Technology
Google Gemini: everything you need to know about the new generative artificial intelligence platform

Google is trying to impress with Gemini, its flagship suite of generative AI models, applications and services.
So what are Gemini? How can you use it? And how does it compare to the competition?
To help you sustain with the latest Gemini developments, we have created this handy guide, which we’ll keep updating as new Gemini models, features, and news about Google’s plans for Gemini grow to be available.
What is Gemini?
Gemini is owned by Google long promised, a family of next-generation GenAI models developed by Google’s artificial intelligence labs DeepMind and Google Research. It is available in three flavors:
- Gemini Ultrathe best Gemini model.
- Gemini Pro“lite” Gemini model.
- Gemini Nanoa smaller “distilled” model that works on mobile devices like the Pixel 8 Pro.
All Gemini models were trained to be “natively multimodal” – in other words, able to work with and use greater than just words. They were pre-trained and tuned based on various audio files, images and videos, a big set of codebases and text in various languages.
This distinguishes Gemini from models akin to Google’s LaMDA, which was trained solely on text data. LaMDA cannot understand or generate anything beyond text (e.g. essays, email drafts), but this isn’t the case with Gemini models.
What is the difference between Gemini Apps and Gemini Models?
Image credits: Google
Google, proving once more that it has no talent for branding, didn’t make it clear from the starting that Gemini was separate and distinct from the Gemini web and mobile app (formerly Bard). Gemini Apps is solely an interface through which you can access certain Gemini models – consider it like Google’s GenAI client.
Incidentally, Gemini applications and models are also completely independent of Imagen 2, Google’s text-to-image model available in a few of the company’s development tools and environments.
What can Gemini do?
Because Gemini models are multimodal, they will theoretically perform a spread of multimodal tasks, from transcribing speech to adding captions to images and videos to creating graphics. Some of those features have already reached the product stage (more on that later), and Google guarantees that each one of them – and more – can be available in the near future.
Of course, it is a bit difficult to take the company’s word for it.
Google seriously fell in need of expectations when it got here to the original Bard launch. Recently, it caused a stir by publishing a video purporting to show the capabilities of Gemini, which turned out to be highly fabricated and kind of aspirational.
Still, assuming Google is kind of honest in its claims, here’s what the various tiers of Gemini will give you the option to do once they reach their full potential:
Gemini Ultra
Google claims that Gemini Ultra – thanks to its multimodality – may help with physics homework, solve step-by-step problems in a worksheet and indicate possible errors in already accomplished answers.
Gemini Ultra can be used for tasks akin to identifying scientific articles relevant to a selected problem, Google says, extracting information from those articles and “updating” a graph from one by generating the formulas needed to recreate the graph with newer data.
Gemini Ultra technically supports image generation as mentioned earlier. However, this feature has not yet been implemented in the finished model – perhaps because the mechanism is more complex than the way applications akin to ChatGPT generate images. Instead of passing hints to a picture generator (akin to DALL-E 3 for ChatGPT), Gemini generates images “natively” with no intermediate step.
Gemini Ultra is accessible as an API through Vertex AI, Google’s fully managed platform for AI developers, and AI Studio, Google’s online tool for application and platform developers. It also supports Gemini apps – but not totally free. Access to Gemini Ultra through what Google calls Gemini Advanced requires a subscription to the Google One AI premium plan, which is priced at $20 monthly.
The AI Premium plan also connects Gemini to your broader Google Workspace account—think emails in Gmail, documents in Docs, presentations in Sheets, and Google Meet recordings. This is useful, for instance, when Gemini is summarizing emails or taking notes during a video call.
Gemini Pro
Google claims that Gemini Pro is an improvement over LaMDA by way of inference, planning and understanding capabilities.
Independent test by Carnegie Mellon and BerriAI researchers found that the initial version of Gemini Pro was actually higher than OpenAI’s GPT-3.5 at handling longer and more complex reasoning chains. However, the study also found that, like all major language models, this version of Gemini Pro particularly struggled with math problems involving several digits, and users found examples of faulty reasoning and obvious errors.
However, Google promised countermeasures – and the first one got here in the type of Gemini 1.5 Pro.
Designed as a drop-in substitute, Gemini 1.5 Pro has been improved in lots of areas compared to its predecessor, perhaps most notably in the amount of information it could actually process. Gemini 1.5 Pro can write ~700,000 words or ~30,000 lines of code – 35 times greater than Gemini 1.0 Pro. Moreover – the model is multimodal – it isn’t limited to text. Gemini 1.5 Pro can analyze up to 11 hours of audio or an hour of video in various languages, albeit at a slow pace (e.g., looking for a scene in an hour-long movie takes 30 seconds to a minute).
Gemini 1.5 Pro entered public preview on Vertex AI in April.
An additional endpoint, Gemini Pro Vision, can process text images – including photos and videos – and display text according to the GPT-4 model with Vision OpenAI.

Using Gemini Pro with Vertex AI. Image credits: Twins
Within Vertex AI, developers can tailor Gemini Pro to specific contexts and use cases through a tuning or “grounding” process. Gemini Pro can be connected to external third-party APIs to perform specific actions.
AI Studio includes workflows for creating structured chat prompts using Gemini Pro. Developers have access to each Gemini Pro and Gemini Pro Vision endpoints and might adjust model temperature to control creative scope and supply examples with tone and elegance instructions, in addition to fine-tune security settings.
Gemini Nano
The Gemini Nano is a much smaller version of the Gemini Pro and Ultra models, and is powerful enough to run directly on (some) phones, slightly than sending the job to a server somewhere. So far, it supports several features on the Pixel 8 Pro, Pixel 8, and Samsung Galaxy S24, including Summarize in Recorder and Smart Reply in Gboard.
The Recorder app, which allows users to record and transcribe audio with the touch of a button, provides a Gemini-powered summary of recorded conversations, interviews, presentations and more. Users receive these summaries even in the event that they do not have a signal or Wi-Fi connection available – and in a nod to privacy, no data leaves their phone.
Gemini Nano can be available on Gboard, Google’s keyboard app. There, it supports a feature called Smart Reply that helps you suggest the next thing you’ll want to say while chatting in the messaging app. The feature initially only works with WhatsApp, but can be available in additional apps over time, Google says.
In the Google News app on supported devices, the Nano enables Magic Compose, which allows you to compose messages in styles akin to “excited”, “formal”, and “lyrical”.
Is Gemini higher than OpenAI’s GPT-4?
Google has had this occur a number of times advertised Gemini’s benchmarking superiority, claiming that Gemini Ultra outperforms current state-of-the-art results on “30 of 32 commonly used academic benchmarks used in the research and development of large language models.” Meanwhile, the company claims that Gemini 1.5 Pro is best able to perform tasks akin to summarizing content, brainstorming, and writing higher than Gemini Ultra in some situations; it will probably change with the premiere of the next Ultra model.
However, leaving aside the query of whether the benchmarks actually indicate a greater model, the results that Google indicates appear to be only barely higher than the corresponding OpenAI models. And – as mentioned earlier – some initial impressions weren’t great, each amongst users and others scientists mentioning that the older version of Gemini Pro tends to misinterpret basic facts, has translation issues, and provides poor coding suggestions.
How much does Gemini cost?
Gemini 1.5 Pro is free to use in Gemini apps and, for now, in AI Studio and Vertex AI.
However, when Gemini 1.5 Pro leaves the preview in Vertex, the model will cost $0.0025 per character, while the output will cost $0.00005 per character. Vertex customers pay per 1,000 characters (roughly 140 to 250 words) and, for models like the Gemini Pro Vision, per image ($0.0025).
Let’s assume a 500-word article incorporates 2,000 characters. To summarize this text with the Gemini 1.5 Pro will cost $5. Meanwhile, generating an article of comparable length will cost $0.1.
Pricing for the Ultra has not yet been announced.
Where can you try Gemini?
Gemini Pro
The easiest place to use Gemini Pro is in the Gemini apps. Pro and Ultra respond to queries in multiple languages.
Gemini Pro and Ultra are also available in preview on Vertex AI via API. The API is currently free to use “within limits” and supports certain regions including Europe, in addition to features akin to chat and filtering.
Elsewhere, Gemini Pro and Ultra might be present in AI Studio. Using this service, developers can iterate on Gemini-based prompts and chatbots after which obtain API keys to use them of their applications or export the code to a more complete IDE.
Code Assistant (formerly AI duo for programmers), Google’s suite of AI-based code completion and generation tools uses Gemini models. Developers could make “large-scale” changes to code bases, akin to updating file dependencies and reviewing large snippets of code.
Google has introduced Gemini models in its development tools for the Chrome and Firebase mobile development platform and database creation and management tools. It has also introduced new security products based on Gemini technology, e.g Gemini in Threat Intelligence, a component of Google’s Mandiant cybersecurity platform that may analyze large chunks of probably malicious code and enable users to search in natural language for persistent threats or indicators of compromise.
Technology
Microsoft Nadella sata chooses chatbots on the podcasts

While the general director of Microsoft, Satya Nadella, says that he likes podcasts, perhaps he didn’t take heed to them anymore.
That the treat is approaching at the end longer profile Bloomberg NadellaFocusing on the strategy of artificial intelligence Microsoft and its complicated relations with Opeli. To illustrate how much she uses Copilot’s AI assistant in her day by day life, Nadella said that as a substitute of listening to podcasts, she now sends transcription to Copilot, after which talks to Copilot with the content when driving to the office.
In addition, Nadella – who jokingly described her work as a “E -Mail driver” – said that it consists of a minimum of 10 custom agents developed in Copilot Studio to sum up E -Mailes and news, preparing for meetings and performing other tasks in the office.
It seems that AI is already transforming Microsoft in a more significant way, and programmers supposedly the most difficult hit in the company’s last dismissals, shortly after Nadella stated that the 30% of the company’s code was written by AI.
(Tagstotransate) microsoft
Technology
The planned Openai data center in Abu Dhabi would be greater than Monaco

Opeli is able to help in developing a surprising campus of the 5-gigawatt data center in Abu Dhabi, positioning the corporate because the fundamental tenant of anchor in what can grow to be considered one of the biggest AI infrastructure projects in the world, in accordance with the brand new Bloomberg report.
Apparently, the thing would include a tremendous 10 square miles and consumed power balancing five nuclear reactors, overshadowing the prevailing AI infrastructure announced by OpenAI or its competitors. (Opeli has not yet asked TechCrunch’s request for comment, but in order to be larger than Monaco in retrospect.)
The ZAA project, developed in cooperation with the G42-Konglomerate with headquarters in Abu Zabi- is an element of the ambitious Stargate OpenAI project, Joint Venture announced in January, where in January could see mass data centers around the globe supplied with the event of AI.
While the primary Stargate campus in the United States – already in Abilene in Texas – is to realize 1.2 gigawatts, this counterpart from the Middle East will be more than 4 times.
The project appears among the many wider AI between the USA and Zea, which were a few years old, and annoyed some legislators.
OpenAI reports from ZAA come from 2023 Partnership With G42, the pursuit of AI adoption in the Middle East. During the conversation earlier in Abu Dhabi, the final director of Opeli, Altman himself, praised Zea, saying: “He spoke about artificial intelligence Because it was cool before. “
As in the case of a big a part of the AI world, these relationships are … complicated. Established in 2018, G42 is chaired by Szejk Tahnoon Bin Zayed Al Nahyan, the national security advisor of ZAA and the younger brother of this country. His embrace by OpenAI raised concerns at the top of 2023 amongst American officials who were afraid that G42 could enable the Chinese government access advanced American technology.
These fears focused on “G42”Active relationships“With Blalisted entities, including Huawei and Beijing Genomics Institute, in addition to those related to people related to Chinese intelligence efforts.
After pressure from American legislators, CEO G42 told Bloomberg At the start of 2024, the corporate modified its strategy, saying: “All our Chinese investments that were previously collected. For this reason, of course, we no longer need any physical presence in China.”
Shortly afterwards, Microsoft – the fundamental shareholder of Opeli together with his own wider interests in the region – announced an investment of $ 1.5 billion in G42, and its president Brad Smith joined the board of G42.
(Tagstransate) Abu dhabi
Technology
Redpoint collects USD 650 million 3 years after the last large fund at an early stage

Redpoint Ventures, an organization based in San Francisco, which is a few quarter of a century, collected $ 650 million at an early stage, in keeping with A regulatory notification.
The latest RedPoint fund corresponds to the size of its previous fund, which was collected barely lower than three years ago. On the market where many enterprises reduce their capital allegations, this cohesion may indicate that limited partners are relatively satisfied with its results.
The company’s early stage strategy is managed by 4 managing partners: Alex Bard (pictured above), Satish Dharmraraj, Annie Kadavy and Eric Brescia, who joined the company in 2021 after he served as the operational director of Githuba for nearly three years.
The last outstanding investments of the RedPoint team at an early stage include AI Coding Pool Pool, which was founded by the former partner Redpoint and CTO GitHub Jason Warner, distributed laboratories of SQL database programmers and Platform Management Platform Platform Levelpath.
A multi -stage company also conducts a development strategy led by Logan Barlett, Jacob Effron, Elliot Geidt and Scott Raney partners. Last 12 months, Redpoint raised its fifth growth fund at USD 740 million, which is a small increase in the USD 725 million fund closed three years earlier.
The recent RedPoint outputs include the next insurance, which was sold for $ 2.6 billion in March, Tastemada Startup Media Travel -utar -Media was enriched by Wonder for $ 90 million, and the takeover of Hashicorp $ 6.4 billion by IBM.
Redpoint didn’t answer the request for comment.
(Tagstranslate) Early Stage Venture Capital (T) Basenside (T) Redpoint Venture Partners
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