Technology
The next Like A Dragon game recasts a series regular as an amnesiac pirate
Ryu Ga Gotoku Studio simply cannot stop pumping out Like A Dragon (aka Yakuza) games. The studio and publisher Sega have revealed that the next entry will hit PS4, PS5, Xbox One, Xbox Series X/S and Steam on February 28, just 13 months after debuted. The latest spinoff has a typically kooky twist that’s not exactly kept secret by its title: Like a Dragon: Pirate Yakuza in Hawaii.
A shown at the studio’s features Goro Majima, a regular of the series, explaining what’s been going on with him recently. About six months earlier, Majima washed up on an island near Hawaii with no memory of how he got there, only to be helped out by a child with a pet tiger cub. It didn’t take long until Majima ran afoul of some pirates and swiftly became a pirate captain himself.
Pirate Yakuza in Hawaii takes place a year after the events of Infinite Wealth and Ichiban Kasuga’s exploits in that game. You’ll assemble your crew, upgrade your ship, engage enemy vessels and discover hidden islands. Majima will have two fighting styles that you can switch between on the fly. Opt for the Mad Dog option to vex enemies with “speed, agility and flair,” and then switch to Sea Dog to dual wield short swords and “pirate tools,” according to a press release. However you slice it, Pirate Yakuza in Hawaii already looks way more fun than the 45 minutes I spent playing .
While February 28 isn’t too far away in the grand scheme of things, there are plenty of other Like A Dragon-related things to help keep you occupied in the meantime. , a live-action TV show based on the series, will . The franchise is also with a port of Yakuza Kiwami, a remake of the first game.
Science & Environment
Antarctica’s “Doomsday Glacier” is set to retreat “further and faster,” scientists warn
The outlook for “Doomsday Glacier” just got gloomier.
Scientists are warning the Antarctic Ice Sheet, known formally as the Thwaites Glacier, will deteriorate “further and faster” and that sea level rise triggered by the melting could impact “hundreds of millions” in coastal communities.
“Towards the end of this century, or into the next century, it is very probable that we will see a rapid increase in the amount of ice coming off of Antarctica,” said Dr. Ted Scambos, a glaciologist at the University of Colorado. “The Thwaites is pretty much doomed.”
The findings are the culmination of six years of research conducted by the International Thwaites Glacier Collaboration, a collective of more than 100 scientists.
The “Doomsday Glacier,” roughly the size of the state of Florida, is one of the largest glaciers in the world. Scientists predict that its collapse could contribute to 65 centimeters, or roughly 26 inches in sea levels to rise.
The sea level rise could be even higher though if you account for the ice the Thwaites will draw in from the large surrounding glacial basins when it collapses. “That total will be closer to three meters of sea level rise,” Scambos said.
According to the researchers, the volume of water flowing into the sea from the Thwaites and its neighboring glaciers has doubled from the 1990s to the 2010s.
Approximately 1/3 of the front of the Twaites is currently covered by a thick plate of ice — an ice shelf — floating in the ocean that blocks ice from flowing into the sea. However, Scambos said the melting is accelerating and that the ice sheet is “very near to the point of breakup.”
“Probably within the next two or three years, it will break apart into some large icebergs,” he said. This will eventually leave the front of the glacier exposed. This may not necessarily lead to a sudden acceleration in melting, but it will change how the ocean interacts with the front of the ice shelf, Scambos said.
Deep ridges that prevent ice from flowing into the ocean are on their way out. The ridges, in the bedrock below the ice sheet in Antarctica, provide a “resistive force” against the ice, Scambos said, that slows down its flow into the ocean. As the Thwaites collapses, it will lose contact with these protective ridges, causing more ice to empty into the ocean.
One of the more surprising findings to come from the International Thwaites Glacier Collaboration was how tidal activity around the glacier is pumping warmer sea water into the ice sheet at high speed. That water, which is a couple of degrees above freezing, is getting trapped in parts of the glacier and forced further upstream.
“It goes in every day, it gets squashed up under the glacier. It completely melts whatever freshwater ice it can, and then it gets ejected, and then the whole thing starts again,” said Scambos.
The new findings from the International Thwaites Glacier Collaboration add to a vast body of research on how the deterioration of glaciers worldwide could contribute to sea level rise. In May, a study found that high-pressure ocean water is seeping beneath the “Doomsday Glacier” leading to a “vigorous ice melt.”
Study co-author Christine Dow called the Thwaites the “most unstable place in the Antarctic” and said the speed at which its melting could prove “devastating for coastal communities around the world.”
Researchers at the University of California, Irvine predicted the ocean could rise by about 60 centimeters, or about 23.6 inches, roughly on par with the predictions from scientists part of the International Thwaites Glacier Collaboration.
Scientists also have also warned about the potential consequences if the Greenland ice sheet were to melt. Greenland’s melting ice mass is now the No. 1 driver of sea level rise, according to Paul Bierman, a scientist at the University of Vermont. If it melts completely, scientists project it could lead to 20 to 25 feet of sea-level rise.
Rising global temperatures linked to climate change have made oceans warmed and generated new wind patterns that make these glaciers more susceptible to melting.
“It is very likely related to increasing greenhouse gasses in the atmosphere, which changed wind patterns around Antarctica, and therefore changed ocean circulation around Antarctica,” said Scambos. “That’s the main culprit.”
Scientists project that without intervention, the Thwaites could completely disappear by the 23rd century.
Technology
Exploding interest in GenAI makes AI governance a necessity
Enterprises need to heed a warning: Ignore AI governance at your own risk.
AI governance is essentially a set of policies and standards designed to mitigate the risks associated with using AI — including generative AI — to inform business decisions and automate processes previously carried out by human beings.
The reason it’s now needed, that it cannot be ignored, is that enterprise interest in generative AI is exploding, which has led to more interest in traditional AI as well.
Historically, AI and machine learning models and applications were developed by and used mostly by small data science teams and other experts within organizations, never leaving their narrow purview. The tools were used to do forecasting, scenario planning and other types of predictive analytics, as well as automate certain repetitive processes also overseen by small groups of experts.
Now, however, sparked by OpenAI’s November 2022 launch of ChatGPT, which represented significant improvement in large language model capabilities, enterprises want to extend their use of AI tools to more employees to drive more rapid growth. LLMs such as ChatGPT and Google Gemini enable true natural language processing that was previously impossible.
When combined with an enterprise’s data, true NLP lets any employee with an internet connection and the requisite clearance query and analyze data in ways that previously required expert knowledge, including coding skills and data literacy training. In addition, when applied to enterprise data, generative AI technology can be trained to relieve experts of repetitive tasks, including coding, documentation and even data pipeline development, thus making developers and data scientists more efficient.
That combination of enabling more employees to make data-driven decisions and improving the efficiency of experts can result in significant growth.
If done properly.
If not, enterprises risk serious consequences, including decisions based on bad AI outputs, data leaks, legal noncompliance, customer dissatisfaction and lack of accountability, all of which could lead to financial losses.
Therefore, as interest in expanding the use of both generative AI and traditional AI transitions to more actual use of AI, and as more employees with less expertise get access to data and AI tools, enterprises need to ensure their AI tools are governed.
Some organizations have heeded the warning, according to Kevin Petrie, an analyst at BARC U.S.
“It is increasing,” he said. “Security and governance are among the top concerns related to AI — especially GenAI — so the demand for AI governance continues to rise.”
However, according to a survey Petrie conducted in 2023, only 25% of respondents said their organization has the proper AI governance controls to support AI and machine learning initiatives, while nearly half said their organization lacks the proper governance controls.
Diby Malakar, vice president of product management at data catalog specialist Alation, similarly said he has noticed a growing emphasis on AI governance.
SingleStore customers, like those of most data management and analytics vendors, have expressed interest in developing and deploying generative AI-driven tools. And as they build and implement conversational assistants, code translation tools and automated processes, they are concerned with ensuring proper use of the tools.
“In every customer call, they are saying they’re doing more with GenAI, or at least thinking about it,” Malakar said. “And one of the first few things they talk about is how to govern those assets — assets as in AI models, feature stores and anything that could be used as input into the AI or the machine learning lifecycle.”
Governance, however, is hard. Data governance has been a challenge for enterprises for years. Now, AI governance is taking its place alongside data governance as a requirement as well as a challenge.
Data has long been a driver of business decisions.
For decades, however, data stewardship and analysis were the domain of small teams within organizations. Data was kept on premises, and even high-level executives had to request that IT personnel develop charts, graphs, reports, dashboards and other data assets before they could use them to inform decisions.
The process of requesting information, developing an asset to analyze the information and reaching a decision was lengthy, taking at a minimum a few days and — depending on how many requests were made and the size of the data team — even months. With data so controlled, there was little need for data governance. Then, a bit less than 20 years ago, self-service analytics began to emerge. Vendors such as Tableau and Qlik developed visualization-based platforms that enabled business users to view and analyze data on their own, with proper training.
With data no longer the sole domain of experts — and with trained experts no longer the only ones in control of their organization’s data, and business users empowered to take action on their own — organizations needed guidelines.
And with data in the hands of more people within an enterprise — still only about a quarter of all employees, but more than before — more oversight was needed. Otherwise, organizations risked noncompliance with government regulations and data breaches that could reveal sensitive information or cost a company its competitive advantage.
A similar circumstance is now taking place with AI — albeit at a much faster rate, given all that has happened in less than two years — that necessitates AI governance. Just as data was once largely inaccessible, so was AI. And just as self-service analytics enabled more people within organizations to use data, necessitating data governance, generative AI is enabling more people within organizations to use AI, necessitating AI governance.
Donald Farmer, founder and principal of TreeHive Strategy, noted a similarity between the rising need for AI governance and events that necessitated data governance.
“That is a parallel,” he said. “It’s a reasonable one.”
However, what is happening with AI is taking place much more quickly and on a much larger scale than what happened with self-service analytics, Farmer continued. AI has the potential to completely alter how businesses conduct themselves, if properly governed. Farmer compared what AI can do for today’s enterprises to what electricity did for businesses at the turn of the 20th century. At the time, widespread electrical use was dangerous. In response, organizations employed what were then known as CEOs — chief electricity officers — who oversaw the use of electricity and made sure safety was maintained.
“This is a very fundamental shift that we’re just seeing the start of,” Farmer said. “It’s almost as fundamental as [electricity] — everything you do is going to be affected by AI. The comparison with self-service analytics is accurate, but it’s even more fundamental than that.”
Alation’s Malakar similarly noted parallels to be drawn between self-service analytics and the surging interest in AI. Both are rooted in less-technical employees wanting to use technology to help make decisions and take action.
“What we see is that the business analyst who doesn’t know coding wants less and less reliance on IT,” Malakar said. “They want to be empowered to make decisions that are data-related.” First, that was enabled to some degree by self-service analytics. Now, it can be enabled to a much larger degree by generative AI. Every enterprise has questions such as how to reduce expenses, predict churn or implement the most effective marketing campaign. AI can provide the answers.
And with generative AI, it can provide the answers to virtually any employee.
“They’re all AI/ML questions that were not being asked to the same degree 10 years ago,” Malakar said. “So now all these things like privacy, security, explainability [and] accountability become very important — a lot more important than it was in the world of pure data governance.”
At its core, AI governance is a lot like — and connected to — data governance. Data governance frameworks are documented sets of guidelines to ensure the proper use of data, including policies related to data privacy, quality and security. In addition, data governance includes access controls that limit who can do what with their organization’s data.
AI governance is linked to data governance and essentially builds on it, according to Petrie.
AI governance applies the same standards as data governance — practices and policies designed to ensure the proper use of AI tools and accuracy of AI models and applications. But without good data governance as a foundation, AI governance loses significance.
Before AI models and applications can be effective and used to inform decisions and automate processes, they need to be trained using good, accurate data. “[Data governance and AI governance] are inextricably linked and overlap quite a bit,” Petrie said. “All the risks of AI have predecessors when it comes to data governance. You should view data governance as the essential foundation of AI governance.”
Most enterprises do have data governance frameworks, he continued. But the same cannot be said for AI governance, as Petrie’s 2023 survey demonstrated.
“That signals a real problem,” he said.
The problem could be one that puts an organization at a competitive disadvantage — that they’re not ready to develop and deploy AI models and applications and reap their benefits, while competitors are doing so. Potentially more damaging, however, is if an enterprise is developing and deploying AI tools, but isn’t properly managing how they’re used. Rather than simply holding back growth, this could lead to negative consequences. But AI governance is about more than just protection from potential problems. It’s also about enabling confident use of AI tools, according to Farmer.
Good data governance frameworks strike a balance between putting limits on data use aimed to protect the enterprise from problems and supporting business users so that they can work with data without fearing that they’re going to unintentionally put their organization in a precarious position.
Good AI governance frameworks need to strike that same balance so that someone asking a question of an AI assistant isn’t afraid that the response they get and subsequent action they take will have a negative effect. Instead, that user needs to feel empowered.
“People are beginning to come around to the idea that a well-governed system gives people more confidence in being able to apply it at scale,” Farmer said. “Good governance isn’t a restricting function. It should be an enabling function. If it’s well governed, you give people freedom.” Specific elements of a good framework for AI governance combine belief in the need for a system to manage the use of AI, guidelines that properly enforce the system and technological tools that assist in its execution, according to Petrie.
“AI governance is defining and enforcing policies, standards and rules to mitigate risks related to AI,” he said. “To do that, you need people and process and technology.”
The people aspect starts with executive support for an AI governance program led by someone such as a chief data officer or chief data and analytics officer. Also involved in developing and implementing the framework are those in supporting roles, such as data scientists, data architects and data engineers.
The process aspect is the AI governance framework itself — the policies that address security, privacy, accuracy and accountability. The technology is the infrastructure. Among other tools, it includes data and machine learning observability platforms that look at data quality and pipeline performance, catalogs that organize data and include governance capabilities, master data management capabilities to ensure consistency, and machine learning lifecycle management platforms to train and monitor models and applications.
Together, the elements of AI governance should lead to confidence, according to Malakar. They should lead to conviction in the outputs of AI models and applications so that end users can confidently act. They should also lead to faith that the organization is protected from misuse.
“AI governance is about being able to use AI applications and foster an environment of trust and integrity in the use of those AI applications,” Malakar said. “It’s best practices and accountability. Not every company will be good at each one of [the aspects of AI governance], but if they at least keep those principles in mind, it will lead to better leverage of AI.”
Confidence is perhaps the most significant outcome of a good AI governance framework, according to Farmer. When the data used to feed and train AI models can be trusted, so can the outputs. And when the outputs can be trusted, users can take the actions that lead to growth. Similarly, when the processes automated and overseen by AI tools can be trusted, data scientists, engineers and other experts can use the time they’ve been given by being relieved of mundane tasks to take on new ones that likewise lead to growth.
“The benefit is confidence,” Farmer said. “There’s confidence to do more with it when you’re well governed.”
More tangibly, good AI governance leads to regulatory compliance and avoiding the financial and reputational harm that comes with regulatory violations, according to Petrie. Europe, in particular, has stringent regulations related to AI, and the U.S. is similarly expected to increase regulatory restrictions on exactly what AI developers and deployers can and cannot do.
Beyond regulatory compliance, good AI governance results in good customer relationships, Petrie continued. AI models and applications can provide enterprises with hyperpersonalized information about customers, efficiently enabling personalized shopping experiences and cross-selling opportunities that can increase profits. “Those benefits are significant,” Petrie said. “[But] if you’re going to take something to customers — GenAI in particular — you better make sure you’re getting it right, because you’re playing with your revenue stream.”
If enterprises get generative AI — or traditional AI, for that matter — wrong, i.e., if the governance framework controlling how AI models and applications are developed and deployed is poor, the consequences can be severe.
“All sorts of bad things can happen,” Petrie said.
Some of them are the polar opposite of what can happen when an organization has a good AI governance framework. Instead of regulatory compliance, organizations can wind up with inquisitive regulators, and instead of strong customer relationships, they can wind up with poor ones. But those are the end results.
First, what leads to inquisitive regulators and poor customer relationships, among other things, includes poor accuracy, biased outputs and mishandling of intellectual property.
“If those risks are not properly controlled and mitigated, you can wind up with … regulatory penalties or costs related to compliance, angry or alienated customers, and you can end up with operational processes that hit bottlenecks because the intended efficiency benefits of AI will not be delivered,” Petrie said.
Lack of data security, explainability and accountability are other results of poor AI governance, according to Malakar. Without the combination of good data governance and AI governance, there can be security breaches as well as improperly prepared data — personally identifiable information that hasn’t been anonymized, for example — that seeps into models and gets exposed. In addition, without good governance, it can be difficult to explain and fix bad outputs in a timely fashion or know whom to hold accountable.
“You don’t want to build a model where it can’t be trusted,” Malakar said. “That’s a risk to the entire culture of the company and can drive morale issues.”
Ultimately, just as good AI governance engenders confidence, bad AI governance leads to a lack of confidence, according to Farmer.
If one competing company trusts its AI models and applications and another doesn’t, the one that can act with confidence will reap the benefits, while the other will be stuck in place and miss out on the growth opportunities enabled by generative AI’s significant potential. “Given that the shift is so fundamental, not being well governed is really going to hold you back,” Farmer said. “Governance is the difference between the ability to move swiftly and with confidence, and being held back and taking dangerous risks.”
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.
Surging need
Elements of AI governance
Benefits and consequences
Technology
28 Years Later was partially shot on an iPhone 15 Pro Max
Danny Boyle’s zombie sequel 28 Years Later was shot using several iPhone 15 Pro Max smartphones, . This makes it the biggest movie ever made using iPhones, as the budget was around $75 million.
There are some major caveats worth going over. First of all, the sourcing on the story is anonymous, as the film’s staff was required to sign an NDA. Also, the entire film wasn’t shot using last year’s high-end Apple smartphone. Engadget has confirmed that Boyle and his team used a bunch of different cameras, with the iPhone 15 Pro Max being just one tool.
Finally, it’s not like the director just plopped the smartphone on a tripod and called it a day. Each iPhone looks to have been adapted to integrate with full-frame DSLR lenses. Speaking of, those professional-grade lenses cost a small fortune. The phones were also nestled in protective cages.
Even if the phones weren’t exclusively used to make this movie, it’s still something of a full-circle moment for Boyle and his team. The original 28 Days Later was shot primarily on a that cost $4,000 at the time. This camcorder recorded footage to MiniDV tapes.
28 Years Later is the third entry in the franchise and is due to hit theaters in June 2025. The film stars Jodie Comer, Aaron Taylor-Johnson, Ralph Fiennes and Cillian Murphy. This will be the first of three new films . Plot details are non-existent, but all three upcoming movies are being written by Alex Garland. He co-wrote the first one and has since gone on to direct genre fare like Ex Machina, and, most recently, Civil War. He also made a truly underrated .
As for the intersection of smartphones and Hollywood, several films have been shot with iPhones. These include Sean Baker’s Tangerine and Steven Soderbergh’s Unsane.
Technology
Netflix reveals new games based on Rebel Moon and other shows
As part of its Geeked Week announcements, Netflix revealed more details about new games coming to its platform. Several of the games are based on the company’s shows, including Rebel Moon and Squid Game, while others such as Monument Valley 3 are well-regarded IPs in their own right. Several of the games are coming in 2025, while others are s…Read More
Technology
M&As and AI are in the spotlight, but there’s still capital left for quick commerce and more
Welcome to Startups Weekly — your weekly recap of everything you can’t miss from the world of startups. Want it in your inbox every Friday? Sign up here.
This week brought reassuring signs that dealmaking is still happening on both sides of the table. New unicorns are being minted, and more capital is flowing into AI, but deals are also coming from some unexpected directions.
Most interesting startup stories from the week
AI news was plentiful this week, but also varied, from large and small M&As to new launches.
AI portfolio: Typeface, a generative AI unicorn, purchased two companies to expand its enterprise offering: New York City-based Treat, which uses AI to create personalized photo products, and Narrato, an Australian AI-powered content creation and management platform.
AI again: Global HR company Workday bought AI-powered contract management platform Evisort, adding to its AI-related acquisitions. The companies didn’t disclose the price tag, but Evisort had raised $155.6 million in capital and debt.
FinOps FTW: IBM acquired Kubecost, a Kubernetes cost optimization startup, as its name suggests. This is another sign of the ongoing rise of FinOps, which may also be boosted by the need to lessen the cost and impact of GenAI.
Only you: Recently launched SocialAI is a social network with a big twist — it is filled with bots, and that’s on purpose. Founder Michael Sayman told TechCrunch that his goal was for users to be able to bounce ideas off a diverse community of AIs.
Most interesting fundraises this week
This week was also busy on the dealmaking front, and some of the capital went to sectors and places you might not necessarily expect.
Flying solo: Quick commerce app Flink raised $150 million, including $115 million in equity. The near-unicorn was once an acquisition target of competitors but is now seeking to forge its own path, with a focus on Germany and the Netherlands.
On alert: New York-based startup Intezer raised $33 million to make sure security teams aren’t overwhelmed by alerts. Using AI, the startup helps them with not only triaging, but also with investigation, which it does much faster than a human would, CEO Itai Tevet said.
Getting permits: NYC-based GreenLite raised a $28.5 million Series A round to facilitate construction permitting. Its co-founders don’t come from the construction sector but previously worked at Gopuff, which got its own taste of dealing with permits when it tried to launch a ghost kitchen network across the U.S.
Tailwinds: Armenian B2B SaaS startup EasyDMARC raised a $20 million Series A round of funding to simplify email security and authentication. The company facilitates the adoption of a technical standard that Google and Yahoo will soon make mandatory for bulk email senders.
Most interesting VC and fund news this week
Accelerating: Salesforce Ventures announced at Dreamforce that its San Francisco-based AI fund would once again double in size and reach $1 billion, a significant acceleration compared to the $5 billion total deployed in its first 15 years.
Decacorn fund: Insights Partners is nearing a whopping $10 billion fundraise for its 13th fund, according to the Financial Times, which also noted the recent sales of two Insight portfolio companies, Own and Recorded Future.
Builders: Proptech venture firm Era Ventures raised $88 million for its first fund, which will be deployed in startups from seed to Series B. Its portfolio includes Honey Homes, a subscription service for handymen that has raised $21.35 million in venture funding to date.
Last but not least
In a recent episode of the Equity podcast, J.P. Morgan’s Head of Startup Banking Ashraf Hebela discussed his recent Startup Insights report and what it might take to create a unicorn in 2024. He also touched on the hot topic of “Founder Mode.”
Technology
8BitDo now sells the NES-themed keycaps from its retro keyboard
8BitDo is now selling a set of keycaps featuring the same Nintendo Entertainment System (NES) inspired design as those used on the Retro Mechanical Keyboard it debuted last July. While the keyboard is now available in four styles including Commodore 64 and Famicom designs, only the NES style keycaps are currently available on their own.
The $49.99 8BitDo Retro Keycaps set includes 165 PBT keys with legends printed using dye-sublimation for added durability. The expanded set allows the keys to be used on larger keyboards with a dedicated number pad. 8BitDo’s $99.99 mechanical keyboards are only available in a shorter tenkeyless layout.
The set features alternate designs for some keys like a spacebar with an added health meter in two different lengths, and both the American ANSI and international ISO versions of others, like an Enter key with an inverted L design, and a smaller Shift key. 8BitDo says the set supports 65, 75, 80, 95, and 100-percent layouts, as well as ergonomic split keyboards, but compatibility is limited to MX-style switches.
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