TikTok’s executives and employees were well aware that its features foster compulsive use of the app, as well as of its corresponding negative mental health effects, according to NPR. The broadcasting organization reviewed the unredacted documents from the lawsuit filed by the Kentucky Attorney General’s Office as published by the Kentucky Public Radio. More than a dozen states sued TikTok a few days ago, accusing it of “falsely claiming [that it’s] safe for young people.” Kentucky Attorney General Russell Coleman said the app was “specifically designed to be an addiction machine, targeting children who are still in the process of developing appropriate self-control.”

Most of the documents submitted for the lawsuits had redacted information, but Kentucky’s had faulty redactions. Apparently, TikTok’s own research found that “compulsive usage correlates with a slew of negative mental health effects like loss of analytical skills, memory formation, contextual thinking, conversational depth, empathy, and increased anxiety.” TikTok’s executives also knew that compulsive use can interfere with sleep, work and school responsibilities, and even “connecting with loved ones.”

They reportedly knew, as well, that the app’s time-management tool barely helps in keeping young users away from the app. While the tool sets the default limit for app use to 60 minutes a day, teens were still spending 107 minutes on the app even when it’s switched on. That’s only 1.5 minutes shorter than the average use of 108.5 minutes a day before the tool was launched. Based on the internal documents, TikTok based the success of the tool on how it “improv[ed] public trust in the TikTok platform via media coverage.” The company knew the tool wasn’t going to be effective, with one document saying that “[m]inors do not have executive function to control their screen time, while young adults do.” Another document reportedly said that “across most engagement metrics, the younger the user, the better the performance.”

In addition, TikTok reportedly knows that “filter bubbles” exist and understands how they could potentially be dangerous. Employees conducted internal studies, according to the documents, wherein they found themselves sucked into negative filter bubbles shortly after following certain accounts, such as those focusing on painful (“painhub”) and sad (“sadnotes”) content. They’re also aware of content and accounts promoting “thinspiration,” which is associated with disordered eating. Due to the way TikTok’s algorithm works, its researchers found that users are placed into filter bubbles after 30 minutes of use in one sitting.

TikTok is struggling with moderation, as well, according to the documents. An internal investigation found that underage girls on the app were getting “gifts” and “coins” in exchange for live stripping. And higher-ups in the company reportedly instructed their moderators not to remove users reported to be under 13 years old unless their accounts state that they indeed are under 13. NPR says TikTok also acknowledged that a substantial number of content violating its rules get through its moderation techniques, including videos that normalize pedophilia, glorify minor sexual assault and physical abuse.

TikTok spokesman Alex Haurek defended the company and told the organization that the Kentucky AG’s complaint “cherry-picks misleading quotes and takes outdated documents out of context to misrepresent our commitment to community safety.” He also said that TikTok has “robust safeguards, which include proactively removing suspected underage users” and that it has “voluntarily launched safety features such as default screentime limits, family pairing, and privacy by default for minors under 16.”

Data is the holy grail of AI. From nimble startups to global conglomerates, organizations everywhere are pouring billions of dollars to mobilize datasets for highly performant AI applications and systems.

But, even after all the effort, the reality is accessing and utilizing data from different sources and across various modalities—whether text, video, or audio—is far from seamless. The effort involves different layers of work and integrations, which often leads to delays and missed business opportunities. 

Enter California-based ApertureData. To tackle this challenge, the startup has developed a unified data layer, ApertureDB, that merges the power of graph and vector databases with multimodal data management. This helps AI and data teams bring their applications to market much faster than traditionally possible. Today, ApertureData announced $8.25 million in seed funding alongside the launch of a cloud-native version of their graph-vector database.

“ApertureDB can cut data infrastructure and dataset preparation times by 6-12 months, offering incredible value to CTOs and CDOs who are now expected to define a strategy for successful AI deployment in an extremely volatile environment with conflicting data requirements,” Vishakha Gupta, the founder and CEO of ApertureData, tells VentureBeat. She noted the offering can increase the productivity of data science and ML teams building multimodal AI by ten-fold on an average. 

What does ApertureData bring to the table?

Many organizations find managing their growing pile of multimodal data— terabytes of text, images, audio, and video daily— to be a bottleneck in leveraging AI for performance gains.

The problem isn’t the lack of data (the volume of unstructured data has only been growing) but the fragmented ecosystem of tools required to put it into advanced AI.

Currently, teams have to ingest data from different sources and store it in cloud buckets – with continuously evolving metadata in files or databases. Then, they have to write bespoke scripts to search, fetch or maybe do some preprocessing on the information.

Once the initial work is done, they have to loop in graph databases and vector search and classification capabilities to deliver the planned generative AI experience. This complicates the setup, leaving teams struggling with significant integration and management tasks and ultimately delaying projects by several months. 

“Enterprises expect their data layer to let them manage different modalities of data, prepare data easily for ML, be easy for dataset management, manage annotations, track model information, and let them search and visualize data using multimodal searches. Sadly their current choice to achieve each of those requirements is a manually integrated solution where they have to bring together cloud stores, databases, labels in various formats, finicky (vision) processing libraries, and vector databases, to transfer multimodal data input to meaningful AI or analytics output,” Gupta, who first saw glimpses of this problem when working with vision data at Intel, explained.

Prompted by this challenge, she teamed up with Luis Remis, a fellow research scientist at Intel Labs, and started ApertureData to build a data layer that could handle all the data tasks related to multimodal AI in one place. 

The resulting product, ApertureDB, today allows enterprises to centralize all relevant datasets – including large images, videos, documents, embeddings, and their associated metadata – for efficient retrieval and query handling. It stores the data, giving a uniform view of the schema to the users, and then provides knowledge graph and vector search capabilities for downstream use across the AI pipeline, be it for building a chatbot or a search system. 

“Through 100s of conversations, we learned we need a database that not only understands the complexity of multimodal data management but also understands AI requirements to make it easy for AI teams to adopt and deploy in production. That’s what we have built with ApertureDB,” Gupta added.

How is it different from what’s in the market?

While there are plenty of AI-focused databases in the market, ApertureData hopes to create a niche for itself by offering a unified product that natively stores and recognizes multimodal data and easily blends the power of knowledge graphs with fast multimodal vector search for AI use cases. Users can easily store and delve into the relationships between their datasets and then use AI frameworks and tools of choice for targeted applications.

“Our true competition is a data platform built in-house with a combination of data tools like a relational / graph database, cloud storage, data processing libraries, vector database, and in-house scripts or visualization tools for transforming different modalities of data into useful insights. Incumbents we typically replace are databases like Postgres, Weaviate, Qdrant, Milvus, Pinecone, MongoDB, or Neo4j– but in the context of multimodal or generative AI use cases,” Gupta emphasized.

ApertureData claims its database, in its current form, can easily increase the productivity of data science and AI teams by an average of 10x. It can prove as much as 35 times faster than disparate solutions at mobilizing multimodal datasets. Meanwhile, in terms of vector search and classification specifically, it is 2-4x faster than existing open-source vector databases in the market.

The CEO did not share the exact names of customers but pointed out that they have secured deployments from select Fortune 100 customers, including a major retailer in home furnishings, a large manufacturer and some biotech, retail and emerging gen AI startups.

“Across our deployments, the common benefits we hear from our customers are productivity, scalability and performance,” she said, noting that the company saved $2 million for one of its customers. 

As the next step, it plans to continue this work by expanding the new cloud platform to accommodate the emerging classes of AI applications, focusing on ecosystem integrations to deliver a seamless experience to users and extending partner deployments.

Japan’s startup sector, despite being one of the biggest in the world, has lagged behind other regions like the U.S., China, and the U.K., in terms of the number of unicorns and the scale of venture capital investment. For years, an aging population, overall economic deflation, and salarymen’s inclination to work at traditional, big corporations meant the startup life wasn’t an attractive one for many.

For context: Per a recent IMF report that cites CB Insights data, as of October 2023, the U.S. had about 661 unicorns, China counted 172, and the U.K. had 52. Japan had a mere seven unicorns. (PitchBook pegs the number of Japanese startups at nine, so it’s possible we have more unicorns in the market than these datasets suggest.)

But things are looking up — somewhat. Young graduates are increasingly breaking from the mold, opting to strike out on their own instead of working within existing corporate systems. And the Japanese government is trying to attract interest in the country’s startups once again.

The government’s “Startup Development Five-Year Plan,” for one, was launched in 2022 and aims to help create 100,000 startups and foster 100 unicorns by 2027 by promoting incubators, strengthening funding with a venture fund, diversifying exit avenues, and more. The Tokyo Metropolitan Government earlier this year launched Tokyo Innovation Base, a startup hub that organizes networking events and pitch competitions and offers workspaces for founders. There’s also a Startup Visa that makes it easier for venture capital firms, startups, and accelerators to set up in Japan, and there’s a special tax system for angel investors. It helps that the country is home to about 130 accelerators, which isn’t too bad given the size of the market.

Despite these advantages, most of the venture capital invested in Japan comes from outside it. The IMF report mentioned found that between 2010 and 2023, investors from the U.S. accounted for 50% of investment in Japanese startups, investors from the U.K. made up about 10%, and Japanese investors lagged at only 5%.

For example, Bessemer Venture Partners recently invested for the first time in a Japanese startup, a food-delivery company called Dinii. “Having been fortunate to be a key investor in Toast in the U.S., supporting it to become a $13 billion company, we see a similar element of success in Dinii,” Bryan Wu of Bessemer Venture Partners said at the time.

Japanese startups usually decide to go public sooner in their development than startups in other countries. For example, they may go public after just a couple of funding rounds, thanks to the Tokyo Stock Exchange’s lenient IPO rules. So it’s likely we might see the unicorns listed below doing an IPO sooner than later.

Here are a few unicorns from Japan that are worth keeping an eye on.

Spiber

Total funding raised: $653 million

Last funding round: $65 million (10 billion JPY) in April 2024

Key investors: Baillie Gifford, Fidelity Investments, Goldwin, Kansai Paint, Iowa Economic Development Authority, Shinsei Bank, and the Carlyle Group.

Spiber grabbed investor, and customers’, attention quite quickly with its environment-friendly biomaterials that have a huge array of applications. Companies across the fashion, cosmetics, and automotive industries use Spiber’s materials instead of animal, plant, or synthetic materials, and its customers include Pangaia, the North Face, Goldwin, Woolrich, Shiseido Japan, and Toyota.

In April this year, it raised about $65 million (10 billion JPY) to scale up production of its “Brewed Protein” materials, which have applications in textile production. It has 300 employees, and the company last year set up an office in Paris to promote its business in Europe.

SmartNews

Total funding raised: $479 million

Last funding round: $69.3 million venture debt round in January 2024

Key investors: Atomico, Asian Capital Alliance, Development Bank of Japan, Globis Capital Partners, Japan Post Capital, JIC Venture Growth Investments, SMBC Venture Capital, Social Venture Partners, Princeville Capital, and Woodline Partners.

Founded in 2012, news aggregator SmartNews sought to take a new approach as a news provider: It partnered with publications to offer a personalized and streamlined news feed to users. It launched in the U.S. in 2014 and quickly saw its fortunes burgeon. It became the first news startup to achieve a billion-dollar valuation since 2015, and then in 2021, its valuation shot up to $2 billion.

The startup, however, has found it difficult to retain users as social media platforms like X, Threads, Mastodon, and Bluesky try to position themselves as places to read breaking news. The startup counted 1.7 million daily active users between Q1 2023 and Q3 2023, down nearly 30% from a year earlier, according to SensorTower.

SmartHR

Total funding raised: $362 million

Last funding round: $140 million Series E in June 2024