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Bodo.ai secures $14M, aims to make Python better at handling large-scale data

By Christine Hall

Bodo.ai, a parallel compute platform for data workloads, is developing a compiler to make Python portable and efficient across multiple hardware platforms. It announced Wednesday a $14 million Series A funding round led by Dell Technologies Capital.

Python is one of the top programming languages used among artificial intelligence and machine learning developers and data scientists, but as Behzad Nasre, co-founder and CEO of Bodo.ai, points out, it is challenging to use when handling large-scale data.

Bodo.ai, headquartered in San Francisco, was founded in 2019 by Nasre and Ehsan Totoni, CTO, to make Python higher performing and production ready. Nasre, who had a long career at Intel before starting Bodo, met Totoni and learned about the project that he was working on to democratize machine learning and enable parallel learning for everyone. Parallelization is the only way to extend Moore’s Law, Nasre told TechCrunch.

Bodo does this via a compiler technology that automates the parallelization so that data and ML developers don’t have to use new libraries, APIs or rewrite Python into other programming languages or graphics processing unit code to achieve scalability. Its technology is being used to make data analytics tools in real time and is being used across industries like financial, telecommunications, retail and manufacturing.

“For the AI revolution to happen, developers have to be able to write code in simple Python, and that high-performance capability will open new doors,” Totoni said. “Right now, they rely on specialists to rewrite them, and that is not efficient.”

Joining Dell in the round were Uncorrelated Ventures, Fusion Fund and Candou Ventures. Including the new funding, Bodo has raised $14 million in total. The company went after Series A dollars after its product had matured and there was good traction with customers, prompting Bodo to want to scale quicker, Nasre said.

Nasre feels Dell Technologies Capital was “uniquely positioned to help us in terms of reserves and the role they play in the enterprise at large, which is to have the most effective salesforce in enterprise.”

Though he was already familiar with Nasre, Daniel Docter, managing director at Dell Technologies, heard about Bodo from a data scientist friend who told Docter that Bodo’s preliminary results “were amazing.”

Much of Dell’s investments are in the early-stage and in deep tech founders that understand the problem. Docter puts Totoni and Nasre in that category.

“Ehsan fits this perfectly, he has super deep technology knowledge and went out specifically to solve the problem,” he added. “Behzad, being from Intel, saw and lived with the problem, especially seeing Hadoop fail and Spark take its place.”

Meanwhile, with the new funding, Nasre intends to triple the size of the team and invest in R&D to build and scale the company. It will also be developing a marketing and sales team.

The company is now shifting from financing to customer- and revenue-focused as it aims to drive up adoption by the Python community.

“Our technology can translate simple code into the fast code that the experts will try,” Totoni said. “I joined Intel Labs to work on the problem, and we think we have the first solution that will democratize machine learning for developers and data scientists. Now, they have to hand over Python code to specialists who rewrite it for tools. Bodo is a new type of compiler technology that democratizes AI.”

 

Apple’s CSAM detection tech is under fire — again

By Zack Whittaker

Apple has encountered monumental backlash to a new child sexual abuse material (CSAM) detection technology it announced earlier this month. The system, which Apple calls NeuralHash, has yet to be activated for its billion-plus users, but the technology is already facing heat from security researchers who say the algorithm is producing flawed results.

NeuralHash is designed to identify known CSAM on a user’s device without having to possess the image or knowing the contents of the image. Because a user’s photos stored in iCloud are end-to-end encrypted so that even Apple can’t access the data, NeuralHash instead scans for known CSAM on a user’s device, which Apple claims is more privacy friendly, as it limits the scanning to just photos rather than other companies which scan all of a user’s file.

Apple does this by looking for images on a user’s device that have the same hash — a string of letters and numbers that can uniquely identify an image — that are provided by child protection organizations like NCMEC. If NeuralHash finds 30 or more matching hashes, the images are flagged to Apple for a manual review before the account owner is reported to law enforcement. Apple says the chance of a false positive is about one in one trillion accounts.

But security experts and privacy advocates have expressed concern that the system could be abused by highly resourced actors, like governments, to implicate innocent victims or to manipulate the system to detect other materials that authoritarian nation states find objectionable. NCMEC called critics the “screeching voices of the minority,” according to a leaked memo distributed internally to Apple staff.

Last night, Asuhariet Ygvar reverse-engineered Apple’s NeuralHash into a Python script and published code to GitHub, allowing anyone to test the technology regardless of whether they have an Apple device to test. In a Reddit post, Ygvar said NeuralHash “already exists” in iOS 14.3 as obfuscated code, but was able to reconstruct the technology to help other security researchers understand the algorithm better before it’s rolled out to iOS and macOS devices later this year.

It didn’t take long before others tinkered with the published code and soon came the first reported case of a “hash collision,” which in NeuralHash’s case is where two entirely different images produce the same hash. Cory Cornelius, a well-known research scientist at Intel Labs, discovered the hash collision. Ygvar confirmed the collision a short time later.

Hash collisions can be a death knell to systems that rely on cryptography to keep them secure, such as encryption. Over the years several well-known password hashing algorithms, like MD5 and SHA-1, were retired after collision attacks rendered them ineffective.

Kenneth White, a cryptography expert and founder of the Open Crypto Audit Project, said in a tweet: “I think some people aren’t grasping that the time between the iOS NeuralHash code being found and [the] first collision was not months or days, but a couple of hours.”

When reached, an Apple spokesperson declined to comment on the record. But in a background call where reporters were not allowed to quote executives directly or by name, Apple downplayed the hash collision and argued that the protections it puts in place — such as a manual review of photos before they are reported to law enforcement — are designed to prevent abuses. Apple also said that the version of NeuralHash that was reverse-engineered is a generic version, and not the complete version that will roll out later this year.

It’s not just civil liberties groups and security experts that are expressing concern about the technology. A senior lawmaker in the German parliament sent a letter to Apple chief executive Tim Cook this week saying that the company is walking down a “dangerous path” and urged Apple not to implement the system.

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