OpenAI has told some investors that it expects to burn $278 billion by the end of 2030, the Financial Times reported, as it spends more on cloud computing and chips to run and train its AI. The forecast, based on a recent presentation, is higher than the one it gave investors early this year, ...
Google acknowledged that its Gemini AI model unexpectedly breached the networks of three outside companies during safety evaluations conducted by third-party testing firm Irregular last May, The Wall Street Journal reported.
During the exercise, Gemini gained entry to the companies’ systems by ...
Anthropic finance chief Krishna Rao and other executives met with groups of prospective public investors last month with his company’s numbers looking spiffier than ever. The company had gone from spending about $2.30 on operations for every $1 of revenue in spring of 2025 to turning a slight profit on that basis in the June quarter—bucking the stereotype of money-losing AI companies that are addicted to spending.
But some investors walked away from meetings with the understanding from executives that the profits would likely be short-lived. Anthropic would take advantage of its sudden growth and deep cash reserves to aggressively sign data center deals, executives told them, which would increase how much they spend on computing infrastructure and could shrink operating profits.
Nvidia-backed startup Nscale disclosed Friday that revenue had surged 10 times in the first half of this year from the year-ago period, but losses mounted as the cloud startup spent billions on data centers and the AI chips they’ll house.
The two-year-old UK startup, a spinout of an Australian ...
In a room full of IT professionals, words and phrases like “tokenization,” “retrieval-augmented generation,” and “context window” need no explanation. But when technology leaders present to their CEOs and other company leaders about topics like AI, they often find that they need to translate these terms into everyday business language, or else risk being met by blank stares.
In the moment, this can be maddening. You know the opportunities and challenges that a technology like generative AI presents for your company. And you know that the organization’s other leaders will likely see things the same way you do, if only you can make yourself understood.
Instead of trying to bridge this communication gap on the fly, use Google Gemini to help translate your technical vision into bottom-line business value.
Step 1: Figure Out What You Want to Say
Your CEO might not speak “tech,” but Google Gemini does. Open up a new chat and explain exactly what it is you hope to communicate to leadership. Here, you can feel free to be as technical as you wish. Maybe you’ll tell Gemini that you’re thinking of building a RAG pipeline to vectorize your company’s proprietary data. Or, maybe you’ll float the idea of using parameter-efficient fine-tuning methods like LoRA to deploy open-weight models on-premises, without exploding your cloud inference costs. You can even upload your technical documents to Gemini, directly in the chat window.
No matter how in-the-weeds you get, Gemini will follow your train of thought, parsing the complexity to find the business value at the core of your plans.
Step 2: Let Gemini Be Your Translator
Now that you’ve captured your vision in your own words, let Gemini help you translate those words into language that will connect with your audience. Use this sample prompt, along with your brainstorming from Step 1:
I am the CIO at [company], and I am trying to build the case to [adopt generative AI solutions]. I need to explain the technical concept of generative AI to a non-technical audience (the CEO and board). Help me write talking points that will help me convey what generative AI is, ways it could help us digitally transform, and why it’s important to our growth as a company. Include details about how it could potentially re-focus our technical talent on more strategic work, help enhance our workforce’s productivity, and help us better serve our global workforce and customers.
In just a few moments, you’ll have a set of boardroom-ready talking points. Gemini might, for instance, break down the benefits of RAG in simple terms by noting that it anchors AI to internal data like HR policies and technical manuals. Or, it might point out the business benefits of arming IT staff and engineers with generative AI tools, noting the opportunity to shift employees’ focus from low-value, repetitive tasks to innovation.
Step 3: Create Content
Now that you have your core narrative and language nailed down, Gemini can help you create a more structured pitch. Instead of staring at a blank slide deck, prompt Gemini for ideas on how to build out a presentation that will sway your peers in the C-suite. Try this prompt:
I need to build a presentation to deliver this information to the board. Turn these talking points into a 10-slide presentation. For each slide, write a headline, three to four short bullet points for the slide itself, and the speaker notes I should use to explain the concept out loud.
Gemini will generate a full, downloadable presentation deck directly in the chat. If you like, you can export the presentation to Google Slides and make your own edits. Or, you can command Gemini to generate a new version with changes to elements like design, language, and narrative flow.
Step 4: Make Gemini Your Sparring Partner
Before you go into your meeting, open a new chat in Gemini, providing your speaker notes and a link to your presentation, and then ask it to act as a skeptical executive: You are my CEO. Review this presentation and grill me on cost, security, and ROI. Ask your toughest questions, one at a time.
Even though Gemini helped you prepare your presentation, it will seamlessly pivot to the role of rigorous executive or tough board member, pressing for more details on project payback, hidden risks, and timeline. By practicing your pitch in this low-stakes environment, you can make certain that you’ll be confident when it’s time for the real thing.
Imagine you are at a cocktail party. The room is crowded, people are coming and going, and you can barely hear your friend as they tell you some juicy gossip about the date they went on last week.
The fact you can still follow what their saying is down to your brain performing some very complex operations amid all the noise. Out of all the sound waves reaching your ears, it is successfully isolating the one of interest while filtering out the rest.
Trying to understand how the brain does this has kept auditory neuroscientists occupied since the 1950s, when British cognitive scientist Colin Cherry first coined the term “the cocktail party problem”.
This was initially studied in experiments that asked volunteers to listen to two speeches simultaneously – but only repeat one of them aloud afterwards. Cherry found that listeners could follow and repeat the pre-selected message remarkably well, despite both speeches being played simultaneously, one in each ear. In contrast, they recalled very little of the other message.
These experiments laid the foundation for decades of selective attention research. But it is only in the past 15 years that scientists have studied this phenomenon in experiments that more closely resemble the complicated, multi-layered way we listen in the real world – using technology that accurately tracks the brain’s activity as speech unfolds.
The most common non-invasive option, electroencephalography (EEG), uses what looks like a swimming cap fitted with small sensors. These measure very fast changes in the electrical activity of populations of neurons (the brain’s nerve cells) with millisecond precision.
In a 2014 study, neuroscientists found that, within 200 milliseconds of hearing someone speak, the brain favours the conversation someone is focusing on over all other noise. This prioritised attention shows up most markedly in secondary brain areas such as the superior temporal gyrus, which are responsible for piecing together the sounds we hear into meaningful speech.
This suggests that our attentive brain is not just enhancing the voice of who we are listening to, but helping us make sense of their words while filtering out all other conversations and background noise.
The original cocktail party problem did not investigate the full range of people’s listening experiences, which are often much less static. In everyday life, we repeatedly switch and reorient our auditory attention – for example, when hearing our name called across a crowded room, or when a door slams.
But what happens in that brief moment when we stop listening to one person and turn our attention to another? Can the brain switch from one voice to another instantly, or does it briefly have to process both? These were questions we investigated in our latest study with colleagues from the Eriksholm Research Centre in Denmark, which is funded by Danish hearing aid manufacturer Oticon.
Clarifying how the brain manages these rapid shifts in attention can help explain why some conversations are easy to follow while others become difficult, especially in noisy environments.
Coming back to the cocktail party: here you are, focused on your friend’s voice as they reveal the identity of their recent date. But then another familiar voice suddenly reaches your awareness. Your partner is (secretly) telling a friend about the surprise birthday gift they are planning for you. Your attention immediately shifts to your partner’s voice, while still giving the appearance of listening to your friend.
When we tested such a scenario in our laboratory, we found something striking. During this attention reorienting, the brain does not simply drop one voice and turn to the other. Rather, for a short period of between one and two seconds, the neural signals associated with the two voices overlap. In other words, the brain starts tuning into the new speaker before it has completely let go of the old one.
This mechanism may help explain how we navigate the constantly changing soundscape around us, moving our attention from one voice or sound to another while maintaining enough information about the previous speaker to make that transition seamless. This efficient processing strategy could be described as the brain maintaining a smooth “attention continuum”.
These insights have some important real-world applications. For example, traditional hearing aids are often designed to favour sounds coming from in front of the listener, which is obviously limiting in many situations.
But using enhanced these attention decoding techniques, hearing aids could be neurally steered not only to enhance the speaker that is currently attended to, but to switch between different speakers, almost in real time, as the listener’s attention is dynamically reoriented.
More generally, understanding how the brain shifts, maintains and combines information from different speakers can give us a clearer picture of how attention shapes what we ultimately hear, understand and remember.
The approach introduced in our study could be particularly useful for studying how individuals vary in their attentional strategies, particularly in noisy environments – and for relating these differences to the mental effort they use to follow and understand speech.
Ultimately, this brings us closer to understanding how the brain makes successful communication possible – at cocktail parties and everywhere else.
Sara Carta received funding from the William Demant Fonden and Taighde Éireann – Research Ireland.
Giovanni Di Liberto receives funding from William Demant Fonden and Taighde Éireann – Research Ireland. He is also supported by Research Ireland at ADAPT, the Research Ireland Centre for AI-Driven Digital Content Technology at Trinity College Dublin.
The cops who’re using Flock cameras to supposedly track down criminals and stalk their ex-girlfriends alike are quite literally laughing in our faces as they tap into the all-seeing surveillance panopticon.
According to a new investigation by the Electric Frontier Foundation, a not insignificant number of police officers are submitting nonsense, joke reasons to officially justify the searches they conduct on Flock systems.
One officer with the Lake County, Indiana Sheriff’s Department wrote “LMAO” in the Flock software’s “reason” box, before searching across more than 19,000 cameras in over 1,500 cities and towns.
The frivolous reasoning is a case in point of why there’s so much outrage over Flock’s AI-powered cameras — which are nominally intended to be licenese plate scanners — and how they’re frequently being abused by cops. Unlike traditional surveillance cameras, Flock’s are part of a centralized network and require no warrants to use. With such little oversight, it’s easy for police officers to conduct sweeping searches for wanton reasons.
In its investigation, which was reported on by 404 Media, the EFF found dozens of examples of cops writing childish responses like “LMAO,” “LOL,” “Hehe,” and “IDK,” in the reason box for Flock searches.
An alarming number simply button mashed their keyboards. “ghjhghjg,” was a sufficient reason for one officer in Oklahoma to plow through the recordings of over 83,000 cameras. Thousands of others simply wrote uninformative responses like “investigation” and “test.” The odd cop left short little messages, all seemingly unaware that their stated reasons would become public record.
“robbery I don’t remember the case number leave me alone,” one wrote.
“f*** this new search engine,” wrote another.
Following up on its findings, the EFF contacted police departments and found that few, if any, of the cops were meaningfully disciplined for their flippant entries. At most, a few were “counseled,” but by and large the departments tried to deflect and offered excuses.
Flock, conveniently, changed its software in late 2025 so that cops can no longer type in their own “reason.” Now, they’re provided a dropdown list of pre-made reasons instead. The EFF blasted the move for causing a “loss of transparency,” as it obviously doesn’t encourage cops to be more serious. It merely papers over the fact that they weren’t treating the powerful surveillance tools seriously in the first place.
“This culture of abuse has allowed police to treat a mass surveillance network like their own personal search engine,” condemned the EFF in a post about its findings, “permitting the tracking of the movements of everyday citizens for low-level complaints, personal whims, and sometimes, seemingly, for the lols.”
More on privacy: Trump Throws His Weight Behind Flock Cameras
The post Cops Are Giving Complete Nonsense Reasons to Search Thousands of Flock Cameras appeared first on Futurism.
Login