ai model Archives - AI News https://www.artificialintelligence-news.com/tag/ai-model/ Artificial Intelligence News Tue, 19 Mar 2024 14:36:05 +0000 en-GB hourly 1 https://www.artificialintelligence-news.com/wp-content/uploads/sites/9/2020/09/ai-icon-60x60.png ai model Archives - AI News https://www.artificialintelligence-news.com/tag/ai-model/ 32 32 Google launches Gemini 1.5 with ‘experimental’ 1M token context https://www.artificialintelligence-news.com/2024/02/16/google-launches-gemini-1-5-experimental-1m-token-context/ https://www.artificialintelligence-news.com/2024/02/16/google-launches-gemini-1-5-experimental-1m-token-context/#respond Fri, 16 Feb 2024 13:42:49 +0000 https://www.artificialintelligence-news.com/?p=14415 Google has unveiled its latest AI model, Gemini 1.5, which features what the company calls an “experimental” one million token context window.  The new capability allows Gemini 1.5 to process extremely long text passages – up to one million characters – to understand context and meaning. This dwarfs previous AI systems like Claude 2.1 and... Read more »

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Google has unveiled its latest AI model, Gemini 1.5, which features what the company calls an “experimental” one million token context window. 

The new capability allows Gemini 1.5 to process extremely long text passages – up to one million characters – to understand context and meaning. This dwarfs previous AI systems like Claude 2.1 and GPT-4 Turbo, which max out at 200,000 and 128,000 tokens respectively:

“Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks across modalities, improves the state-of-the-art in long-document QA, long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra’s state-of-the-art performance across a broad set of benchmarks,” said Google researchers in a technical paper (PDF).

The efficiency of Google’s latest model is attributed to its innovative Mixture-of-Experts (MoE) architecture.

“While a traditional Transformer functions as one large neural network, MoE models are divided into smaller ‘expert’ neural networks,” explained Demis Hassabis, CEO of Google DeepMind.

“Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialisation massively enhances the model’s efficiency.”

To demonstrate the power of the 1M token context window, Google showed how Gemini 1.5 could ingest the entire 326,914-token Apollo 11 flight transcript and then accurately answer specific questions about it. It also summarised key details from a 684,000-token silent film when prompted.

Google is initially providing developers and enterprises free access to a limited Gemini 1.5 preview with a one million token context window. A 128,000 token general release for the public will come later, along with pricing details.

For now, the one million token capability remains experimental. But if it lives up to its early promise, Gemini 1.5 could set a new standard for AI’s ability to understand complex, real-world text.

Developers interested in testing Gemini 1.5 Pro can sign up in AI Studio. Google says that enterprise customers can reach out to their Vertex AI account team.

(Image Credit: Google)

See also: Amazon trains 980M parameter LLM with ’emergent abilities’

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Google launches Gemini to replace Bard chatbot https://www.artificialintelligence-news.com/2024/02/09/google-launches-gemini-replace-bard-chatbot/ https://www.artificialintelligence-news.com/2024/02/09/google-launches-gemini-replace-bard-chatbot/#respond Fri, 09 Feb 2024 09:54:16 +0000 https://www.artificialintelligence-news.com/?p=14347 Google has launched its AI chatbot called Gemini, which replaces its short-lived Bard service. Unveiled in December, Bard was touted as a competitor to chatbots like ChatGPT but failed to impress in demos. Google staff even called the launch “botched” and slammed CEO Sundar Pichai. Now rebranded as Gemini, Google says it represents the company’s... Read more »

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Google has launched its AI chatbot called Gemini, which replaces its short-lived Bard service.

Unveiled in December, Bard was touted as a competitor to chatbots like ChatGPT but failed to impress in demos. Google staff even called the launch “botched” and slammed CEO Sundar Pichai.

Now rebranded as Gemini, Google says it represents the company’s “most capable family of models” for natural conversations. Two experiences are being launched: Gemini Advanced and a mobile app.

Gemini Advanced grants access to Ultra 1.0, billed by Google as its “largest and most capable state-of-the-art AI model.” In blind evaluations, third-party raters preferred Gemini Advanced with Ultra 1.0 over alternatives in complex tasks like coding, logical reasoning, and creative collaboration.  

The AI can serve as a tutor by creating personalised lessons and quizzes. Developers are aided for trickier coding problems. Gemini Advanced is designed to spark ideas and strategise ways that creators can grow their audiences.

Google plans to expand Gemini Advanced’s capabilities over time with exclusive features like expanded multimodal interactions, interactive coding, deeper data analysis, and more. The service already supports over 150 countries in English and will add more languages soon.  

Access to Gemini Advanced is granted through a new $19.99 (£18.99) per month Google One AI Premium Plan, including a free two-month trial. Subscribers get the latest Google AI advancements plus 2TB of storage from the existing Premium plan.  

Google claims Gemini Advanced underwent extensive trust and safety checks before its launch, including external reviews, to mitigate issues around unsafe content and bias. More details are available in an updated technical report (PDF).

Lastly, Google launched new mobile apps on Android and iOS to access basic Gemini features on-the-go. Users can ask for help with images, tasks, and more while out-and-about. Over time, the plan is for Gemini to become a true personal AI assistant.

The Gemini mobile apps are now available in the US as a dedicated app on Android and in the Google app on iOS, supporting English conversations initially. Next week, the apps expand to Japan and Korea, followed by more countries and languages thereafter.

(Image Credit: Google)

See also: DeepMind framework offers breakthrough in LLMs’ reasoning

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IBM and Hugging Face release AI foundation model for climate science https://www.artificialintelligence-news.com/2023/08/03/ibm-hugging-face-ai-foundation-model-climate-science/ https://www.artificialintelligence-news.com/2023/08/03/ibm-hugging-face-ai-foundation-model-climate-science/#respond Thu, 03 Aug 2023 10:32:39 +0000 https://www.artificialintelligence-news.com/?p=13423 In a bid to democratise access to AI technology for climate science, IBM and Hugging Face have announced the release of the watsonx.ai geospatial foundation model. The geospatial model, built from NASA’s satellite data, will be the largest of its kind on Hugging Face and marks the first-ever open-source AI foundation model developed in collaboration... Read more »

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In a bid to democratise access to AI technology for climate science, IBM and Hugging Face have announced the release of the watsonx.ai geospatial foundation model.

The geospatial model, built from NASA’s satellite data, will be the largest of its kind on Hugging Face and marks the first-ever open-source AI foundation model developed in collaboration with NASA.

Jeff Boudier, head of product and growth at Hugging Face, highlighted the importance of information sharing and collaboration in driving progress in AI. Open-source AI and the release of models and datasets are fundamental in ensuring AI benefits as many people as possible.

Climate science faces constant challenges due to rapidly changing environmental conditions, requiring access to the latest data. Despite the abundance of data, scientists and researchers struggle to analyse the vast datasets effectively. NASA estimates that by 2024, there will be 250,000 terabytes of data from new missions.

To address this issue, IBM embarked on a Space Act Agreement with NASA earlier this year—aiming to build an AI foundation model for geospatial data.

By making this geospatial foundation model openly available on Hugging Face, both companies aim to promote collaboration and accelerate progress in climate and Earth science.

Sriram Raghavan, VP at IBM Research AI, commented:

“The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer.

By combining IBM’s foundation model efforts aimed at creating flexible, reusable AI systems with NASA’s repository of Earth-satellite data, and making it available on the leading open-source AI platform, Hugging Face, we can leverage the power of collaboration to implement faster and more impactful solutions that will improve our planet.”

The geospatial model, jointly trained by IBM and NASA on Harmonized Landsat Sentinel-2 satellite data (HLS) over one year across the continental United States, has shown promising results. It demonstrated a 15 percent improvement over state-of-the-art techniques using only half the labelled data.

With further fine-tuning, the model can be adapted for various tasks such as deforestation tracking, crop yield prediction, and greenhouse gas detection.

IBM’s collaboration with NASA in building the AI model aligns with NASA’s decade-long Open-Source Science Initiative, promoting a more accessible and inclusive scientific community. NASA, along with other federal agencies, has designated 2023 as the Year of Open Science, celebrating the benefits of sharing data, information, and knowledge openly.

Kevin Murphy, Chief Science Data Officer at NASA, said:

“We believe that foundation models have the potential to change the way observational data is analysed and help us to better understand our planet.

By open-sourcing such models and making them available to the world, we hope to multiply their impact.”

The geospatial model leverages IBM’s foundation model technology and is part of IBM’s broader initiative to create and train AI models with transferable capabilities across different tasks.

In June, IBM introduced watsonx, an AI and data platform designed to scale and accelerate the impact of advanced AI with trusted data. A commercial version of the geospatial model, integrated into IBM watsonx, will be available through the IBM Environmental Intelligence Suite (EIS) later this year.

By leveraging the power of open-source technologies, this latest collaboration aims to address climate challenges effectively and contribute to a more sustainable future for our planet.

(Photo by Markus Spiske on Unsplash)

See also: Jay Migliaccio, IBM Watson: On leveraging AI to improve productivity

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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OpenAI is not currently training GPT-5 https://www.artificialintelligence-news.com/2023/04/17/openai-is-not-currently-training-gpt-5/ https://www.artificialintelligence-news.com/2023/04/17/openai-is-not-currently-training-gpt-5/#respond Mon, 17 Apr 2023 10:36:35 +0000 https://www.artificialintelligence-news.com/?p=12963 Experts calling for a pause on AI development will be glad to hear that OpenAI isn’t currently training GPT-5. OpenAI CEO Sam Altman spoke remotely at an MIT event and was quizzed about AI by computer scientist and podcaster Lex Fridman. Altman confirmed that OpenAI is not currently developing a fifth version of its Generative... Read more »

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Experts calling for a pause on AI development will be glad to hear that OpenAI isn’t currently training GPT-5.

OpenAI CEO Sam Altman spoke remotely at an MIT event and was quizzed about AI by computer scientist and podcaster Lex Fridman.

Altman confirmed that OpenAI is not currently developing a fifth version of its Generative Pre-trained Transformer model and is instead focusing on enhancing the capabilities of GPT-4, the latest version.

Altman was asked about the open letter that urged developers to pause training AI models larger than GPT-4 for six months. While he supported the idea of ensuring AI models are safe and aligned with human values, he believed that the letter lacked technical nuance regarding where to pause.

“An earlier version of the letter claims we are training GPT-5 right now. We are not, and won’t for some time. So in that sense, it was sort of silly,” said Altman.

“We are doing things on top of GPT-4 that I think have all sorts of safety issues that we need to address.”

GPT-4 is a significant improvement over its predecessor, GPT-3, which was released in 2020. 

GPT-3 has 175 billion parameters, making it one of the largest language models in existence. OpenAI has not confirmed GPT-4’s exact number of parameters but it’s estimated to be in the region of one trillion.

OpenAI said in a blog post that GPT-4 is “more creative and collaborative than ever before” and “can solve difficult problems with greater accuracy, thanks to its broader general knowledge and problem-solving abilities.”

In a simulated law bar exam, GPT-3.5 scored around the bottom 10 percent. GPT-4, however, passed the exam among the top 10 percent.

OpenAI is one of the leading AI research labs in the world, and its GPT models have been used for a wide range of applications, including language translation, chatbots, and content creation. However, the development of such large language models has raised concerns about their safety and ethical implications.

Altman’s comments suggest that OpenAI is aware of the concerns surrounding its GPT models and is taking steps to address them.

While GPT-5 may not be on the horizon, the continued development of GPT-4 and the creation of other models on top of it will undoubtedly raise further questions about the safety and ethical implications of such AI models.

(Photo by Victor Freitas on Unsplash)

Related: ​​Italy will lift ChatGPT ban if OpenAI fixes privacy issues

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Meta’s NLLB-200 AI model improves translation quality by 44% https://www.artificialintelligence-news.com/2022/07/07/metas-nllb-200-ai-model-improves-translation-quality-by-44/ https://www.artificialintelligence-news.com/2022/07/07/metas-nllb-200-ai-model-improves-translation-quality-by-44/#respond Thu, 07 Jul 2022 17:02:38 +0000 https://www.artificialintelligence-news.com/?p=12146 Meta has unveiled a new AI model called NLLB-200 that can translate 200 languages and improves quality by an average of 44 percent.  Translation apps have been fairly adept at the most popular languages for some time. Even when they don’t offer a perfect translation, it’s normally close enough for the native speaker to understand.... Read more »

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Meta has unveiled a new AI model called NLLB-200 that can translate 200 languages and improves quality by an average of 44 percent. 

Translation apps have been fairly adept at the most popular languages for some time. Even when they don’t offer a perfect translation, it’s normally close enough for the native speaker to understand.

However, there are hundreds of millions of people in regions with many languages – like Africa and Asia – that still suffer from poor translation services.

In a press release, Meta wrote:

“To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world’s languages.

Today, we’re announcing an important breakthrough in NLLB: We’ve built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish.”

The metaverse aims to be borderless. To enable that, translation services will have to quickly offer accurate translations.

“As the metaverse begins to take shape, the ability to build technologies that work well in a wider range of languages will help to democratise access to immersive experiences in virtual worlds,” the company explained.

According to Meta, NLLB-200 scored 44 percent higher in the “quality” of translations compared to previous AI research. For some African and Indian-based languages, NLLB-200’s translations were more than 70 percent more accurate.

Meta created a dataset called FLORES-200 to evaluate and improve NLLB-200. The dataset enables researchers to assess FLORES-200’s performance “in 40,000 different language directions.”

Both NLLB-200 and FLORES-200 are being opened to developers to help build on Meta’s work and improve their own translation tools.

Meta has a pool of up to $200,000 in grants for researchers and nonprofit organisations that wish to use NLLB-200 for impactful uses focused on sustainability, food security, gender-based violence, education, or other areas that support UN Sustainable Development Goals. 

However, not everyone is fully convinced by Meta’s latest breakthrough.

“It’s worth bearing in mind, despite the hype, that these models are not the cure-all that they may first appear. The models that Meta uses are massive, unwieldy beasts. So, when you get into the minutiae of individualised use-cases, they can easily find themselves out of their depth – overgeneralised and incapable of performing the specific tasks required of them,” commented Victor Botev, CTO at Iris.ai.

“Another point to note is that the validity of these measurements has yet to be scientifically proven and verified by their peers. The datasets for different languages are too small, as shown by the challenge in creating them in the first place, and the metric they’re using, BLEU, is not particularly applicable.”

A demo of NLLB-200 is available here.

(Photo by Jason Leung on Unsplash)

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State of ModelOps: 90% expect a dedicated budget within 12 months, 80% say risk-management is a key AI barrier https://www.artificialintelligence-news.com/2021/04/16/state-of-modelops-90-expect-budget-12-months-risk-management-key-ai-barrier/ https://www.artificialintelligence-news.com/2021/04/16/state-of-modelops-90-expect-budget-12-months-risk-management-key-ai-barrier/#respond Fri, 16 Apr 2021 08:45:58 +0000 http://artificialintelligence-news.com/?p=10471 The first annual State of ModelOps report highlights some interesting trends about the real-world adoption of AI in enterprises. Independent research firm Corinium Intelligence conducted the study on behalf of ModelOp and aims to summarise the state of model operationalisation today. Stu Bailey, Co-Founder and Chief Enterprise AI Architect at ModelOp, said: “As the report... Read more »

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The first annual State of ModelOps report highlights some interesting trends about the real-world adoption of AI in enterprises.

Independent research firm Corinium Intelligence conducted the study on behalf of ModelOp and aims to summarise the state of model operationalisation today.

Stu Bailey, Co-Founder and Chief Enterprise AI Architect at ModelOp, said:

“As the report shows, enterprises increasingly view ModelOps as the key to ensuring operational excellence and maximising value from their AI initiatives, in the same way that DevOps, ITOps, and SecOps have for the development, IT, and cybersecurity sectors.”

According to the survey of 100 AI-focused executives from F100 and global financial services companies—each enterprise has an average of 270 models in production.

Despite the rapid uptake, 80 percent report that difficulty in managing risk and ensuring compliance is a key barrier to adoption. With increasingly strict AI regulations – such as those being drafted by the EU – this figure could increase without robust solutions.

Improving the enforcement of AI governance processes is noted by 69 percent of respondents as a key reason for investing in a ModelOps platform

Bailey explains:

Experience has shown that creating AI models is only half the battle. Operationalising models – getting them into production, keeping them functioning properly and within guidelines for compliance and risk, and demonstrating their business value – is the next frontier as organisations mature and scale their AI initiatives.”

Data scientists at the surveyed organisations are using an average of 5-7 different tools for developing models—highlighting the potential for streamlining operations. Just 25 percent rate their existing processes as “very effective” for inventorying production models.

76 percent of respondents say the cost reductions associated with a ModelOps platform is a “very important” benefit for such an investment. 42 percent describe it as crucial.

Skip McCormick, Data Science Fellow at BNY Mellon, commented: “ModelOps is the next logical step after DevOps. We’re looking for a systematic way to make sure that the models we’re putting into play actually do what they should do.”

Overall, 90 percent of respondents expect to have a dedicated ModelOps budget within 12 months.

(Photo by Kevin Ku on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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NVIDIA breakthrough emulates images from small datasets for groundbreaking AI training https://www.artificialintelligence-news.com/2020/12/07/nvidia-emulates-images-small-datasets-ai-training/ https://www.artificialintelligence-news.com/2020/12/07/nvidia-emulates-images-small-datasets-ai-training/#respond Mon, 07 Dec 2020 16:08:23 +0000 http://artificialintelligence-news.com/?p=10069 NVIDIA’s latest breakthrough emulates new images from existing small datasets with truly groundbreaking potential for AI training. The company demonstrated its latest AI model using a small dataset – just a fraction of the size typically used for a Generative Adversarial Network (GAN) – of artwork from the Metropolitan Museum of Art. From the dataset,... Read more »

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NVIDIA’s latest breakthrough emulates new images from existing small datasets with truly groundbreaking potential for AI training.

The company demonstrated its latest AI model using a small dataset – just a fraction of the size typically used for a Generative Adversarial Network (GAN) – of artwork from the Metropolitan Museum of Art.

From the dataset, NVIDIA’s AI was able to create new images which replicate the style of the original artist’s work. These images can then be used to help train further AI models.

The AI achieved this impressive feat by applying a breakthrough neural network training technique similar to the popular NVIDIA StyleGAN2 model. 

The technique is called Adaptive Discriminator Augmentation (ADA) and NVIDIA claims that it reduces the number of training images required by 10-20x while still getting great results.

David Luebke, VP of Graphics Research at NVIDIA, said:

“These results mean people can use GANs to tackle problems where vast quantities of data are too time-consuming or difficult to obtain.

I can’t wait to see what artists, medical experts and researchers use it for.”

Healthcare is a particularly exciting field where NVIDIA’s research could be applied. For example, it could help to create cancer histology images to train other AI models.

The breakthrough will help with the issues around most current datasets.

Large datasets are often required for AI training but aren’t always available. On the other hand, large datasets are difficult to ensure their content is suitable and does not unintentionally lead to algorithmic bias.

Earlier this year, MIT was forced to remove a large dataset called 80 Million Tiny Images. The dataset is popular for training AIs but was found to contain images labelled with racist, misogynistic, and other unacceptable terms.

A statement on MIT’s website claims it was unaware of the offensive labels and they were “a consequence of the automated data collection procedure that relied on nouns from WordNet.”

The statement goes on to explain the 80 million images contained in the dataset – with sizes of just 32×32 pixels – meant that manual inspection would be almost impossible and couldn’t guarantee all offensive images would be removed.

By starting with a small dataset that can be feasibly checked manually, a technique like NVIDIA’s ADA could be used to create new images which emulate the originals and can scale up to the required size for training AI models.

In a blog post, NVIDIA wrote:

“It typically takes 50,000 to 100,000 training images to train a high-quality GAN. But in many cases, researchers simply don’t have tens or hundreds of thousands of sample images at their disposal.

With just a couple thousand images for training, many GANs would falter at producing realistic results. This problem, called overfitting, occurs when the discriminator simply memorizes the training images and fails to provide useful feedback to the generator.”

You can find NVIDIA’s full research paper here (PDF). The paper is being presented at this year’s NeurIPS conference as one of a record 28 NVIDIA Research papers accepted to the prestigious conference.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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MIT has removed a dataset which leads to misogynistic, racist AI models https://www.artificialintelligence-news.com/2020/07/02/mit-removed-dataset-misogynistic-racist-ai-models/ https://www.artificialintelligence-news.com/2020/07/02/mit-removed-dataset-misogynistic-racist-ai-models/#comments Thu, 02 Jul 2020 15:43:05 +0000 http://artificialintelligence-news.com/?p=9728 MIT has apologised for, and taken offline, a dataset which trains AI models with misogynistic and racist tendencies. The dataset in question is called 80 Million Tiny Images and was created in 2008. Designed for training AIs to detect objects, the dataset is a huge collection of pictures which are individually labelled based on what... Read more »

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MIT has apologised for, and taken offline, a dataset which trains AI models with misogynistic and racist tendencies.

The dataset in question is called 80 Million Tiny Images and was created in 2008. Designed for training AIs to detect objects, the dataset is a huge collection of pictures which are individually labelled based on what they feature.

Machine-learning models are trained using these images and their labels. An image of a street – when fed into an AI trained on such a dataset – could tell you about things it contains such as cars, streetlights, pedestrians, and bikes.

Two researchers – Vinay Prabhu, chief scientist at UnifyID, and Abeba Birhane, a PhD candidate at University College Dublin in Ireland – analysed the images and found thousands of concerning labels.

MIT’s training set was found to label women as “bitches” or “whores,” and people from BAME communities with the kind of derogatory terms I’m sure you don’t need me to write. The Register notes the dataset also contained close-up images of female genitalia labeled with the C-word.

The Register alerted MIT to the concerning issues found by Prabhu and Birhane with the dataset and the college promptly took it offline. MIT went a step further and urged anyone using the dataset to stop using it and delete any copies.

A statement on MIT’s website claims it was unaware of the offensive labels and they were “a consequence of the automated data collection procedure that relied on nouns from WordNet.”

The statement goes on to explain the 80 million images contained in the dataset, with sizes of just 32×32 pixels, means that manual inspection would be almost impossible and cannot guarantee all offensive images will be removed.

“Biases, offensive and prejudicial images, and derogatory terminology alienates an important part of our community – precisely those that we are making efforts to include. It also contributes to harmful biases in AI systems trained on such data,” wrote Antonio Torralba, Rob Fergus, and Bill Freeman from MIT.

“Additionally, the presence of such prejudicial images hurts efforts to foster a culture of inclusivity in the computer vision community. This is extremely unfortunate and runs counter to the values that we strive to uphold.”

You can find a full pre-print copy of Prabhu and Birhane’s paper here (PDF)

(Photo by Clay Banks on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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