasoning questions from a popular test dataset (BoolQ) and generate pseudocode.

Above: GPT-J proving a theorem.

Image Credit: EleutherAI

“[OpenAI’s] GPT-2 was about 1.5 billion parameters and doesn’t have the best performance since it’s a bit old, GPT-Neo was about 2.7 billion parameters but somewhat underperforms equal-sized GPT-3 models, GPT-J, the new one, is now 6B — sized similar to the Curie model of OpenAI, we believe,” Leahy said.

Looking ahead

EleutherAI plans to eventually deliver the code and weights needed to run a model similar, though not identical, to the full “DaVinci” GPT-3. (Weights are parameters within a neural network that transform input data.) Compared with GPT-J, the full GPT-3 contains 175 billion parameters and was trained on 499 billion tokens from a 45TB dataset.

Language models like GPT-3 often amplify biases encoded in data. A portion of the training data is not uncommonly sourced from communities with pervasive gender, race, and religious prejudices. OpenAI notes that this can lead to placing words like “naughty” or “sucked” near female pronouns and “Islam” near words like “terrorism.” Other studies, like one published in April by Intel, MIT, and the Canadian Institute for Advanced Research (CIFAR) researchers, have found high levels of stereotypical bias in some of the most popular models.

Above: GPT-J answering a word problem.

Image Credit: EleutherAI

But EleutherAI claims to have performed “extensive bias analysis” on The Pile and made “tough editorial decisions” to exclude datasets they felt were “unacceptably negatively biased” toward certain groups or views.

While EleutherAI’s model might not be cutting edge in terms of its capabilities, it could go a long way toward solving a common problem in tech: the disconnect between research and engineering teams. As Hugging Face CEO Clément Delangue told VentureBeat in a recent interview, on the one hand, tech giants provide black-box NLP APIs while also releasing open source repositories that can be hard to use or aren’t well-maintained. EleutherAI’s efforts could help enterprises to realize the business value of NLP without having to do much of the legwork themselves.

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