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Game

Game Boy Advance ‘hacked’ to run PlayStation games using a Raspberry Pi

The Game Boy Advance is useful in the modern era for more than watching Christopher Nolan blockbusters. Gizmodo notes that tinkerer Rodrigo Alfonso has Nintendo’s 20-year-old handheld running PlayStation (and Genesis, and SNES) games without special modifications. The trick, as you might imagine revolves around a custom cartridge — you’re technically running the game on a separate system.

The cartridge houses a Raspberry Pi 3 mini-computer running the RetroPie emulator and streaming both video and input through the GBA’s multiplayer-oriented Link Port. Yes, that’s constraining as you think it is — you can’t transfer more than 1.6Mbps bi-directionally, and the Pi has to routinely give the “poor” GBA’s processor a break for a few microseconds. Alfonso suggests lowering the stream resolution from the console’s native 240 x 160 if a high frame rate is important.

Still, the results are mostly impressive. The special cart can handle classics like the Crash Bandicoot series and Spyro the Dragon at smooth frame rates, albeit with some video artifacts that reflect the limited bandwidth. You can overclock the GBA’s processor to improve the frame rate and quality.

You’ll have to build the cartridge and load code yourself, although Alfonso has helpfully provided both on GitHub. This probably won’t replace a PSP if you want the most authentic PlayStation handheld experience you can get. It might, however, give you a reason to dig your GBA out of the closet.

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Game

Advance Wars 1+2: Re-Boot Camp delayed beyond 2021

Revealed earlier this year, Advance Wars: 1+2: Re-Boot Camp – a remake of the first two Advance Wars games for Nintendo Switch – caught a lot of Switch owners by surprise. Even more surprising was the game’s release date of December 3rd, 2021. Unfortunately, Nintendo announced today that Advance Wars 1+2: Re-Boot Camp has been delayed, citing the need for additional polish.

In a tweet published to the official Nintendo of America Twitter account today, Nintendo confirmed that Advance Wars 1+2: Re-Boot Camp will now be launching at some point in spring 2022. Sadly, we don’t have a new release date for the compilation yet, suggesting that Nintendo isn’t quite sure how much more time it needs for finishing touches.

“Hello, troops! #AdvanceWars 1+2: Re-Boot Camp, which was set to launch on 12/3, will now release for #NintendoSwitch in spring 2022,” today’s tweet reads. “The game just needs a little more time for fine tuning. You’ll be battling with Andy & friends soon! Thanks for your patience.”

While any delay is a bummer, it isn’t as if Advance Wars fans aren’t used to waiting. Before the announcement of Advance Wars 1+2: Re-Boot Camp, the last time we heard from the Advance Wars series was way back in 2008 with Advance Wars: Days of Ruin for the Nintendo DS.

So, when Advance Wars fans have already been waiting 13 years for a new entry in the series, waiting a few more months isn’t huge in the grand scheme of things. On the other thing of that coin, this delay might sting a little more than some given the long wait since the Days of Ruin. In any case, we’ll let you know when Nintendo confirms a new release date for Advance Wars 1+2: Re-Boot Camp, so stay tuned for more.



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AI

BASALT Minecraft competition aims to advance reinforcement learning

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Deep reinforcement learning, a subfield of machine learning that combines reinforcement learning and deep learning, takes what’s known as a reward function and learns to maximize the expected total reward. This works remarkably well, enabling systems to figure out how to solve Rubik’s Cubes, beat world champions at chess, and more. But existing algorithms have a problem: they implicitly assume access to a perfect specification. In reality, tasks don’t come prepackaged with rewards — those rewards come from imperfect human reward designers. And it can be difficult to translate conceptual preferences into a reward functions environments can calculate.

To solve this problem, researchers at DeepMind and the University of California, Berkeley, have launched a competition, BASALT, where the goal of an AI system must be communicated through demonstrations, preferences, or some other form of human feedback. Built on Minecraft, systems in BASALT must learn the details of specific tasks from human feedback, choosing among a wide variety of actions to perform.

BASALT

Recent research has proposed algorithms that allow designers to iteratively communicate details about tasks. Instead of rewards, they leverage new types of feedback like demonstrations, preferences, corrections, and more and elicit feedback by taking the first steps of provisional plans and seeing if humans intervene, or by asking designers questions.

But there aren’t benchmarks to evaluate algorithms that learn from human feedback. A typical study will take an existing deep reinforcement learning benchmark, strip away the rewards, train a system using their feedback mechanism, and evaluate performance according to the preexisting reward function. This is problematic. For example, in the Atari game Breakout, which is often used as a benchmark, a system must either hit the ball back with the paddle or lose. Good performance on Breakout doesn’t necessarily mean the algorithm mastered the game mechanics. It’s possible that it learned a simpler heuristic like “don’t die.”

BASALT Minecraft

In the real world, systems aren’t funneled into an obvious task above all others. That’s why BASALT provides a set of tasks and task descriptions as well as information about the player’s inventory — but no rewards. For example, one task — MakeWaterfall — provides in-game items including water buckets, stone pickaxe, stone shovel, and cobblestone blocks and the description “After spawning in a mountainous area, the agent should build a beautiful waterfall and then reposition itself to take a scenic picture of the same waterfall. The picture of the waterfall can be taken by orienting the camera and then throwing a snowball when facing the waterfall at a good angle.”

BASALT allows designers to use whichever feedback mechanisms they prefer to create systems that accomplish the tasks. The benchmark records the trajectories of two different systems on a particular environment and asks a human to decide which of the agents performed the task better.

Future work

The researchers say that BASALT affords a number of advantages over existing benchmarks including reasonable goals, large amounts of data, and robust evaluations. In particular, they make the case that Minecraft is well-suited to the task because there are thousands of hours of gameplay on YouTube with which competitors could train a system. Moreover, Minecraft’s properties are easy to understand, the researchers say, with tools that have functions similar to real-world tools and straightforward goals like building shelter and acquiring enough food to not starve.

BASALT is also designed to be feasible to use on a budget. The code ships with a baseline system that can be trained in a couple of hours on a single GPU, according to Rohin Shah, a research scientist at DeepMind and project lead on BASALT.

“We hope that BASALT will be used by anyone who aims to learn from human feedback, whether they are working on imitation learning, learning from comparisons, or some other method. It mitigates many of the issues with the standard benchmarks used in the field. The current baseline has lots of obvious flaws, which we hope the research community will soon fix,” Shah wrote in a blog post. “We envision eventually building agents that can be instructed to perform arbitrary Minecraft tasks in natural language on public multiplayer servers, or inferring what large-scale project human players are working on and assisting with those projects, while adhering to the norms and customs followed on that server.”

The evaluation code for BASALT will be available in beta soon. The team is accepting sign-ups now, with plans to announce the winners of the competition at the NeurIPS 2021 machine learning conference in December.

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Categories
Game

Advance Wars 1+2: Re-Boot Camp Remasters the GBA Classics

Nintendo has announced that Advance Wars 1+2: Re-Boot Camp, a remaster of Advance Wars and Advance Wars 2: Black Hole Rising, will be coming to Switch. Nintendo says the games have been “reimagined and rebuilt from the ground up.” Advance Wars 1+2: Re-Boot Camp will release on December 3, but pre-orders are available starting today.

Advance Wars is a series of turn-based strategy games that has lain dormant for many years, but Nintendo appears intent on giving the franchise new life on the Switch. The trailer shown for the game features a variety of unit types, grid maps, and terrain types, as well as the ability to capture locations like cities. Advance Wars‘ gameplay will likely be familiar to anyone who has played recent Fire Emblem games or other turn-based strategy titles.

Nintendo promises that Advance Wars 1+2: Re-Boot Camp will contain “thrilling stories, memorable characters, and vibrant gameplay.” The game features the entirety of the campaigns from the first two games in the series, Advance Wars and Advance Wars 2: Black Hole Rising.

A trailer showed a variety of cartoonish commanding officers leading their troops in ground, air, and naval combat. Players can purchase and upgrade units to help defend against invading armies and will have to deal with a variety of threats, including fog of war. Thanks to a rock-paper-scissors-like structure, units have advantages and disadvantages over other units – for example, anti-air units are great at attacking planes, but may have trouble against other unit types.

Advance Wars 1+2: Re-Boot Camp will be available on December 3 on Nintendo Switch.

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AI

IBM is acquiring Turbonomic to advance AIOps agenda

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IBM announced this week that it is acquiring Turbonomic, provider of application resource management (ARM) and network performance management (NPM) software infused with machine learning algorithms. Terms of the acquisition, which is expected to close this quarter, were not disclosed.

The two companies have a long-standing relationship under which IBM has been reselling Turbonomic’s ARM platform. Cisco also resells tools developed by the company. Turbonomic, which is privately held, claims revenues were up 41% for fiscal 2021 and counts Avon, HauteLook, and Litehouse Foods among its customers.

Applications and systems management

The decision to acquire Turbonomic comes after IBM began revamping its application and systems management portfolio last fall. This push began in earnest with the acquisition of Instana, provider of an application performance management (APM) platform for monitoring and observing applications.

IBM now plans to further integrate the ARM software Turbonomic developed with the APM software from Instana and an IBM Cloud Pak for Watson AIOps platform that employs machine learning algorithms to identify anomalies in real time.

“Turbonomic provides actionable observability,” IBM Automation GM Dinesh Nirmal told VentureBeat in an interview.

IBM is further extending its IT management portfolio via the recent acquisition of WDG Automation, provider of a robotic process automation (RPA) platform, and MyInvenio, which offers process mining tools, he noted.

As IT environments become more complex, Nirmal said it won’t be feasible to manage these environments without augmenting IT staff with capabilities enabled by AI platforms. It’s not likely AI platforms will replace the need for human IT administrators, but the job functions themselves will continue to evolve as lower-level manual tasks become automated, Nirmal added.

IT challenges

Now that companies are becoming more cognizant of the scope of IT management challenges, IT teams are increasingly embracing AI platforms. Organizations are now deploying a new generation of microservices-based applications that are more difficult to manage than the existing monolithic legacy applications, which are not likely to be retired anytime soon, Nirmal said. Those applications make use of cloud-native technologies such as containers, Kubernetes, and serverless computing frameworks that all need to be managed alongside virtual machines. At the same time, the IT environment has become more distributed than ever, thanks to the rise of both cloud and edge computing platforms.

The only way to contain the total cost of managing that extended enterprise is to rely more on automation enabled by AIOps platforms, Nirmal said.

IT teams need to come to terms with the fact that it takes time for machine learning algorithms to learn IT environments that are unique and subject to change. Implementing AI requires patience, Nirmal said, adding, “IT teams need to accept that AI comes with an upfront cost.”

But the return on investment in AIOps becomes apparent as rote tasks are eliminated and more potential issues are addressed before they impact an application, Nirmal noted. IT teams, for example, will be able to predict the impact new code is likely to have on the overall IT environment before it’s deployed.

IBM’s investments in AIOps are a natural extension of the capabilities IBM has developed to automate a wide range of business processes using AI technologies, Nirmal added. IT leaders can’t make a credible case for applying AI to automate business processes if the IT team isn’t using the same technologies to automate IT operations, he noted.

At this juncture, AI is about to become a mainstream component of IT operations. The issue now is determining to what degree. In some cases, AI capabilities will be slipstreamed into existing platforms, while in others, IT teams will decide to move to a new platform. Either way, machine learning algorithms will be present in one form or another.

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Categories
AI

Vivun raises $35 million to advance presales engineering platform

Vivun provides a software-as-a-service (SaaS) platform dubbed Hero that automates the management of presales processes. Today the company revealed it has garnered $35 million in additional funding via a series B round led by Menlo Ventures.

While customer relationship management (CRM) software is widely employed to manage sales processes, applications optimized for presales teams — made up of engineers who often have more insights into which deals are likely to close than other members of the sales team — are not widely deployed, Vivun cofounder and CEO Matt Darrow said.

Vivun is focused on presales processes that are especially critical in IT sales involving software and increasingly complex IT infrastructure platforms. The company counts among its customers Autodesk, Okta, Cloudera, and Dell. But industry segments like aerospace face similar presales management challenges, Darrow noted. “These issues apply to all sorts of industries,” he said.

Presales engineers tend to have a better idea of which deals are likely to close based on the attributes of a product. As that data is captured in Hero, the platform applies AI and other data science techniques. It can, for example, identify when enhancements to a product might address a technical gap that had resulted in an organization losing deals. That expert system is invoked via a natural language processing (NLP) engine Vivun developed.

Hero is designed to enable presales teams to create their own lists to prioritize tasks based on their preferences, as opposed to relying solely on the agenda of a sales team that may not have as deep a technical understanding of a platform’s capabilities. Presales engineers can also receive personalized alerts based on those preferences.

Hero offers a scoring system for rating the likelihood a deal will close using clustering techniques that identify similar sales opportunities that have been closed successfully. Hero can also recommend strategies that have been proven to remedy risky deals or otherwise bolster sales opportunities.

While presales teams have historically been viewed as an adjunct to salespeople, who typically engage the customer first, Darrow said presales teams often have a deeper understanding of each customer’s requirements. Based on that knowledge, they may have a better appreciation for which deals are likely to close based on what they learned during engagements with other customers, Darrow said.

Those insights can be balanced against less technically oriented members of the sales team who often tend to be overly optimistic about the prospects of closing a deal, Darrow said. That doesn’t necessarily mean a deal won’t close. Sometimes salespeople have a deep enough relationship with the customer to enable them to overcome any potential technical obstacles. Nevertheless, the “biggest voice in the room” should not always be allowed to dictate the allocation of limited presales engineering resources, Darrow said.

Organizations have for decades invested billions in platforms to automate sales management processes, with mixed results. A CRM application, for example, is often employed more as a system of record for sales managers and finance executives than as a tool to enable individual salespeople to succeed. Most of those CRM platforms are not designed to maximize the investment in a sales engineering team that needs to support multiple deals before and after they close. In fact, Darrow notes that customers often trust members of sales engineering teams because of their technical acumen.

Regardless of how a deal is closed, the need to manage presale and post-sale engineering processes using a dedicated platform will only continue growing.

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Categories
AI

Pony.ai raises $100 million more to advance its autonomous vehicle tech

Self-driving car startup Pony.ai today announced it has raised $100 million in an extension of its series C round. The funds bring the company’s total raised to over $1 billion at a post-money valuation of $5.3 billion, up from $3 billion as of February 2020.

Some experts predict the pandemic will hasten the adoption of autonomous transportation technologies. Despite needing disinfection, driverless cars can potentially minimize the risk of spreading disease. For example, Pony.ai says it has delivered more than 15,000 packages of food and health kits in California during the COVID-19 health crisis.

Pony.ai BotRide

Former Baidu chief architect James Peng cofounded Pony.ai in 2016 with Tiancheng Lou, who worked at Google X’s autonomous car project before it was spun off into Waymo. The two aim to build level 4 autonomous cars — cars able to operate without human oversight under select conditions, as defined by the Society of Automotive Engineers — for “predictable” environments such as industrial parks, college campuses, and small towns, with a tentative deployment window of several years from now.

Pony’s full-stack hardware platform, PonyAlpha, leverages lidars, radars, and cameras to keep tabs on obstacles up to 200 meters from its self-driving cars. PonyAlpha is the foundation for the company’s fully autonomous trucks and freight delivery solution, which commenced testing in April 2019 and is deployed in test cars in Fremont, California and in Beijing and Guangzhou in China.

Pony.ai, which has offices in Guangzhou and Fremont, is one of the few companies to have secured an autonomous vehicle testing license in Beijing. In California, it has obtained a robo-taxi operations permit from the California Public Utilities Commission. The only other companies to have secured such a license in California are Cruise, AutoX, Aurora, Voyage, Waymo, and Zoox.

Last October, Pony.ai partnered with Via and Hyundai to launch BotRide, Pony.ai’s second public robo-taxi service after a pilot program (PonyPilot) in Nansha, China. BotRide allowed riders and carpoolers to hail autonomous Hyundai Kona electric SUVs through apps developed with Via, sourcing from a fleet of 10 cars with human safety drivers behind the wheel.

Pony.ai BotRide

In August, Pony.ai inked an agreement with Bosch to “explore the future of automotive maintenance and repair for autonomous fleets.” Pony.ai and Bosch’s Automotive Aftermarket division in North America plan to develop and test fleet maintenance solutions for commercial robo-taxi programs. Pony.ai says it began piloting a maintenance program with Bosch in the San Francisco Bay Area in early July.

Among other potential advantages, autonomous driving promises the continuous operation of fleets and reduction of downtime. According to a 2017 McKinsey report, robo-taxis could reduce a fleet operator’s total cost of ownership by 30% to 50% compared with private vehicle ownership and by about 70% compared to shared mobility, significantly disrupting the market. But robo-taxis will need vastly different maintenance infrastructure than cars, in part because they might lack regular monitoring; have only minutes between passengers; and sport expensive, sensitive, and unconventional parts, like lidar sensors.

Pony.ai has competition in Daimler, which in summer 2018 obtained a permit from the Chinese government that allows it to test self-driving cars powered by Baidu’s Apollo platform on public roads in Beijing. And startup Optimus Ride built out a small driverless shuttle fleet in Brooklyn. Waymo, which has racked up more than 20 million real-world miles in over 25 cities across the U.S. and billions of simulated miles, in November 2018 became the first company to obtain a driverless car testing permit from the California Department of Motor Vehicles (DMV). Other competitors include Tesla, Aptiv, May Mobility, Cruise, Aurora, Argo AI, Pronto.ai, and Nuro.

However, only Nuro, Waymo, Cruise, and Argo rival Pony.ai’s fundraising. With the exception of Argo, each has raised more than $3 billion in venture capital at valuations ranging from $7.5 billion (Argo) to $175 billion (Waymo).

Fortunately for Pony.ai, it has partnerships with Chinese state-owned auto group FAW and GAC Group, a Guangzhou-based automobile maker, to develop level 4 robo-taxi vehicles. It also has a joint collaboration with On Semiconductor to prototype image sensing and processing technologies for machine vision. And Pony.ai has driven over 1.5 million autonomous kilometers (or about 932,056 miles) as of year-end 2019, putting it within striking distance of Yandex (2 million miles) and Baidu (1.8 million miles).

Brunei Investment Agency, Brunei’s sovereign wealth fund, and CITIC Private Equity Funds Management participated in the series C extension. Previous and existing investors in Pony.ai include Toyota, video game publisher Beijing Kunlun Wanwei, Sequoia Capital China, IDG Capital, and Legend Capital. The Ontario Teachers’ Pension Plan board’s Teachers’ Innovation Platform led this latest round, with participation from Fidelity China Special Situations PLC, 5Y Capital, ClearVue Partners, Eight Roads, and others.

“Technological innovation is constantly transforming traditional industries, and autonomous driving will change traditional ways of transportation in the near future,” a CPE spokesperson told VentureBeat via email. “CPE believes that Pony.ai will lead this trend, and will help it seize the opportunity for faster growth.”

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Categories
AI

Here’s where AI will advance in 2021

Artificial intelligence continues to advance at a rapid pace. Even in 2020, a year that did not lack compelling news, AI advances commanded mainstream attention on multiple occasions. OpenAI’s GPT-3, in particular, showed new and surprising ways we may soon be seeing AI penetrate daily life. Such rapid progress makes prediction about the future of AI somewhat difficult, but some areas do seem ripe for breakthroughs. Here are a few areas in AI that we feel particularly optimistic about in 2021.

Transformers

Two of 2020’s biggest AI achievements quietly shared the same underlying AI structure. Both OpenAI’s GPT-3 and DeepMind’s AlphaFold are based on a sequence processing model called the Transformer. Although Transformer structures have been around since 2017, GPT-3 and Alphafold demonstrated the Transformer’s remarkable ability to learn more deeply and quickly than the previous generation of sequence models, and to perform well on problems outside of natural language processing.

Unlike prior sequence modelling structures such as recurrent neural networks and LSTMs, Transformers depart from the paradigm of processing data sequentially. They process the whole input sequence at once, using a mechanism called attention to learn what parts of the input are relevant in relation to other parts. This allows Transformers to easily relate distant parts of the input sequence, a task that recurrent models have famously struggled with. It also allows significant parts of the training to be done in parallel, better leveraging the massively parallel hardware that has become available in recent years and greatly reducing training time. Researchers will undoubtedly be looking for new places to apply this promising structure in 2021, and there’s good reason to expect positive results. In fact, in 2021 OpenAI has already modified GPT-3 to generate images from text descriptions. The transformer looks ready to dominate 2021.

Graph neural networks

Many domains have data that naturally lend themselves to graph structures: computer networks, social networks, molecules/proteins, and transportation routes are just a few examples. Graph neural networks (GNNs) enable the application of deep learning to graph-structured data, and we expected GNNs to become an increasingly important AI method in the future. More specifically, in 2021, we expect that methodological advances in a few key areas will drive broader adoption of GNNs.

Dynamic graphs are the first area of importance. While most GNN research to date has assumed a static, unchanging graph, the scenarios above necessarily involve changes over time: For example, in social networks, members join (new nodes) and friendships change (different edges). In 2020, we saw some efforts to model time-evolving graphs as a series of snapshots, but 2021 will extend this nascent research direction with a focus on approaches that model a dynamic graph as a continuous time series. Such continuous modeling should enable GNNs to discover and learn from temporal structure in graphs in addition to the usual topological structure.

Improvements on the message-passing paradigm will be another enabling advancement. A common method of implementing graph neural networks, message passing is a means of aggregating information about nodes by “passing” information along the edges that connect neighbors. Although intuitive, message passing struggles to capture effects that require information to propagate across long distances on a graph. Next year, we expect breakthroughs to move beyond this paradigm, such as by iteratively learning which information propagation pathways are the most relevant or even learning an entirely novel causal graph on a relational dataset.

Applications

Many of last year’s top stories highlighted nascent advances in practical applications of AI, and 2021 looks poised to capitalize on these advances. Applications that depend on natural language understanding, in particular, are likely to see advances as access to the GPT-3 API becomes more available. The API allows users to access GPT-3’s abilities without requiring them to train their own AI, an otherwise expensive endeavor. With Microsoft’s purchase of the GPT-3 license, we may also see the technology appear in Microsoft products as well.

Other application areas also appear likely to benefit substantially from AI technology in 2021. AI and machine learning (ML) have spiraled into the cyber security space, but 2021 shows potential of pushing the trajectory a little steeper. As highlighted by the SolarWinds breach, companies are coming to terms with impending threats from cyber criminals and nation state actors and the constantly evolving configurations of malware and ransomware. In 2021, we expect an aggressive push of advanced behavioral analytics AI for augmenting network defense systems. AI and behavioral analytics are critical to help identify new threats, including variants of earlier threats.

We also expect an uptick in applications defaulting to running machine learning models on edge devices in 2021. Devices like Google’s Coral, which features an onboard tensor processing unit (TPU), are bound to become more widespread with advancements in processing power and quantization technologies. Edge AI eliminates the need to send data to the cloud for inference, saving bandwidth and reducing execution time, both of which are critical in fields such as health care. Edge computing may also open new applications in other areas that require privacy, security, low latency, and in regions of the world that lack access to high-speed internet.

The bottom line

AI technology continues to proliferate in practical domains, and advances in Transformer structures and GNNs are likely to spur advances in domains that haven’t yet readily lent themselves to existing AI techniques and algorithms. We’ve highlighted here several areas that seem ready for advancement this year, but there will undoubtedly be surprises as the year unfolds. Predictions are hard, especially about the future, as the saying goes, but right or wrong, 2021 looks to be an exciting year for the field of AI.

Ben Wiener is a data scientist at Vectra AI and has a PhD in physics and a variety of skills in related topics including computer modeling, optimization, machine learning, and robotics.

Daniel Hannah is a data scientist and researcher with more than 8 years of experience turning messy data into actionable insights. At Vectra AI, he works at the interface of artificial intelligence and network security. Previously, he applied machine learning approaches to anomaly detection as a fellow at Insight Data Science.

Allan Ogwang is a data scientist at Vectra AI with a strong math background and experience in econometrics, statistical modeling, and machine learning.

Christopher Thissen is a data scientist at Vectra AI, where he uses machine learning to detect malicious cyber behaviors. Before joining Vectra, Chris led several DARPA-funded machine learning research projects at Boston Fusion Corporation.

VentureBeat is always looking for insightful guest posts on data tech and strategy.

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