Jun 11, 2021

Eightfold AI valued at US$2bn after raising $220mn

AI
Eightfold
funding
Data
2 min
Eightfold AI has raised $220mn and will use the funds to continue the growth and development of its AI-powered talent intelligence platform

Eightfold AI, a talent matching platform, has raised $220mn in a Series E funding round led by SoftBank’s Vision Fund 2, boosting its valuation to around $2bn.

The Mountain View-based startup provides its clients with a talent acquisition platform that helps them identify suitable candidates and import and filter thousands of resumes. The funds will be used to continue the growth and development of Eightfold's AI-powered Talent Intelligence Platform and expand its growing partner ecosystem. 

This Series E funding round led by SoftBank Vision Fund 2 also includes investors from previous rounds, including General Catalyst, Capital One Ventures, Foundation Capital, IVP and Lightspeed Venture Partners. With this, the company has raised more than $410m in total and $350m in just the past six months.

"Current HR systems were designed to address issues from a previous era, and they have failed to keep pace with the changing nature of work and the workforce. At Eightfold, we have an unprecedented opportunity, using AI to align the career goals of individuals while simultaneously creating better results for employers," said Ashutosh Garg, Founder & CEO at Eightfold. "Transforming HR and global talent further unlocks trillions of dollars worth of human potential. SoftBank shares our bold vision, and we are excited to welcome them as our partner."

How does the platform work?

Recruiting remains a big global problem. Eightfold itself analyses data it sources from a range of places, including employers’ sales, customer management, and human resources as well as public sources such as patent sites and publications.

"Powered by AI and machine learning, Eightfold's platform provides global enterprises with a single solution for managing the entire talent lifecycle, including hiring, retaining, and growing a diverse global workforce," said Deep Nishar, Senior Managing Partner at SoftBank Investment Advisers. "We are pleased to partner with Ashutosh and the Eightfold team to support their ambition of transforming how enterprises manage talent and how people build their careers."

The company has customers across 110 countries, 19 industries, and 16 languages. In the state of Indiana, Eightfold maintains a partnership for people applying for unemployment insurance to build up their resumes and find job opportunities instantly. The startup also has a programme for veterans in the U.S. to help them find opportunities relevant to their skills.

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Jun 17, 2021

Facebook Develops AI to Crackdown on Deepfakes

Facebook
MSU
AI
Deepfakes
3 min
Social media giant, Facebook, has developed artificial intelligence that can supposedly identify and reverse-engineer deepfake images

In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale? 

 

Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.” 

 

The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with. 

Why Reverse Engineering? 

Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient. 

 

First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source. 

 

How It Works 

Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.

 

To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters. 

 

What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’. 

 

What Did They Find? 

‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising. 


Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.

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