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Apple Releases Open Source AI Models That Run On-Device

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Apple today released several open source large language models (LLMs) that are designed to run on-device rather than through cloud servers. Called OpenELM (Open-source Efficient Language Models), the LLMs are available on the Hugging Face Hub, a community for sharing AI code.

Apple Silicon AI Optimized Feature Siri
As outlined in a white paper [PDF], there are eight total OpenELM models, four of which were pre-trained using the CoreNet library, and four instruction tuned models. Apple uses a layer-wise scaling strategy that is aimed at improving accuracy and efficiency.

Apple provided code, training logs, and multiple versions rather than just the final trained model, and the researchers behind the project hope that it will lead to faster progress and “more trustworthy results” in the natural language AI field.

OpenELM, a state-of-the-art open language model. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. For example, with a parameter budget of approximately one billion parameters, OpenELM exhibits a 2.36% improvement in accuracy compared to OLMo while requiring 2x fewer pre-training tokens.

Diverging from prior practices that only provide model weights and inference code, and pre-train on private datasets, our release includes the complete framework for training and evaluation of the language model on publicly available datasets, including training logs, multiple checkpoints, and pre-training configurations.

Apple says that it is releasing the OpenELM models to “empower and enrich the open research community” with stage-of-the-art language models. Sharing open source models gives researchers a way to investigate risks and data and model biases. Developers and companies are able to use the models as-is or make modifications.

The open sharing of information has become an important tool for Apple to recruit top engineers, scientists, and experts because it provides opportunities for research papers that would not normally have been able to be published under Apple’s secretive policies.

Apple has not yet brought these kinds of AI capabilities to its devices, but iOS 18 is expected to include a number of new AI features, and rumors suggest that Apple is planning to run its large language models on-device for privacy purposes.

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AI traces mysterious metastatic cancers to their source

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Coloured scanning electron micrograph of a cultured breast cancer cell (orange) moving through two holes in a support film.

A breast cancer cell (artificially coloured) climbs through a supportive film in a laboratory experiment.Credit: Steve Gschmeissner/SPL

Some stealthy cancers remain undetected until they have spread from their source to distant organs. Now scientists have developed an artificial intelligence (AI) tool that outperforms pathologists at identifying the origins of metastatic cancer cells that circulate in the body1. The proof-of-concept model could help doctors to improve the diagnosis and treatment of late-stage cancer, and extend people’s lives.

“That’s a pretty significant finding — that it can be used as an assistive tool,” says Faisal Mahmood, who studies AI applications in health care at Harvard Medical School in Boston, Massachusetts.

Elusive origins

To treat metastatic cancers, doctors need to know where they came from. The origin of up to 5% of all tumours cannot be identified, and the prognosis for people whose primary cancer remains unknown is poor.

One method used to diagnose tricky metastatic cancers relies on tumour cells found in fluid extracted from the body. Clinicians examine images of the cells to work out which type of cancer cell they resemble. For example, breast cancer cells that migrate to the lungs still look like breast cancer cells.

Every year, of the 300,000 people with cancer who are newly treated at the hospital affiliated with Tianjin Medical University (TMU) in China, some 4,000 are diagnosed using such images, but around 300 people remain undiagnosed, says Tian Fei, a colorectal cancer surgeon at TMU.

Tian, Li Xiangchun, a bioinformatics researcher who studies deep learning at TMU, and their colleagues wanted to develop a deep-learning algorithm to analyse these images and predict the origin of the cancers. Their results were published in Nature Medicine on 16 April.

Tumour training

The researchers trained their AI model on some 30,000 images of cells found in abdominal or lung fluid from 21,000 people whose tumour of origin was known. They then tested their model on 27,000 images and found there was an 83% change that it would accurately predict the source of the tumour. And there was a 99% chance that the source of the tumour was included in the model’s top three predictions.

Having a top-three list is useful because it can help clinicians to reduce the number of extra — often intrusive — tests needed to identify a tumour’s origins, says Mahmood. The predictions were restricted to 12 common sources of cancer, including the lungs, ovaries, breasts and stomach. Some other forms of cancer, including those originating in the prostate and kidneys, could not be identified, because they don’t typically spread to fluid deposits in the abdomen and lungs, says Li.

When tested on some 500 images, the model was better than human pathologists at predicting a tumour’s origin. This improvement was statistically significant.

The researchers also retrospectively assessed a subset of 391 study participants some four years after they had had cancer treatment. They found that those who had received treatment for the type of cancer that the model predicted were more likely to have survived, and lived longer, than participants for whom the prediction did not match. “This is a pretty convincing argument” for using the AI model in a clinical setting, says Mahmood.

Mahmood has previously used AI to predict the origin of cancers from tissue samples2, and other teams have used genomic data. Combining the three data sources — cells, tissue and genomics — could further improve outcomes for people with metastatic cancers of unknown origins, he says.

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Computers

Inside the Creation of DBRX, the World’s Most Powerful Open Source AI Model

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This past Monday, about a dozen engineers and executives at data science and AI company Databricks gathered in conference rooms connected via Zoom to learn if they had succeeded in building a top artificial intelligence language model. The team had spent months, and about $10 million, training DBRX, a large language model similar in design to the one behind OpenAI’s ChatGPT. But they wouldn’t know how powerful their creation was until results came back from the final tests of its abilities.

“We’ve surpassed everything,” Jonathan Frankle, chief neural network architect at Databricks and leader of the team that built DBRX, eventually told the team, which responded with whoops, cheers, and applause emojis. Frankle usually steers clear of caffeine but was taking sips of iced latte after pulling an all-nighter to write up the results.

Databricks will release DBRX under an open source license, allowing others to build on top of its work. Frankle shared data showing that across about a dozen or so benchmarks measuring the AI model’s ability to answer general knowledge questions, perform reading comprehension, solve vexing logical puzzles, and generate high-quality code, DBRX was better than every other open source model available.

Four people standing at the corner of a grey and yellow wall in an office space

AI decision makers: Jonathan Frankle, Naveen Rao, Ali Ghodsi, and Hanlin Tang.Photograph: Gabriela Hasbun

It outshined Meta’s Llama 2 and Mistral’s Mixtral, two of the most popular open source AI models available today. “Yes!” shouted Ali Ghodsi, CEO of Databricks, when the scores appeared. “Wait, did we beat Elon’s thing?” Frankle replied that they had indeed surpassed the Grok AI model recently open-sourced by Musk’s xAI, adding, “I will consider it a success if we get a mean tweet from him.”

To the team’s surprise, on several scores DBRX was also shockingly close to GPT-4, OpenAI’s closed model that powers ChatGPT and is widely considered the pinnacle of machine intelligence. “We’ve set a new state of the art for open source LLMs,” Frankle said with a super-sized grin.

Building Blocks

By open-sourcing, DBRX Databricks is adding further momentum to a movement that is challenging the secretive approach of the most prominent companies in the current generative AI boom. OpenAI and Google keep the code for their GPT-4 and Gemini large language models closely held, but some rivals, notably Meta, have released their models for others to use, arguing that it will spur innovation by putting the technology in the hands of more researchers, entrepreneurs, startups, and established businesses.

Databricks says it also wants to open up about the work involved in creating its open source model, something that Meta has not done for some key details about the creation of its Llama 2 model. The company will release a blog post detailing the work involved to create the model, and also invited WIRED to spend time with Databricks engineers as they made key decisions during the final stages of the multimillion-dollar process of training DBRX. That provided a glimpse of how complex and challenging it is to build a leading AI model—but also how recent innovations in the field promise to bring down costs. That, combined with the availability of open source models like DBRX, suggests that AI development isn’t about to slow down any time soon.

Ali Farhadi, CEO of the Allen Institute for AI, says greater transparency around the building and training of AI models is badly needed. The field has become increasingly secretive in recent years as companies have sought an edge over competitors. Opacity is especially important when there is concern about the risks that advanced AI models could pose, he says. “I’m very happy to see any effort in openness,” Farhadi says. “I do believe a significant portion of the market will move towards open models. We need more of this.”

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Is the AI GPU the new mainframe? New open source tech allows users to ‘timeshare’ GPU resources for AI purposes for free — reminiscent of the days where scarce resources fosted computing elitism

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Without an efficient way to squeeze additional computing power from existing infrastructure, organizations are often forced to purchase additional hardware or delay projects. This can lead to longer wait times for results and potentially losing out to competitors. This problem is compounded by the rise of AI workloads which require a high GPU compute load.

ClearML has come up with what it thinks is the perfect solution to this problem –  fractional GPU capability for open source users, making it possible to “split” a single GPU so it can run multiple AI tasks simultaneously.

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Elon Musk’s Grok chatbot is going open source, but maybe not for the right reasons

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In its bid to become one of the best AI tools around right now, Elon Musk is set to release the source code to X Corp’s Grok AI chatbot to the public this week.

The decision, as TechCrunch reports, comes with Musk’s filing of a lawsuit in early March 2024 against ChatGPT developer OpenAI, claiming that it has strayed from its original purpose of developing artificial intelligence technology ‘for the benefit of humanity’ and now pursues profit.

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Russian Hackers Stole Microsoft Source Code—and the Attack Isn’t Over

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For years, Registered Agents Inc.—a secretive company whose business is setting up other businesses—has registered thousands of companies to people who appear to not exist. Multiple former employees tell WIRED that the company routinely incorporates businesses on behalf of its customers using what they claim are fake personas. An investigation found that incorporation paperwork for thousands of companies that listed these allegedly fake personas had links to Registered Agents.

State attorneys general from around the US sent a letter to Meta on Wednesday demanding the company take “immediate action” amid a record-breaking spike in complaints over hacked Facebook and Instagram accounts. Figures provided by the office of New York attorney general Letitia James, who spearheaded the effort, show that in 2023 her office received more than 780 complaints—10 times as many as in 2019. Many complaints cited in the letter say Meta did nothing to help them recover their stolen accounts. “We refuse to operate as the customer service representatives of your company,” the officials wrote in the letter. “Proper investment in response and mitigation is mandatory.”

Meanwhile, Meta suffered a major outage this week that took most of its platforms offline. When it came back, users were often forced to log back in to their accounts. Last year, however, the company changed how two-factor authentication works for Facebook and Instagram. Now, any devices you’ve frequently used with Meta services in recent years will be trusted by default. The move has made experts uneasy; this means that your devices may not need a two-factor authentication code to log in anymore. We updated our guide for how to turn off this setting.

A ransomware attack targeting medical firm Change Healthcare has caused chaos at pharmacies around the US, delaying delivery of prescription drugs nationwide. Last week, a Bitcoin address connected to AlphV, the group behind the attack, received $22 million in cryptocurrency—suggesting Change Healthcare has likely paid the ransom. A spokesperson for the firm declined to answer whether it was behind the payment.

And there’s more. Each week, we highlight the news we didn’t cover in depth ourselves. Click on the headlines below to read the full stories. And stay safe out there.

In January, Microsoft revealed that a notorious group of Russian state-sponsored hackers known as Nobelium infiltrated the email accounts of the company’s senior leadership team. Today, the company revealed that the attack is ongoing. In a blog post, the company explains that in recent weeks, it has seen evidence that hackers are leveraging information exfiltrated from its email systems to gain access to source code and other “internal systems.”

It is unclear exactly what internal systems were accessed by Nobelium, which Microsoft calls Midnight Blizzard, but according to the company, it is not over. The blog post states that the hackers are now using “secrets of different types” to breach further into its systems. “Some of these secrets were shared between customers and Microsoft in email, and as we discover them in our exfiltrated email, we have been and are reaching out to these customers to assist them in taking mitigating measures.”

Nobelium is responsible for the SolarWinds attack, a sophisticated 2020 supply-chain attack that compromised thousands of organizations including the major US government agencies like the Departments of Homeland Security, Defense, Justice, and Treasury.

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NixOS free open source Linux makes system configuration easy

NixOS Linux makes system configuration easy

If you’re in the market for a Linux distribution that offers advanced package and system management, NixOS is a platform that might catch your interest. It stands out with its unique approach to handling software packages and system configurations, aiming to provide users with both stability and flexibility. This Linux distribution is designed for those who need a reliable and efficient system, and it comes with a set of features that make it an attractive option for developers and system administrators.

At the heart of NixOS is its declarative package management system. This system is different from the traditional methods you might be familiar with, such as apt or Pacman. Instead, NixOS uses the Nix package manager, which is more similar to npm or Gem. With NixOS, you simply declare what you want your system to look like, and the operating system takes care of making it happen. This means you don’t have to manually handle the installation and maintenance of packages.

One of the most appealing aspects of NixOS is its ability to roll back system updates. If you find that an update causes issues with your workflow, you can quickly return to a previous state using the boot menu. This rollback feature acts as a safety net, protecting you from updates that might otherwise cause problems and giving you the confidence to update without fear.

How to use NixOS system configuration features

NixOS also streamlines system configuration by centralizing it. Instead of dealing with scattered configuration files as you might in other distributions, NixOS consolidates configurations into a single file or just a few files. This makes it much easier to control versions and replicate systems, which simplifies the setup and recovery processes. Watch the fantastic tutorial kindly created by Tris at No Boilerplate to learn more about the NixOS and how you can easily configure your system and move these settings to others. To create reproducible, declarative and reliable system configurations.

Here are some other articles you may find of interest on the subject of Linux :

 

The distribution caters to different types of users by offering both stable and unstable channels. This means that whether you’re someone who needs a dependable system or someone who likes to try out the latest features, NixOS has you covered. And because it’s so easy to roll back changes, you can experiment with new updates without worrying about compromising your system’s stability.

Creating systemd services is made simpler with NixOS, which normalizes system configuration tasks. This means you can manage services efficiently without having to write complex scripts or deal with complicated configurations.

For those who are particularly concerned with reproducibility, NixOS introduces Nix Flakes. This feature ensures that you can replicate your system, with all its dependencies and configurations, anywhere. Additionally, Home Manager is a tool that helps manage user-specific configurations, maintaining consistency across different installations.

NixOS is particularly adept at managing package dependencies. It isolates them, which helps to avoid version conflicts and broken packages. This isolation is beneficial for both development and production environments, as it contributes to the overall robustness of the system.

For those interested in learning more about NixOS, there are plenty of resources available. Vim Joy’s comprehensive guide and Tris’s Patreon content and podcasts provide valuable insights and practical advice on how to get the most out of NixOS. They emphasize the importance of understanding its declarative nature and recommend steering clear of commands that might conflict with the operating system’s design principles.

NixOS is a compelling option within the Linux ecosystem for those looking for innovative features that improve stability, reproducibility, and ease of management. It’s suitable for both seasoned Linux users and newcomers. With its ability to roll back changes and its declarative management style, NixOS could be the efficient and dependable platform that meets your needs.

Understanding NixOS and Its Package Management

NixOS is a Linux distribution that distinguishes itself with a unique approach to package and system management. It is designed to offer users a high degree of stability and flexibility, making it an appealing choice for developers and system administrators who require a reliable and efficient operating system. The distribution is equipped with a range of features that enhance its attractiveness, particularly its advanced package management capabilities.

At the core of NixOS is its declarative package management system. Unlike traditional package managers like apt or Pacman, NixOS employs the Nix package manager, which shares similarities with npm or Gem from other programming environments. In NixOS, users declare the desired state of their system in configuration files, and the Nix package manager automates the process of achieving that state. This approach eliminates the need for manual package installation and maintenance, streamlining the management of software on the system.

Rollback Capabilities and System Configuration

One of the standout features of NixOS is its ability to roll back system updates. This functionality provides a safety net for users, allowing them to revert to a previous system state if a new update introduces problems. The rollback capability is accessible through the boot menu, offering a straightforward way to restore the system to a known good configuration. This feature enhances user confidence in applying updates, knowing that they can easily undo changes if necessary.

System configuration in NixOS is centralized, which contrasts with the scattered configuration files found in many other Linux distributions. NixOS consolidates system settings into one or a few configuration files, simplifying version control and system replication. This centralization aids in setting up new systems and recovering from issues, as configurations can be easily copied and applied to other installations.

Channels, Services, and Reproducibility in NixOS

NixOS caters to a diverse user base by offering both stable and unstable channels. Users who prioritize a stable and reliable system can opt for the stable channel, while those interested in experimenting with cutting-edge features may choose the unstable channel. The ease of rolling back changes in NixOS encourages users to try new updates without the risk of destabilizing their system.

The creation and management of systemd services are streamlined in NixOS. The distribution normalizes system configuration tasks, allowing users to manage services effectively without the need for intricate scripts or complex configurations. For users focused on reproducibility, NixOS introduces features like Nix Flakes and Home Manager. Nix Flakes ensure that systems can be replicated with exact dependencies and configurations, regardless of the environment. Home Manager assists in managing user-specific configurations, ensuring consistency across different systems.

NixOS’s approach to managing package dependencies is particularly noteworthy. It isolates dependencies to prevent version conflicts and broken packages, which is advantageous in both development and production settings. This isolation contributes to the system’s robustness and reliability. NixOS is a compelling choice within the Linux ecosystem for those seeking innovative features that enhance stability, reproducibility, and ease of management. Its rollback capabilities, declarative management style, and advanced package handling make it a suitable platform for both experienced Linux users and those new to the operating system. To download the Linux operating system jump over to the official website.

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Google Gemma open source AI optimized to run on NVIDIA GPUs

Google Gemma open source AI optimized to run on NVIDIA GPUs

Google has made a significant move by joining forces with NVIDIA, a giant in the field of artificial intelligence hardware, to boost the capabilities of its Gemma language models. This collaboration is set to enhance the efficiency and speed for those who work with AI applications, making it a noteworthy development in the tech world.

The Google Gemma AI models have been upgraded and now come in two versions, one with 2 billion parameters and another with 7 billion parameters. These models are specifically designed to take full advantage of NVIDIA’s cutting-edge AI platforms. This upgrade is beneficial for a wide range of users, from those running large data centers to individuals using personal computers, as the Gemma models are now optimized to deliver top-notch performance.

At the heart of this enhancement lies NVIDIA’s TensorRT-LLM, an open-source library that is instrumental in optimizing large language model inference on NVIDIA GPUs. This tool is essential for ensuring that Gemma operates at peak performance, offering users faster and more precise AI interactions.

Google Gemma

One of the key improvements is Gemma’s compatibility with a wide array of NVIDIA hardware. Now, over 100 million NVIDIA RTX GPUs around the world can support Gemma, which greatly increases its reach. This includes the powerful GPUs found in data centers, the A3 instances in the cloud, and the NVIDIA RTX GPUs in personal computers.

In the realm of cloud computing, Google Cloud plans to employ NVIDIA’s H200 Tensor Core GPUs, which boast advanced memory capabilities. This integration is expected to enhance the performance of Gemma models, particularly in cloud-based applications, resulting in faster and more reliable AI services. NVIDIA’s contributions are not limited to hardware; the company also provides a comprehensive suite of tools for enterprise developers. These tools are designed to help with the fine-tuning and deployment of Gemma in various production environments, which simplifies the development process for AI services, whether they are complex or simple.

For those looking to further customize their AI projects, NVIDIA offers access to model checkpoints and a quantized version of Gemma, all optimized with TensorRT-LLM. This allows for even more detailed refinement and efficiency in AI projects. The NVIDIA AI Playground serves as a user-friendly platform for interacting directly with Gemma models. This platform is designed to be accessible, eliminating the need for complex setup processes, and is an excellent resource for those who want to quickly dive into exploring what Gemma has to offer.

An intriguing element of this integration is the combination of Gemma with NVIDIA’s Chat with RTX tech demo. This demo utilizes the generative AI capabilities of Gemma on RTX-powered PCs to provide a personalized chatbot experience. It is fast and maintains data privacy by operating locally, which means it doesn’t rely on cloud connectivity.

Overall, Google’s Gemma models have made a significant stride with the optimization for NVIDIA GPUs. This progress brings about improved performance, broad hardware support, and powerful tools for developers, making Gemma a strong contender for AI-driven applications. The partnership between Google and NVIDIA promises to deliver a robust and accessible AI experience for both developers and end-users, marking an important step in the evolution of AI technology. Here are some other articles you may find of interest on the subject of  Google Gemma :

 

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Google Gemma open source AI prompt performance is slow and inaccurate

Google Gemma open source AI prompt performance results

Google has unveiled Gemma, a new open-source artificial intelligence model, marking a significant step in the tech giant’s AI development efforts. This model, which is available in two variants offering either 2 billion and 7 billion parameters AI models, is designed to rival the advanced AI technologies of competitors such as Meta. For those with a keen interest in the progression of AI, it’s crucial to grasp both the strengths and weaknesses of Gemma.

Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Developed by Google DeepMind and other teams across Google, Gemma is inspired by Gemini, and the name reflects the Latin gemma, meaning “precious stone.”  Gemma is an evolution of Google’s Gemini models, which suggests it is built on a robust technological base. Gemma AI models provide a choice between 7B parameters, for efficient deployment and development on consumer-size GPU and TPU and 2B versions for CPU and on-device applications. Both come in base and instruction-tuned variants.

However, the sheer size of the model has raised questions about its practicality for individuals who wish to operate it on personal systems. Performance benchmarks have indicated that Gemma might lag behind other models like Llama 2 in terms of speed and accuracy, especially in real-world applications. One of the commendable aspects of Gemma is its availability on platforms such as Hugging Face and Google Colab. This strategic move by Google encourages a culture of experimentation and further development within the AI community. By making Gemma accessible, a wider range of users can engage with the model, potentially accelerating its improvement and adaptation.

Google Gemma results tested

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Despite the accessibility, Gemma has faced criticism from some quarters. Users have pointed out issues with the model’s performance, particularly regarding its speed and accuracy. Moreover, there are concerns about the extent of censorship in Google’s AI models, including Gemma. This could lead to a user experience that may not measure up to that offered by less restrictive competitors.

Gemma AI features :

  • Google Open Source AI:
    • Gemma is a new generation of open models introduced by Google, designed to assist developers and researchers in building AI responsibly.
    • It is a family of lightweight, state-of-the-art models developed by Google DeepMind and other Google teams, inspired by the Gemini models.
    • The name “Gemma” reflects the Latin “gemma,” meaning “precious stone.”
  • Key Features of Gemma Models:
    • Model Variants: Two sizes are available, Gemma 2B and Gemma 7B, each with pre-trained and instruction-tuned variants.
    • Responsible AI Toolkit: A toolkit providing guidance and tools for creating safer AI applications with Gemma.
    • Framework Compatibility: Supports inference and supervised fine-tuning across major frameworks like JAX, PyTorch, and TensorFlow through native Keras 3.0.
    • Accessibility: Ready-to-use Colab and Kaggle notebooks, integration with tools like Hugging Face, MaxText, NVIDIA NeMo, and TensorRT-LLM.
    • Deployment: Can run on laptops, workstations, or Google Cloud, with easy deployment on Vertex AI and Google Kubernetes Engine (GKE).
    • Optimization: Optimized for multiple AI hardware platforms, including NVIDIA GPUs and Google Cloud TPUs.
    • Commercial Use: Terms of use allow for responsible commercial usage and distribution by all organizations.
  • Performance and Safety:
    • State-of-the-Art Performance: Gemma models achieve top performance for their sizes and are capable of running on developer laptops or desktops.
    • Safety and Reliability: Gemma models are designed with Google’s AI Principles in mind, using automated techniques to filter out sensitive data and aligning models with responsible behaviors through fine-tuning and RLHF.
    • Evaluations: Include manual red-teaming, automated adversarial testing, and capability assessments for dangerous activities.
  • Responsible Generative AI Toolkit:
    • Safety Classification: Methodology for building robust safety classifiers with minimal examples.
    • Debugging Tool: Helps investigate Gemma’s behavior and address potential issues.
    • Guidance: Best practices for model builders based on Google’s experience in developing and deploying large language models.
  • Optimizations and Compatibility:
    • Multi-Framework Tools: Reference implementations for various frameworks, supporting a wide range of AI applications.
    • Cross-Device Compatibility: Runs across devices including laptops, desktops, IoT, mobile, and cloud.
    • Hardware Platforms: Optimized for NVIDIA GPUs and integrated with Google Cloud for leading performance and technology.

However, there is room for optimism regarding Gemma’s future. The development of quantized versions of the model could help address the concerns related to its size and speed. As Google continues to refine Gemma, it is anticipated that future iterations will overcome the current shortcomings.

Google’s Gemma AI model has made a splash in the competitive AI landscape, arriving with a mix of promise and challenges. The model’s considerable size, performance issues, and censorship concerns are areas that Google will need to tackle with determination. As the company works on these fronts, the AI community will be watching closely to see how Gemma evolves and whether it can realize its potential as a significant player in the open-source AI arena.

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Manage your finances using AI and ProjectX open source app

Manage your finances using AI and ProjectX open source app

Imagine having control over your finances with a tool that not only simplifies the process but also enhances it with the power of artificial intelligence (AI). This is what ProjectX offers—a sophisticated, open-source financial management platform that uses AI to help you make smarter, more secure financial decisions.

ProjectX is at the cutting edge of financial management technology, featuring a dynamic dashboard that brings together all your financial information in one place. Whether it’s your credit cards, bank accounts, investments, or even cryptocurrencies, you get a real-time snapshot of your financial status. This integrated view provides immediate insights and a complete picture of your financial well-being.

ProjectX AI financial management system

“Welcome to ProjectX, where we’re ushering in a new era of financial management. Leveraging cutting-edge AI, ProjectX redefines how you track, analyze, and optimize your finances, ensuring smarter, more secure financial decisions. With ProjectX, gain unparalleled insights into your spending habits and financial patterns, empowering you to budget better and experience more. Trusted by the world’s most innovative companies, ProjectX is here to revolutionize your financial management experience. Empower your financial management with AI-driven insights, making tracking and optimizing your finances effortless.”

One of the standout features of ProjectX is its ability to categorize your expenses automatically. This not only makes it easier to manage your budget but also helps you spot areas where you can cut back on spending. By identifying unnecessary expenses, the platform empowers you with the knowledge to make better financial decisions and optimize how you allocate your funds.

Manage your finances more effectively with AI

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But ProjectX’s intelligence doesn’t stop there. It delves into your spending habits and financial behaviors to offer tailored advice. These insights can lead to further savings and assist you in making wiser financial choices. The platform also includes tools for tracking your budget and managing bill payments, which simplifies your financial tasks and puts you in control of your economic destiny.

Security is a fundamental aspect of ProjectX. The platform uses advanced cloud backup solutions to protect your financial data from loss and unauthorized access. Its user-friendly interface allows you to customize settings according to your preferences while maintaining a high level of security.

The platform is designed to work seamlessly with your existing financial tools and services, making the transition smooth and ensuring you can continue managing your finances without interruption. For those who appreciate a sense of community, ProjectX offers a space for engagement. Supporters on Patreon can gain access to exclusive AI tool subscriptions and connect with a network of like-minded individuals.

Although ProjectX is still evolving, with some features like bank account integration being perfected, users are encouraged to be part of its growth. You can test the platform locally and contribute to its development through its GitHub repository. The developers keep users informed with regular updates and insights shared on Twitter.

Things to consider when allowing AI to manage your finances

ProjectX has been designed to be more than just a financial management tool—it’s an advanced platform that harnesses the capabilities of AI to enhance your financial life. However when considering the use of AI to manage your finances, it’s important to approach the decision with a comprehensive understanding of both the capabilities and limitations of AI systems in financial management. This involves evaluating various factors that could impact the effectiveness, security, and appropriateness of AI for your financial goals and circumstances. Here are key considerations:

  • Security and Privacy: AI systems require access to sensitive financial data to operate effectively. It’s crucial to assess the security measures in place to protect your data from unauthorized access or breaches. Understand the privacy policies of the AI service provider and ensure compliance with data protection regulations.
  • Accuracy and Reliability: Evaluate the track record and reliability of the AI system. Consider the technology’s ability to accurately analyze financial markets, predict trends, and execute transactions based on your financial goals and risk tolerance. Understand the algorithms’ basis for decision-making and the potential for errors.
  • Regulatory Compliance: Financial markets are heavily regulated. Ensure the AI system complies with all relevant financial regulations and standards in your jurisdiction, including those related to investment advice, reporting, and fiduciary responsibilities.
  • Transparency and Control: Investigate how much visibility and control you will have over the AI’s decisions and actions. It’s important to understand how decisions are made and to have the ability to intervene or override the AI’s actions if necessary.
  • Costs and Fees: Analyze the cost structure associated with using the AI for financial management. This includes any subscription fees, transaction fees, and potential hidden costs. Compare these costs against the expected benefits and savings the AI might provide.
  • Customization and Flexibility: Consider whether the AI system can be tailored to your specific financial goals, risk tolerance, and investment preferences. The ability to customize settings and preferences is crucial for ensuring that the AI’s actions align with your financial strategy.
  • Performance Track Record: Review the performance history of the AI system in managing finances, if available. This includes looking at historical returns, risk management outcomes, and the system’s ability to adapt to changing market conditions.
  • Human Oversight and Support: Determine the level of human oversight involved in the AI’s financial management process. Access to human financial advisors or support staff can be valuable for addressing complex issues or concerns that the AI might not be equipped to handle.
  • Impact on Employment and Ethical Considerations: Reflect on the broader implications of using AI in financial management, including its impact on employment within the financial sector and ethical considerations around algorithmic decision-making and potential biases.
  • Exit Strategy: Finally, consider your options for discontinuing the use of the AI system, including the process for transferring management of your finances back to human control or another service provider. Understand any potential costs or complications associated with this transition.

By thoroughly evaluating these considerations, you can make an informed decision about the suitability of AI for managing your finances, aligning the technology’s capabilities with your financial objectives, risk tolerance, and ethical standards.

Filed Under: Technology News, Top News





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