NASA’s Europa Clipper mission, set to visit the icy moon of Jupiter, was set to launch from the Kennedy Space Center in Florida this week but had its launch delayed because of Hurricane Milton. Now, NASA has announced that it is targeting no earlier than Monday, October 14, for the launch, and we’ve got the details on how you can watch the event live.

What to expect from the Europa Clipper launch

The mission intends to explore Europa, the moon of Jupiter that has a liquid water ocean beneath a thick, icy shell. Because of the presence of liquid water there, scientists want to learn whether the moon could be potentially habitable, as it is one of the most promising locations that life could survive outside of Earth. The mission will search for information about the ocean and the presence of the building blocks of life, called organic compounds, to see if the ingredients for life are present there.

Europa Clipper had been scheduled to launch this week on Thursday October 10, but the launch was postponed because of the hurricane conditions around the Kennedy Space Center. The spacecraft had to be secured against the high winds and heavy rain in its hanger at Launch Complex 39A.

“The safety of launch team personnel is our highest priority, and all precautions will be taken to protect the Europa Clipper spacecraft,” said Tim Dunn, senior launch director at NASA’s Launch Services Program, at the time. “Once we have the ‘all-clear’ followed by facility assessment and any recovery actions, we will determine the next launch opportunity for this NASA flagship mission.”

NASA Kennedy confirmed that it was all clear after the storm, and now teams are continuing to check the status of the spacecraft and the ground systems, but NASA has confirmed it is targeting Monday onward for the launch.

How to watch the Europa Clipper launch

The launch will be livestreamed by NASA, which you can watch either using the NASA+ app or using the YouTube video embedded above. The YouTube video currently has a date of November 6 on it, but this is just a placeholder date and not when the actual launch will take place.

The exact time of the launch hasn’t been announced yet, but you can follow NASA’s X (formerly Twitter) account to get details as soon as the time is made public.

If you love streaming TV, Xbox games, and as little clutter as possible, then you need to pick up this Amazon Fire TV bundle deal that gets you set up with all the Xbox games you could ever want to play. The regular price for this bundle is $146.97, but Amazon currently has it on sale for $79.99. At that price, you’re getting a very good value and, it’s the lowest this bundle has ever been. Prior to this discount, which is 45% off the regular price, this bundle was $111.97. You’re basically saving another $32 by picking it up now as opposed to its previous price.

The best thing about this bundle is that you get everything you need to play Xbox games. The Fire TV Stick 4K Max gives you access to the Xbox Game Pass app. The bundle also comes with a 30-day subscription to Xbox Game Pass Ultimate. So once you get that 30-day sub applied, all you need is a controller. As luck would have it this bundle comes with an Xbox Wireless Controller as well. The Sky Cipher Special Edition model to be exact.

If you don’t like the Sky Cipher color, Amazon is offering the bundle with the Astral Purple and Deep Pink colors as well. Xbox Game Pass is great because you get access to tons of games in Microsoft’s curated list, which is always adding new titles to play. Now since this is on a Fire TV Stick 4K Max, you’ll be playing Xbox games in the cloud through the cloud feature of Xbox Game Pass Ultimate. So you will need an internet connection.

In addition to the games, the Fire TV Stick 4K Max gives you access to tons of other streaming apps. Including Disney Plus, Netflix, Hulu, Apple TV Plus, Prime Video, and many more. It’s definitely a killer deal you don’t want to pass up.

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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.