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Wildlife photos are a treasure trove for AI-driven conservation research

If you look at a photograph of leopards, would you be able to tell which two were related based on their spots?

Unless you’re a leopard expert, the answer is most likely not, says Tanya Berger-Wolf, director of the Translational Data Analytics Institute (TDAI) at Ohio State University. But, she says, computers can.

Berger-Wolf and her team are pioneering a new field of study called imageomics. As the name suggests, imageomics uses machine learning to extract biological data from photos and videos of living organisms. Berger-Wolf and her team have recently begun collaborating with researchers studying leopards in India to compare spot patterns of moms and children using algorithms.

“Images have become the most abundant source of information now, and we have the technology, too. We have computer vision machine learning,” says Berger-Wolf. She compares this technology to the invention of the microscope, offering scientists a completely different way to look at wildlife.

Building on TDAI’s open-source platform called Wildbook, which helps wildlife researchers gather and analyze photos, the team is now focusing on generative AI approaches. These programs use existing content to generate meaningful data. In this case, they are attempting to analyze crowdsourced images to make biological traits that humans may naturally miss computable, like the curvature of a fish’s fin — or a leopard’s spots. The algorithms scan images of leopards publicly available online, from social media to digitized museum collections.

In simple terms, the algorithms “quantify the similarity,” she says. The aim is to help wildlife researchers overcome a data deficiency problem and, ultimately, better protect animals at risk of extinction.

Machine learning can be used to identify all the relevant parts of a photo, including wildlife of interest.
Photo by Tanya Berger-Wolf

Ecologists and other wildlife researchers are currently facing a data crunch — it’s tedious, expensive, and time-consuming for people to spend time in the field monitoring animals. Due to these challenges, 20,054 species on the International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species are labeled as “data deficient,” meaning there’s not enough information to make a proper assessment of its risk of extinction. As Berger-Wolf sums it up, “biologists are making decisions without having good data on what we’re losing and how fast.”

The platform started with supervised learning — Berger-Wolf says the computer uses algorithms “simpler than Siri” to count how many animals are in the image, as well as where it was taken and when, which could contribute to metrics like population counts. Not only can AI do this at a much lower cost than hiring people but also at a faster rate. In August 2021, the platform analyzed 17 million images automatically.

There are also barriers that only a computer can seem to overcome. “Humans are not the best ones at figuring out what’s the informative aspect,” she says, noting how humans are biased in how we see nature, focusing mostly on facial features. Instead, AI can scan for features humans would likely miss, like the color range of the wings on a tiger moth. A March 2022 study found that the human eye couldn’t tell male polymorphic wood tiger moth genotypes apart — but moth vision models with ultraviolet light sensitivity could.

“That’s where all the true innovation in all of this is,” Berger-Wolf says. The team is implementing algorithms that create pixel values of patterned animals, like leopards, zebras, and whale sharks, and analyze those hot spots where the pixel values change most — it’s like comparing fingerprints. Having these fingerprints means researchers can track animals non-invasively and without GPS collars, count them to estimate population sizes, understand migration patterns, and more.

As Berger-Wolf points out, population size is the most basic metric of a species’ well-being. The platform scanned 11,000 images of whale sharks to create hot spots and help researchers identify individual whale sharks and track their movement, which led to updated information about their population size. This new data pushed the IUCN to change the conservation status of the whale shark from “vulnerable” to “endangered” in 2016.

There are also algorithms using facial recognition for primates and cats, shown to be about 90 percent accurate, compared to humans being about 42 percent accurate.

Generative AI is still a burgeoning field when it comes to wildlife conservation, but Berger-Wolf is hopeful. For now, the team is cleaning the preliminary data of the leopard hot spots to ensure the results are not data artifacts — or flawed — and are true biologically meaningful information. If meaningful, the data could teach researchers how species are responding to changing habitats and climates and show us where humans can step in to help.

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AI-driven background check startup Intelligo raises $22M

Intelligo, an Israeli startup providing AI-driven background check solutions to investors, financial institutions, and corporations, today announced it has raised $22 million from Felicitas Global Partners, one of its long-standing clients. The company said that the growth round, which takes its total fund-raise to $44 million, will drive the development of its AI-powered risk intelligence platform — Clarity — and help set up shop in additional geographies.

For years, due diligence processes such as background verification have been manual and time-consuming (typically taking 10-15 days). Agencies providing these services often struggle with delays and backlogs, leaving end-users — people requiring the due diligence — little option but to provide temporary clearances. In some cases, analysts can also miss out on important sources of information such as news reports.

Automated background checks

Founded in 2014 by Shlomo Mirvis and Dana Rakovsky, Intelligo tackles this problem through automation, claiming to bring speed and accuracy into the whole process. The company’s Clarity intelligence system employs multiple layers of AI and automation to comb through more than 400,000 public data sources and gather all relevant information (legal, professional, regulatory, financial, online media) required to make a decision on a particular individual or company.

Then, a team of in-house experts reviews this information to create the final report for the customer.

“Each client has an account where they can log in and run a report at any time from any place, share and collaborate with other team members, and manage the account in a very simple way,” Mirvis, who is also the CEO of the company, told VentureBeat. The dashboard gives a 360-degree view of the gathered data, with full information of the sources and links to original documents, allowing customers to vet potential red flags in the risk profile.

Currently, the platform returns reports between 30 minutes and five days, and it is used by hundreds of customers, primarily investment banks, private equity firms, and hedge funds with combined assets under management of over $500 billion. Plus, even after producing the report, Clarity can continue to monitor the target and provide alerts as and when something changes in its background profile.

“We use our AI algorithms to weed out irrelevant data and deduplicate data to keep only the most relevant data in the report and help individuals arrive at the right conclusions. Our AI also uses advanced natural language processing analysis to identify adverse news articles written about our subjects,” Mirvis said.

Competitive space

Intelligo is not the only one solving the problem of legacy background checks with technology. Over the past few years, a number of players have come up in the same space, including Checkr, HireRight, Kroll, K2 Integrity, Paycom, and Exiger.

“There are a number of companies in the background check and human resources technology space targeting different ends of the market. Most are focused on general checks for hiring at all levels — from minimum wage positions on up. Intelligo, however, is focused on the high end of the market for both pre-hire and pre-investment decision making by firms, as part of their risk intelligence,” Mirvis added.

Targeting accuracy and revenue growth

Moving forward, the company aims to double its headcount and expand the capabilities of its risk intelligence platform to draw more customers.

While the CEO didn’t share how exactly the company plans to improve the platform, he did note that Intelligo continues to take recommendations from clients and improve the accuracy of the solution. Part of the effort is likely to be driven toward covering an even broader base of data sources, as background check reports are only as good as the data they are based on. The company has seen 200% revenue growth over the past three years and expects to clock 300% growth in the current year.

“The last 18 months have been very positive for Intelligo as remote and hybrid work structures have made due diligence processes and recruiting even more essential. A lack of face-to-face interaction takes away the ability to make intuitive, personal connections, so organizations are even more reliant on getting accurate and thorough intelligence that provides a 360-degree view of who candidates really are,” Mirvis said.

Globally, the overall market for due diligence, pre-investment and pre-hiring, is estimated to be valued at $17 billion.

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Data Society launches AI-driven meldR platform for data science training

Data Society, a Washington-headquartered organization providing data science training programs and AI/ML solutions to corporations and government agencies, has announced the launch of meldR, a learning experience and communication platform (LXCP).

Targeted at the health care and life sciences industries, the offering allows learning and development teams of businesses to deliver AI/ML-generated data science learning pathways to their employees. It curates courses according to the organization’s goals and the learner’s needs, and even works with proprietary datasets, allowing teams to offer courses using their own data.

“The healthcare and life science industry today faces the challenge of delivering centralized training programs effectively, which is a big roadblock to building an internal data culture,” Merav Yuravlivker, CEO of Data Society, said in a statement. “meldR supports an organization’s desire to prepare its employees with the skills needed to solve complex challenges and unlock the new potential that further their organization’s goals.”

According to a recent survey commissioned by Domino Data Lab, 97% of U.S. data executives say data science is crucial to maintaining profitability and boosting the bottom line. However, nearly as many said that flawed approaches to staffing, processes, and tooling are causing failure in scaling data science projects, making achieving that goal difficult. This is where meldR comes in.

Community of practice with meldR

In addition to providing industry-tailored, domain-specific data science academies, the platform also creates an internal community of practice that fosters innovation, empowers communication between employees and their L&D teams, and streamlines the process of finding the right talent for the right team.

This, as Data Society explains, is done through a series of tools on the platform such as messaging, email platform integration, notifications, discussion boards, calendars, online events, and one-on-one TA and instructor meetings.

Beyond this, L&D team leaders can even use their meldR dashboard to take a quick look at learner badges, pathways, and certifications to gather metrics and quickly identify up-skilled internal resources, matching internal talent and data science department requirements.

Data Society is offering the solution as a freemium product available on a rapid deployment model. It remains restricted to the healthcare and life sciences industry but should expand to other segments at a later stage.

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Inworld AI joins metaverse innovation with AI-driven virtual characters

Join gaming leaders online at GamesBeat Summit Next this upcoming November 9-10. Learn more about what comes next. 


Inworld AI, a company that aims to develop a platform that enables users to create AI-driven virtual characters which can be used to populate virtual worlds, announced today that it has raised $7 million in seed funding.

In an exclusive interview, Inworld’s cofounder and CEO Ilya Gelfenbeyn explained that “Inworld AI is a platform for building, basically brains for virtual characters” to populate virtual environments, including the metaverse, VR, and AR worlds. “What we provide is a toolset that enables developers to add brains and build these characters for the world, for different types of environments.”

To successfully create immersive characters, Inworld AI attempts to mimic the cognitive abilities of the human by leveraging a mixture of AI technologies like natural language understanding and processing, optical character recognition, reinforcement learning, and conversational AI to develop sophisticated virtual characters — characters that can even respond to questions and carry on conversations.

Inworld AI isn’t developing a solution to design visual avatars, but instead aims to create an AI development platform that enables companies that produce digital avatars and virtual characters to add more advanced communication to their visual designs.

The end goal of the platform is to offer a platform that visual avatar providers and organizations can use to develop “characters that can interact naturally with wide-ranging and completely open dialog,” Gelfenbeyn said. Although, speech is just the tip of the iceberg in terms of the communicative capabilities of these AI characters.

As Gelfenbeyn notes, “Inworld characters should not be limited to speech only, but be able to interact with many of the modalities that humans use, such as facial gestures, body language, emotions, as well as physical interactions.”

Enhancing the metaverse experience with AI brains

“We structure our technology stack based on inspiration from the human brain. We have three main components: perception, cognition, and behavior. Perception is focused on input and understanding of the environment and other agents, using senses like audio and visual,”  Gelfenbeyn said.

To enable virtual characters to perceive the environment audibly and visually, the organization uses a complex mixture of speech-to-text, rules engines, natural language understanding, OCR, and event triggers.

The next component is cognition.“Cognition is about the internal states of the character, such as memory, emotion, personality, goals, and background,” he said. Here Inworld AI will use natural language processing, emotion recognition, reinforcement learning, and goal-directed conversational AI to enhance the cognitive abilities of virtual characters.

Finally, “behavior is about the output or interactions of the character, such as speech gestures, body language, and motion.” Technologies like state-of-the-art generative language models, reinforcement learning, and customized voice and emotion synthesis,” enable virtual characters to replicate human gestures and behaviors.

Together, these three components provide a solid framework for developers to build virtual characters that can respond in detail to natural language, perceive the digital environment, and offer significant interactions for users.

Investors include Kleiner Perkins, CRV, and Meta. Inworld AI’s launch is well-timed, with publicity for the metaverse at an all-time high following Facebook’s rebrand to Meta, and decision-makers eager to identify what solutions are available to interact with customers in the metaverse.

As Izhar Harmony, General Partner of CRV explained, “the team is growing rapidly, so now is an exciting time for people interested in VR, games, and virtual worlds to partner with and join the company, so they can be at the forefront of this rapidly growing space.”

New kid on the block 

Inworld AI is entering into the highly competitive space of AI and machine learning development and competing against established providers like Open AI, and Google AI, that let you create machine learning models, yet Inworld AI fulfills a unique gap in the market, as it provides a highly specialized solution for developing conversational AI for AI-driven virtual characters, rather than generic machine learning models.

At the same time, the AI solutions that Inworld AI is developing will enable virtual character creation that extends well beyond the complexity of AI-driven avatars like Pandora Bots and Soul Machines.

“Many existing companies have solutions that provide limited answers to script triggers and dialog. In fact, our team built one of the largest providers of such services (API.ai, acquired by Google and now known as Google Dialogflow) so we are very familiar with their capabilities,” Gelfenbeyn said.

“Other companies are beginning to experiment with new technologies (such as large language models) but we believe that these parts, while essential, only provide one piece of the stack necessary to really bring characters to life,” he said.

In other words, these solutions have only scratched the surface of human-AI interactions, and Inworld AI’s approach to replicate human cognition is designed to create much more intelligent virtual entities. While Inworld AI’s mission to build AI brains for virtual characters is ambitious, the team’s AI development pedigree speaks for itself.

Inworld AI’s founders include a swath of experts such as Gelfenbeyn who was previously the CEO of API.ai, chief technology officer Michael Ermolenko, who led machine learning development at API.ai and the Dialogflow NLU/AI team at Google, and product director Kylan Gibbs, who previously led product for applied generative language models at DeepMind.

With this experienced team, the organization is in a strong position to set the standard for interactive virtual characters. After all, “Widespread success of the metaverse and other immersive applications depends on how enveloping those experiences can be,” said Ilya Fushman, investment partner at Kleiner Perkins.

“Inworld AI is building the engine that enables businesses to provide that exciting depth of experience and captivate users. With the team’s track record in providing developers with the tools they need to build AI-fueled applications, we’re excited to support the company in building the future of immersive experiences,” Fushman explained.

Virtual characters are key for immersion

With the metaverse boom beginning to pick up steam, Inworld AI also has a unique role to play in providing providers with a toolset that they can use to create sophisticated virtual characters and create more compelling digital experiences for users. The level of immersion offered by these experiences will determine whether the metaverse lives or dies.

The types of experiences that developers can use Inworld AI to build are diverse. As Gelfenbeyn explained, “Immersive realities continue to accelerate, with an increasingly diverse and fascinating ecosystem of worlds and use cases.”

“Virtual spaces like Meta’s Horizon Worlds, Roblox, Fortnite, and others that offer unique experiences and enable users to exist in other worlds will also continue to see quick demand from businesses, offering everything from games to story content to new enterprise applications,” Gelfenbeyn said.

Although Gelfenbeyn noted that the technology is simply to enable providers to create a “native population” for the digital world to offer realistic experiences, the metaverse is also becoming a new channel that technical decision-makers can use to interact with customers in the future.

While complete, immersive realities with sophisticated virtual characters are a long way off, Inworld AI’s team’s knowledge of conversational AI will undoubtedly enable other providers to move closer toward building vibrant, virtually populated, and interactive digital worlds.

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AI-driven strategies are becoming mainstream, survey finds

Deloitte today released the fourth edition of its State of AI in the Enterprise report, which surveyed 2,857 business decision-makers between March and May 2021 about their perception of AI technologies. Few organizations claim to be completely AI-powered, the responses show, but a significant percentage are beginning to adopt practices that could get them there.

In the survey, Deloitte explored the transformations happening inside firms applying AI and machine learning to drive value. During the pandemic, digitization efforts prompted many companies to adopt AI-powered solutions to back-office and customer-facing challenges. A PricewaterhouseCoopers whitepaper found that 52% percent of companies have accelerated their AI adoption plans, with global spending on AI systems set to jump from $85.3 billion in 2021 to over $204 billion in 2025, according to IDC.

However, only 40% of respondents to the Deloitte survey agreed that their employer has an enterprise-wide AI strategy in place. While 66% view AI as critical to their success, only 38% believe that their use of AI differentiates them from competitors and only about one-third say that they’ve adopted “leading operational practices” for AI.

“The risks associated with AI remain top of mind for executives,” Deloitte executive director of the AI institute Beena Ammanath said in a statement. “We found that high-achieving organizations report being more prepared to manage risks associated with AI and confident that they can deploy AI initiatives in a trustworthy way.”

Embracing AI is a marathon, not a sprint

To this end, “AI-fueled” businesses leverage data to deploy and scale AI across core processes in a human-centric way, according to Deloitte. Using data-driven decision-making, they enhance workforce and customer experiences to achieve an advantage, continuously innovating.

Organizations with an enterprise-wide strategy and leaders who communicate a bold vision are nearly twice as likely to achieve high-level outcomes, Deloitte reports. Furthermore, businesses that document and enforce MLOps processes are twice as likely to achieve their goals “to a high degree,” four times more likely to be prepared for AI risks, and three times more confident in their ability to deploy AI products “in a trustworthy way.”

MLOps, a compound of “machine learning” and “information technology operations,” is a newer discipline involving collaboration between data scientists and IT professionals with the aim of productizing machine learning algorithms. MLOps essentially aims to capture and expand on previous operational practices while extending these practices to manage the unique challenges of machine learning.

“Becoming an AI-fueled organization is to understand that the transformation process is never complete, but rather a journey of continuous learning and improvement,” Deloitte AI principal Nitin Mittal said.

Companies successfully adopting AI also haven’t ignored cultural and change management, the Deloitte report found. Those investing heavily in change management are 60% more likely to report that their AI initiatives exceed expectations and 40% more likely to achieve their desired goals. As for organizations that have undergone significant changes to workflows or added new roles, they’re almost 1.5 times more likely to achieve outcomes to a high degree, while 83% of the highest-achieving organizations create a diverse ecosystem of partnerships to execute their AI strategy, according to Deloitte.

But only 37% of decision-maker respondents reported a major investment in change management, incentives, or training activities, highlighting roadblocks companies will need to overcome. “By embracing AI strategically and challenging orthodoxies, organizations can define a roadmap for adoption, quality delivery, and scale to create or unlock value faster than ever before,” Deloitte AI principal Irfan Saif said.

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Pilot on the AI-driven ‘financial back office’

All the sessions from Transform 2021 are available on-demand now. Watch now.


Many startups fail due to cash flow challenges. Keeping an eye on financials is important, but bookkeeping and back office tasks might not be every startup’s strength. Especially when operating under tight seed money and capital, employing an entire back office might not make sound business sense. This is where Pilot, winner of VentureBeat’s 2021 AI Business Application Innovation award, comes in.

Founder and CEO Waseem Daher said Pilot runs “the financial back office for startups.” Daher said the company handles bookkeeping, tax preparation, budgeting, forecasting, and other business needs. Pilot hires full-time, U.S. based employees who specialize in working with startups to form the back office.

The startup’s financial challenges

While all companies have to keep an eye on the balance sheet, a startup has special financial challenges. The early stages for example, demand tracking compliance with employment laws and tax returns and the cash balance and burn rate. As the company grows, founders might want to track and plan for strategic hiring and growth. “They might want to really get in the weeds of a forecast or a budget or work with a fractional CFO to make sure they have a plan and are tracking against that plan,” Daher says.

“The biggest challenge is making sure you’re putting into place the appropriate groundwork for the business stage you’re in,” Daher said. Pilot, which was founded in 2017, has offices in San Francisco and Nashville and a deep bench of experts that keep an eye on such groundwork.

A little help from AI

The team at Pilot uses AI strategically. AI helps with automated visibility of errors and their management, predictive insights and context-specific reporting. The company anonymizes and aggregates financial data from the more than 1,000 startups on its rolls to create supervised machine learning models that can detect, for example, anomalies and unexpected variance in balance sheets. Vendor overpayments and tracking overdue invoices are just two of the many routine bookkeeping tasks AI models keep track of.

AI provides automated visibility, which means it flags non-traditional behavior and spending, while keeping aggregated charts of what spending (the burn rate) should look like. Context-specific insights are key, which means Pilot’s experts sift through the recommendations to separate the signal from the noise. The models learn as they go along.

Daher sees the AI models becoming increasingly sophisticated as analysis can be sliced and diced according to a number of variables such as geography — how do you fare with respect to other startups in your area — or growth stage or industry.

The challenges, Daher said, is in making sure to use AI where it shines best: on big data analytics and the routine gruntwork of making bookkeeping follow rules. AI is a workhorse and should be treated as such. The stars are still the human employees and the customers, Daher said.

Avoiding overfitting of models is another trap that Pilot makes sure it doesn’t fall into. For example, Daher pointed out, some startups might consider LinkedIn a recruiting expense while others might categorize it as a sales expense. Models need to be aware that one data point does not make a rule. “You may end up with an overly simplistic view of the world that may not always be accurate,” Daher said. “You have to design AI models with that sort of generality in mind.”

“Our engineers have built a ton of software to help our team do the work more accurately, more reliably, and more consistently. The AI is really the Iron Man suit for our internal finance teams,” Daher said. He expects Pilot to move to more unsupervised learning models in the future, which can yield aggregate insights in a scalable and automated fashion.

But, Daher added, “it’s not about using AI just for the sake of using AI. It’s about figuring out what’s going to yield the best possible customer experience and working backward from there.”

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AI-driven HR seeks to balance ‘human’ and ‘resources’

All the sessions from Transform 2021 are available on-demand now. Watch now.


Human resources (HR) is an area that is ripe for automation, and in particular, the kind of automation made possible by artificial intelligence (AI). HR, after all, is a cost center at most organizations, which means organizations are always looking for ways to keep costs as low as possible.

And yet, HR is rife with complex, time-consuming processes that, so far, have required the unique logic and intuitive thinking that only humans can provide.

A New World

But all that is changing with the newest generations of AI-driven HR platforms. Globality’s Sonia Mathai notes that everything from hiring and onboarding to scheduling and benefits management, and all the way to termination and access control, AI is creating a new brand of HR that is leaner, more accurate, and less costly than traditional HR.

For one thing, she says, AI-driven HR is available 24/7, delivering user-friendly services via fully conversational chatbots that provide immediate responses to most questions with no wait-listing. At the same time, AI can provide a more personalized experience due to its access to real-time data. And as seen with AI in other business units, all of this allows human reps to shed the rote, repetitive aspects of the job to focus on more creative, strategic solutions to endemic issues.

HR is such an important function at most companies that it should not be deployed lightly or haphazardly, according to Thirdera CIO Jeff Gregory. In a recent interview with Venture Beat, he pointed out that HR acts as the “steward of a company” and maintains the pulse of the health and development of employees. So it must consistently present the right information even when employees do not ask the right questions. For this reason, AI must learn the ins and outs of HR processes and resource utilization just like any employee, which is why it is best for it to start small and then work its way up to more complicated and consequential functions.

Be careful that AI doesn’t get you into legal trouble as well, says Eric Dunleavy, director of litigation and employment services at DCI Consulting Group, and Michelle Duncan, an attorney with Jackson Lewis. It’s one thing to use AI to prescreen applications, evaluate interviews, and mine social media. It’s quite another to have it decide who gets hired or promoted, particularly with the numerous examples of AI showing bias in regards to race, gender, age, and other factors. In the end, it is up to the company to ensure that all employees, whether human or digital, abide by established laws like Title VII of the Civil Rights Act, the Age Discrimination in Employment Act, and the Americans with Disabilities Act.

Crunching Numbers

Perhaps the most profound impact AI will have on HR is in analytics, rather than hiring or employee self-service tools. At its heart, HR is a numbers game, according to Erik van Vulpen, founder of the AIHR Academy, and AI is a whiz with numbers. For instance, AI can delve deep into turnover data to divine why employees are leaving and what can be done to correct it. As well, AI can assess the impact of learning and development programs, or determine which new hires will become top performers.  Ultimately, this will replace the “gut feeling” approach to decision-making in traditional HR shops to one that is more data-driven and quantifiable.

It’s been said that employees are the enterprise’s most valuable resource. In this case, organizations should proceed with caution when deciding how quickly and how thoroughly they want to integrate AI into their HR processes. People who take their jobs seriously might not maintain that attitude if they feel they cannot get a fair shake from an algorithm.

The best way to avoid this is to ensure that AI is trained to deliver positive outcomes, preferably ones that benefit the individual and the organization alike. If this is not possible, then there should be mechanisms in place, either human-driven or artificial, explaining why a given result has emerged and what the employee may do to alter it.

In the end, we all want to be treated fairly no matter who, or what, is making the decisions.

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Comcast’s AI-driven voice remote cuts through the glut of shows

All the sessions from Transform 2021 are available on-demand now. Watch now.


With the rise of on-demand TV shows and movies, viewers have a dizzying array of entertainment options to explore. Cable provider Comcast has been helping customers navigate this expansive content landscape using AI via its Xfinity voice remote.

The remote taps machine learning to help customers decide what to watch and when to watch it, providing users with a tailored at-home video experience, Comcast CTO Matthew Zelesko explained at VentureBeat’s virtual Transform 2021 conference.

“The content landscape has grown dramatically. And we realized that it was much harder for customers to determine even simple questions, like what to watch and where to watch it,” Zelesko said. “Navigating to the content you want is really so important. And we felt voice was just the most natural way to do that.”

Comcast invested in AI to find better ways to provide a personalized viewing experience, Zelesko said. Doing so required a deep understanding of content and how to provide it, and the voice remote helped streamline this process. Comcast released the first version of the remote as an app in 2015 to explore voice interfaces. The company said it received overwhelmingly positive feedback and shipped millions of remotes in the first year.

“More than 70% of our customers with a voice remote use voice commands at least once a week. And those who use the remote consistently spend more time watching more content than those who don’t,” Zelesko said. “So when we make it easy to discover what they want, customers use more of our services, and they use them longer.”

AI backed by data

Comcast attributes the remote’s success to the sheer amount of data it consolidates, which helps it understand users’ preferences. Understanding content metadata, like genre and cast, is critical to the customer’s content discovery experience. To organize its knowledge, Comcast uses CoMPASS, a cloud-based metadata platform to make informed decisions about consumer preferences and experiences.

A new feature called deep metadata utilizes computer vision to understand what is happening in scenes, which could allow customers to jump to the most important highlights in a recorded football game, for example. It uses the show’s closed captioning and computer vision of the video to pinpoint specific on-screen highlights to feature.

To ensure accurate labeling, the metadata classification relies on overlapping annotations — where a small percentage of images analyzed by computer vision are also annotated by two human analysts and assessed for discrepancies to refine the algorithm.

“Without that great organized metadata about all of the hundreds of thousands of pieces of content, no amount of AI or machine learning is going to get customers to what they want,” Zelesko said.

Buy or build conundrum

Comcast licenses much of the technology it builds internally, like the voice remote, to other cable providers in the United States, Canada, and Europe. Zelesko said Comcast reviewed available commercial products before developing the remote and realizing nothing on the market met its need for accurate and easy-to-use natural language processing. No other company was indexing the quantity and complexity of metadata, so “no off-the-shelf platform was going to work,” Zelesko said.

“We still buy where it makes sense, and where there’s a great solution that meets our needs in the market. But given our scale, and also really the unique nature of the problems that we’re solving, we find that we get the best results when we build to meet our customers’ needs,” he said.

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AI

Charli.ai CEO on training AI-driven personal assistants

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Charli.ai’s digital personal assistant that employs AI to organize a user’s life is expected to enter beta this summer. Known as Charli, this personal assistant promises to do everything from finding all related documents in email and cloud storage systems to organizing receipts and tax filings.

VentureBeat sat down with Charli.ai founder and CEO Kevin Collins to better understand how AI is about to transform the way we all work.

This interview has been edited for brevity and clarity.

VentureBeat: What exactly does Charli do?

Kevin Collins: It’s really about keeping yourself organized and finding what you need. If you go into email and try to find something you’re looking for, it’s a nightmare trying to search for it. If you’re going into your cloud storage to find documents that you filed a year ago, it’s a nightmare to find them. Charli is really about keeping your digital content organized, allowing you to find things instantly when you need to find them. That includes files that you might be keeping in email or cloud storage, but it also includes links to the internet. We get inundated with links to every piece of content that’s out there. [Keeping] track of it can just be handed off to Charli.

VentureBeat: Sounds like everybody can now have their own digital assistant?

Collins: We wanted Charli to be like that personal assistant for you. We’ve got a whole natural language processor in the front end of it that Charlie can understand. For example, if I say “Charli, show my expenses for this month,” it will understand that. It’s also designed for speech that we haven’t yet integrated. We haven’t integrated with Alexa or Google. We’re still at an early stage. We’re coming out of our beta program in the summer. The whole idea about Charli was providing a personal assistant for me. The name Charli is a bit of a play on chief of staff. We dropped the E because we wanted [the name] to be gender-neutral.

Venture Beat: What is the business strategy for Charli?

Collins: There will be a free version of Charli so that people are comfortable with it just doing the organization. If there are more sophisticated use cases, there’s going to be a subscription-based service. If you sign up for the premium package or professional package, you’ll get different access to different aspects of the AI. For example, the pro package will allow you to enable Charli to read your invoices and receipts, pull out the very specific information, and then send that off to QuickBooks for you.

Venture Beat: What role do you think AI has to play in this helping people and kind of navigating their work from anywhere scenario?

Collins: Getting back to that pre-pandemic normal isn’t going to happen. There’s going to be a lot more remote work. That’s going to put a lot of pressure on organizations to be far more productive with the remote workers. There’s no longer the ability to walk down the hallway to talk to somebody. There’s no longer the ability to just put something on the internal mail and send it off to somebody. There needs to be a new way of getting employees productive, and that means more automation. AI has a massive role to play.

Venture Beat: Will employees embrace that idea, or are they fearful of AI?

Collins: When we talk to our customers and our users, they’re quite excited about the potential of AI. They see AI taking a big load off for them. There’s a glimmer of hope that AI can take some of the pain away, but there is an undercurrent of fear of what that means from a job perspective. Is it going to take jobs away? And the short answer to that is yes. You will see a shift in the labor market. A lot of these manual tedious jobs that require a lot of people are now going to go away, but there’s going to be demand for other types of jobs, especially in skilled labor. There is a shift in the labor market that’s going to affect different people in different ways. Some are going to be negatively impacted, while others going to be positively impacted.

Venture Beat: What else are people fearful of when it comes to AI?

Collins: The other fear, and I think it’s a real one, is that there is an inherent bias in AI. We’re starting to talk about that a lot more. There’s more emphasis on auditing the AI algorithms. There’s a lot more emphasis on making sure that they are behaving in a standardized way that is positive rather than negative. AI is mathematical models. They’re trained to behave a certain way, and they’re trained by people and organizations. There is an underlying fear that they’re going to be trained in a negative way.

VentureBeat: Do you think we will get to the point where we have instances of AI that are optimized for opposite outcomes that will eventually do battle with one another?

Collins: I would say yes. We want Charli to be biased [in your favor] because it’s your personal assistant to a certain degree. But we need to introduce diversity into that, so that means competing viewpoints. If you’ve got various decisions that have to be made, there has to be a decision criterion that comes from diverse AI models. You have to be able to consult those diverse models and make a decision on which one suits the case or the instance. AI has to be very contextually aware, which means different training, different algorithms, and they have to compete against each other for the right decision at the right time for the right reason.

VentureBeat: Don’t I just wind up with multiple personal secretaries making different recommendations based on their biases?

Collins: That actually gets into some of our intellectual property because we don’t want multiple personal assistants. You want that one personal assistant, but you want that personal assistant to have a diverse set of inputs in order to make the right decision for you. There are competing AI models that have to really fight each other in order to make a confidence-level decision for you. It’s your personal assistant inspecting these competing decisions and making the right decision for you. You don’t have to deal with multiple personal assistants, you deal with one, but you want the confidence in that one to make the right decision.

Venture Beat: How long does it take Charli to be trained?

Collins: It’s a loaded question. The short answer is this is really hard. To get Charli to be brilliant at doing all this takes a couple of years, but for Charli to learn some basics to understand how to organize your life is quite quick. I’ve been using Charli now for a little over a year. Charli rarely comes back and asks me questions anymore, whereas at the beginning Charli was asking me a lot of clarification questions. It’s just not a matter of going to the shelf, getting a machine learning algorithm, and thinking it’s going to work out of the box. That doesn’t happen anywhere. You can go and get all these algorithms out of the box, but you have to invest in the data, the training, the testing, and the automation of the continuous learning processes. That is a very heavy investment. For other companies considering this, they’re really going to be in for a bit of a shock of how much work it is to get this right.

VentureBeat: How will we ultimately know Charli is getting it right?

Collins: We’ve invested a lot in guardrails because we need the AI to behave a certain way. There are a lot of guardrails around laws, restrictions, rules, and policies for humans. We need those types of constraints in AI as well. That is another heavy investment area because we just don’t want AI to think outside the box for us. We want AI to simply take the pain and aggravation of automation off of our plate. There’s a massive investment that went into testing AI.

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AI

Stoke nabs $15.5M to boost its AI-driven freelance management system

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Stoke is announcing it has raised $15.5 million in a series A round of funding. The company, which offers a freelance management system (FMS) to help enterprises manage independent contractors, freelancers, consultants, agencies, and gig workers, will use the funds to build out engineering, product marketing, and sales, Stoke cofounder and CEO Shahar Erez told VentureBeat.

In terms of the product itself, he says the company wants to expand its partner ecosystem for marketplaces with sources for talent, including improving the experience for sourcing and adding a greater variety of sourcing capabilities. The company will also work toward launching global compliance for classification, rounding out compliance offerings for the U.S. and some European countries that Erez said are now “pretty solidified.”

In March, Stoke launched its Worker Classification Engine, an AI-powered system that analyzes companies’ relationships with contractors and freelancers and alerts them to potentially costly compliance risks. The model is built on more than 3,000 classification cases filed in the U.S.

“We’re seeing more regulatory changes that are impacting the future of work,” Erez said, mentioning classification, data compliance, and tax regulations. “Companies that need to move faster and have a growing number of independent contractors do not have the right tooling in place to build toward this new composition of workforce.”

The future of work

In addition to spurring a shift to remote work, the pandemic greatly accelerated dependence on freelance and contract work. An Upwork survey from September revealed that 59 million Americans performed freelance work in the last 12 months, an increase of 2 million since 2019. These freelancers represent 36% of the total U.S. workforce and half of the Gen Z workforce, with Upwork citing a “tough job market for recent college graduates” as a reason behind the boom. By 2027, it’s estimated that 86.5 million people — 50.9% of the total U.S. workforce — will be freelancing, according to Statista. The rapid shift has made it difficult for some enterprises to keep up with the workforce changes happening inside their own companies.

“A lot of these organizations don’t even know how many independent contractors they have,” Erez said. “Ask any CFO, CEO, or head of HR and you won’t find a single person who knows. There are so many things wrong with that because there is really no process. No process for effectively onboarding, no process to make sure they’re signing the right legal documents, and then we’re seeing events like freelancers not getting paid on time. Again, no one’s bad intention. It’s just a huge mess.”

Indeed, payments are a significant problem — 71% of freelancers report difficulty getting paid, according to the Freelancers Union. This type of arrangement also typically excludes workers from accessing company health care benefits and other support structures, like paid time off. The narrative is often that workers are going freelance for the flexibility, but the loss of millions of full-time jobs during the pandemic certainly plays a role. Zip Recruiter reported the percentage of temporary job postings on its platform jumped from 24% to 34% when the pandemic hit. Meanwhile, 90% of active job seekers on the platform are looking for a permanent, full-time position.

Financing the pivot

With these workforce changes rapidly underway, companies that can help enterprises navigate their extended worker base are raking in investor cash. In March, HoneyBook announced it had raised $155 million at a $1.1 billion valuation post-money. Utmost, which functions specifically for Workday customers, also announced a $21 million series B round of funding last month. Other companies in this space include giants like Fiverr and Upwork, as well as Freelancer.com and Guru.com.

This latest round marks a total of $20 million in funding for Israel-based Stoke, which launched in 2019. The round was led by Battery Ventures, with participation from all previous investors and angels, including TLV Partners, Dynamic, and Loop.

“We’ve been interested for years in companies shaping the future of work, and the pandemic has made that investment thesis even more relevant and led directly to our interest in Stoke,” Itzik Parnafes, general partner at Battery Ventures, told VentureBeat.

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