Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

Relativity, provider of a platform that enables legal teams to aggregate large volumes of unstructured data, yesterday announced it has acquired Text IQ as part of an effort to embed AI technologies deeper within its core platform. Terms of the deal were not disclosed.

The companies have an existing partnership, but the acquisition will allow Relativity to expand its reach further into the realms of compliance and data privacy, Relativity CEO Mike Gamson told VentureBeat.

How it works

Text IQ employs unsupervised machine learning (ML), graphical modeling, social network analysis (SNA), natural language processing (NLP), and deep learning algorithms to identify the data most relevant to a legal case. This includes data that is privileged or might contain personally identifiable information (PII) that would be relevant to a case involving, for example, the Global Data Protection Regulation (GDPR) enacted by the European Union.

Text IQ also identifies social networks from unstructured data using a Socio-Linguistic Hypergraph that establishes relationships between individuals, how they communicate, and how they communicate differently with different groups of people. The company claims it finds every trace of a person in a dataset, which is not something any human can achieve on their own.

Relativity’s platform aggregates all the data in a way that makes it simple to apply Text IQ’s AI technologies to it. In combination, these platforms reduce the total cost of a legal proceeding while ostensibly accelerating the rate at which cases can be resolved, Gamson added. In fact, Text IQ claims its platform reduces the time and cost of conducting privilege reviews by up to 75% while also reducing the risk that privileged information might be inadvertently shared.

The Text IQ platform employs an unsupervised approach that allows AI models to be trained using all the raw data available, rather than employing a subset of data selected by humans. A supervised approach to training makes it much more likely unconscious biases will be injected into an AI model, Gamson explained.

Legal landscape

Relativity claims its platform is already employed by hundreds of law firms as judges continue to encourage lawyers to embrace AI technologies to accelerate the pace of litigation. “We like to say we cut the time it takes to get to the truth,” Gamson said.

The Text IQ platform will continue to be sold as a standalone offering, but over time more of the AI capabilities developed by Text IQ will be integrated into the Relativity platform, Gamson added. The company claims its platform has more than 300,000 users.

It’s not clear what impact AI will have on the legal profession. Many of the research tasks that were once performed by interns and junior partners are clearly being automated. However, automating rote tasks will free up time for lawyers to launch additional lawsuits. The caseload each lawyer manages may simply expand, resulting in a lot more lawsuits.

One way or another, the proverbial AI genie is now out of the bag. Law firms that are not able to leverage AI to change the way they operate will over time be forced to limit the number of cases they can handle or outright close. But not every law firm is in a position to absorb the upfront costs AI initiatives incur.

Still, the technology is continuing to advance. AI models employing speech interfaces might even one day help argue a case in court. However, the immediate priority is reducing the current backlog of cases that often take a long time to resolve simply because the discovery process is too slow.

Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

This article is part of a VB Lab Insight series paid for by Plus.

The safest drivers are those with the most experience. Studies show it can take years of practice for automobile drivers to become careful and competent road users. Similarly, the more experience a truck driver has the less likely it is that they will cause a serious crash.

What holds true for human drivers holds true for autonomous driving systems — up to a point. The safest self-driving vehicle platforms are those that have accumulated the most experience.

Since driving experience is so important, how can technologists make sure computerized driving systems get the training they need to operate safely on the nation’s roads and highways?

Solving this challenge is the key to unlocking a fully autonomous future.

How computers learn to drive a semi-truck

Thanks to advances in sensor technology and artificial intelligence (AI), an automated truck is capable of analyzing many objects on the road and making a decision about how to respond.

This is accomplished in large part by training so-called “deep learning” algorithms. Repeatedly expose a self-driving system to all kinds of obstacles, from a cut-in vehicle to a construction site, and the system will start to understand how to react when an obstruction appears on the highway.

Here it is important to note that unlike people, machines lack common sense and don’t do well handling novel situations. Human drivers know to slow down in the face of an unexpected obstacle — a bear, say — because we can make decisions based on similar situations we have already encountered or extrapolate from other incidents.

Unlike humans, however, deep neural networks can only learn from data they have been trained on, whether from public roads, closed courses, or computer simulations.

So back to the original question: How do you train the machines so they are exposed to the full range of the driving experience?

Data, data, and more data

Plus’s goal is to help truck drivers on long-haul routes, where they encounter a variety of road and weather conditions. In addition to closed-road testing and computer simulations, the company’s PlusDrive system is learning on the open road, where the trucks can be exposed to real-world obstacles and situations. Junk flying from a pickup bed. Ice slicks. A wind turbine blade. A zigzagging motorcycle.

Though these so-called “long tail” phenomena comprise less than 1% of the time behind the wheel, knowing how to safely navigate them is critical for machines. Society expects that a computer-operated machine must be at least an order of magnitude safer than a human driver.

Billions of miles of on-road testing

Starting this summer, Plus will put its supervised automated driving system into factory production. It is also retrofitting existing trucks with the system. By this time next year, hundreds of automated trucks powered by PlusDrive will be on the road, hauling commercial cargo.

Human drivers will be behind the wheel. Like an experienced professional training a new recruit, Plus drivers will monitor the autonomous trucks while teaching them how to handle unexpected obstacles.

Plus estimates that its fleet will accumulate billions of collective miles before the company deploys fully driverless vehicles. Taking an evolutionary approach to full autonomy enables the company to rack up miles more quickly, with the assistance of on-board professional drivers who are training and validating the system.

To support its global deployment in the U.S., China, Europe, and other markets, Plus recently raised $420 million in new funding.

Truck driver retention and low-carbon solution

The drivers benefit too. The Plus supervised autonomous trucking solution elevates the role of the truck driver, upskilling them in preparation for an autonomous future. At the same time a digital co-pilot will ease driver exhaustion on long-haul routes, and fleets will spend less on the hiring process.

The system yields other gains. Fuel comprises about a third of a trucking company’s operating budget, by far the largest cost for heavy trucks. When an automated system understands the road, pulling in GPS and weather data too, they optimize shifting and braking. Plus has run pilot projects showing that  PlusDrive saves 10% of the tank compared to the most efficient drivers, a win for the bottom line and the environment.

The autonomous trucking future, now

Commercial space travel, solar-powered cities, autonomous vehicles — the first two visions of the future depend on specific economic inflection points, while the third is wholly dependent on the amount of data a system has accumulated.

Plus is building the necessary feedback loop of information today. Its trucks are accumulating the data. Its drivers, who are among the safest and most efficient Class A drivers, are training the system with their responses. Its engineers are fine-tuning PlusDrive’s algorithms and decisions. And eventually PlusDrive will be one of the safest and most experienced drivers on the road.

Plus is applying autonomous trucking technology to trucks today. For more information, please visit www.plus.ai.

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