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After it looked like Apple might no-show, the company has committed to sending a representative to a Senate antitrust hearing on app store competition later this month.

Last week, Senators Amy Klobuchar (D-MN) and Mike Lee (R-UT) put public pressure on the company to attend the hearing, which will be held by the Senate Judiciary Subcommittee on Competition Policy, Antitrust, and Consumer Rights. Klobuchar chairs that subcommittee, and has turned her focus toward antitrust worries about the tech industry’s most dominant players.

The hearing, which Google will also attend, will delve into Apple and Google’s control over “the cost, distribution, and availability of mobile applications on consumers, app developers, and competition.”

App stores are one corner of tech that looks the most like a duopoly, a perception that Apple’s high profile battle with Fortnite-maker Epic is only elevating. Meanwhile, with a number of state-level tech regulation efforts brewing, Arizona is looking to relieve developers from Apple and Google’s hefty cut of app store profits.

In a letter last week, Klobuchar and Lee, the subcommittee’s ranking member, accused Apple of “abruptly” deciding that it wouldn’t send a witness to the hearing, which is set for April 21.

“Apple’s sudden change in course to refuse to provide a witness to testify before the Subcommittee on app store competition issues in April, when the company is clearly willing to discuss them in other public forums, is unacceptable,” the lawmakers wrote.

By Monday, that pressure had apparently done its work, with Apple agreeing to attend the hearing. Apple didn’t respond to a request for comment.

While the lawmakers are counting Apple’s acquiescence as a win, that doesn’t mean they’ll be sending their chief executive. Major tech CEOs have been called before Congress more often over the last few years, but those appearances might have diminishing returns.

Tech CEOs, Apple’s Tim Cook included, are thoroughly trained in the art of saying little when pressed by lawmakers. Dragging in a CEO might work as a show of force, but tech execs generally reveal little over the course of their lengthy testimonies, particularly when a hearing isn’t accompanied by a deeper investigation.

As artificial intelligence becomes more advanced, previously cutting-edge — but generic — AI models are becoming commonplace, such as Google Cloud’s Vision AI or Amazon Rekognition.

While effective in some use cases, these solutions do not suit industry-specific needs right out of the box. Organizations that seek the most accurate results from their AI projects will simply have to turn to industry-specific models.

There are a few ways that companies can generate industry-specific results. One would be to adopt a hybrid approach — taking an open-source generic AI model and training it further to align with the business’ specific needs. Companies could also look to third-party vendors, such as IBM or C3, and access a complete solution right off the shelf. Or — if they really needed to — data science teams could build their own models in-house, from scratch.

Let’s dive into each of these approaches and how businesses can decide which one works for their distinct circumstances.

Generic models alone often don’t cut it

Generic AI models like Vision AI or Rekognition and open-source ones from TensorFlow or Scikit-learn often fail to produce sufficient results when it comes to niche use cases in industries like finance or the energy sector. Many businesses have unique needs, and models that don’t have the contextual data of a certain industry will not be able to provide relevant results.

Building on top of open-source models

At ThirdEye Data, we recently worked with a utility company to tag and detect defects in electric poles by using AI to analyze thousands of images. We started off using Google Vision API and found that it was unable to produce our desired results — with the precision and recall values of the AI models completely unusable. The models were unable to read the characters within the tags on the electric poles 90% of the time because it didn’t identify the nonstandard font and varying background colors used in the tags.

So, we took base computer vision models from TensorFlow and optimized them to the utility company’s precise needs. After two months of developing AI models to detect and decipher tags on the electric poles, and another two months of training these models, the results are displaying accuracy levels of over 90%. These will continue to improve over time with retraining iterations.

Any team looking to expand its AI capabilities should first apply its data and use cases to a generic model and assess the results. Open-source algorithms that companies can start off with can be found on AI and ML frameworks like TensorFlow, Scikit-learn or Microsoft Cognitive Toolkit. At ThirdEye Data, we used convolutional neural network (CNN) algorithms on TensorFlow.

Then, if the results are insufficient, the team can extend the algorithm by training it further on their own industry-specific data.

A few months back, robotic process automation (RPA) unicorn UiPath raised a huge $750 million round at a valuation of around $35 billion. The capital came ahead of the company’s expected IPO, so its then-new valuation helped provide a measuring stick for where its eventual flotation could price.

UiPath then filed to go public. But today the company’s first IPO price range was released, failing to value the company where its final private backers expected it to.

In an S-1/A filing, UiPath disclosed that it expects its IPO to price between $43 and $50 per share. Using a simple share count of 516,545,035, the company would be worth $22.2 billion to $25.8 billion at the lower and upper extremes of its expected price interval. Neither of those numbers is close to what it was worth, in theory, just a few months ago.

According to IPO watching group Renaissance Capital, UiPath is worth up to $26.0 billion on a fully diluted basis. That’s not much more than its simple valuation.

For UiPath, its initial IPO price interval is a disappointment, though the company could see an upward revision in its valuation before it does sell shares and begin to trade. But more to the point, the company’s private-market valuation bump followed by a quick public-market correction stands out as a counter-example to something that we’ve seen so frequently in recent months.

Is UiPath’s first IPO price interval another indicator that the IPO market is cooling?

Remember Roblox?

If you think back to the end of 2020, Roblox decided to cancel its IPO and pursue a direct listing instead. Why? Because a few companies like Airbnb had gone public at what appeared to be strong valuation marks only to see their values rocket once they began to trade. So, Roblox decided to raise a huge amount of private capital, and then direct list.

Cybersecurity training startup Hack The Box, which emerged originally from Greece, has raised a Series A investment round of $10.6 million, led by Paladin Capital Group and joined by Osage University Partners, Brighteye Ventures, and existing investors Marathon Venture Capital. It will use the funding to expand. Most recently it launched Hack The Box Academy.

Started in 2017, Hack The Box specializes in using ‘ethical hacking’ to train cybersecurity techniques. Users are given challenges to “attack” virtual vulnerable labs in a simulated, gamified, and test environment. This approach has garnered over 500,000 platform members, from beginners to experts, and brought in around 800 organizations (such as governments, Fortune 500 companies, and academic institutions) to improve their cyber-adversarial knowledge.

Haris Pylarinos, Hack The Box Co-Founder and CEO said: “Everything we do is geared around creating a safer Internet by empowering corporate teams and individuals to create unbreakable systems.”

Gibb Witham, Senior Vice President, Paladin Capital Group commented: “We’re excited to be backing Hack The Box at this inflection point in their growth as organizations recognize the increasing importance of an adversarial security practice to combat constantly evolving cyber attacks.”

Hack The Box competes with Offensive Security, Immersive Labs,   
INE, and eLearnSecurity (acquired by INE).

Hack The Box is using a SaaS business model. In the B2C market it provides monthly and annual subscriptions that provide unrestricted access to the training content and in the B2B market, it provides bi-annual and annual licenses which provide access to dedicated adversarial training environments with value-added admin capabilities.

Rivian, the Amazon-backed EV manufacturer aiming to bring an electric pickup to market later this year, has partnered with Samsung SDI as its battery cell supplier, the company said Monday.

The two companies did not disclose the value of the deal or its term length, but in a statement released Monday Rivian said it had been working with Samsung SDI “throughout the vehicle development process.”

Rivian pointed out that its anticipated R1T pickup and R1S SUV, which Rivian calls “adventure vehicles,” require a battery module and pack that can handle extreme temperatures and durability use cases.

South Korea-based Samsung SDI already supplies battery cells to other automakers. In 2019, the company signed a $3.2 billion deal with BMW Group for a 10-year supply agreement.

“We’re excited about the performance and reliability of Samsung SDI battery cells combined with our energy-dense module and pack design,” Rivian CEO Rj Scaringe said in a statement. “Samsung SDI’s focus on innovation and responsible sourcing of battery materials aligns well with our vision.”