cybersecurity
Our personal, digital, national and military security depend on a safe cyber space.
Top 4 Anime Series of all Time! That you must watch. Top 4 shounen anime.
Top 4 Shounen anime in my opinion that you must watch. Hey audience We here cover anime of all types (Shounen, Shoujo, Mecha, Isekai, Slice of life, Action, Romance, Comedy, Fantasy, Adventure, etc.) Follow our page to stay updated on anime and more.
By Unkown Me?4 years ago in 01
Can Nanotechnology Build The AI Of The Future?
Nanotechnology is one of the biggest fields of emerging technologies, seeing application in various fields such as agriculture, medicine and engineering. However, many, including prominent futurist Ray Kurzweil, have said that nanotechnology will be the tool that AI will use to achieve the ‘singularity’. While such a vision is still in the future, technology today have begun to exhibit similar synchronisation in their abilities to work with each other, especially when it comes to AI and nanotech. These two fields can be used in conjunction with each other, from AI helping nanotech research to progress faster, to creating foods that taste like meat when they have none, to nanotech powered compute to ensure the spread of AI. This article covers how AI and nanotechnology can come together and build the future of humanity’s existence. AI Helps Scale Nanotech Due to the scale of research undertaken when dealing with nanoparticles, it is difficult to quantify some of the variables in the setting, as they cannot be recorded. Moreover, interpreting these results is also a behemoth task, owing to the same reasons. However, AI is a perfect fit for these tasks, as it can not only optimise the data and algorithm design for the technology, but also estimate multiple parameters and interpret results from experiments. This has also led to multiple nanotechnology facilities to begin utilising AI in their research segments, owing to the high degree of accuracy and effectiveness that AI brings to the table in nanotechnology. Moreover, in applications that require a high degree of precision, AI can also enable the intricate preprogramming of nanobots’. This includes medical applications, as the minimal invasiveness of these bots is sought after in the medical field. Nanotechnology can also enable the transformation of stem cells to bone cells on command, thus providing a solution for multiple diseases. The manipulation of stem cells by AI can also result in the creation of human organs for the replacement and repair of damaged ones. Guided by the AI, the nanobots can generate an organ from stem cells. By utilising an AI, nanobots can also differentiate between good and bad cells, something that cancer researchers have been trying to determine for many years. Moreover, nanobots can also rely on unsupervised machine learning to help them make these decisions. With the help of a vast library of data on the human body, and the difference in the appearances of these cells from normal cells, a model can be trained to identify which cells are infected and which aren’t. This will be targeted by the nanobots in order to tackle the problem and destroy only the affected cells. Nanotech And AI Can Resolve The World’s Food Problems The relationship between AI and nanotechnology isn’t restricted to the medical field purely. Multiple companies in the United States are aiming to bring the next step in the evolution of human food, fueled by nanotechnology and AI working together. They are working towards building plant-based products that look, smell and taste similar to meat, but aren’t. Even though this idea has been around for as long as veganism has, the companies claim that AI has offered them tools that have not been seen until now. The components of the food are broken down to their most basic level, as every aspect of the taste and flavour of the dish are caused by micro-components. These components are usually comprised of specific molecules, or other ingredients such as protein and fat, making it difficult to replicate without the presence of animal products. However, AI has found ways to combine these micro-components in the multiple permutations and combinations in order to achieve a result that is close to the end result. The algorithms find new ingredients or formulation that provide substitutes for animal based products. When the AI provides an unsatisfactory result, the model is tweaked by researchers and provides a satisfactory result. Until now, companies such as NotCo are using this method to create liquid foods like mayonnaise, scrambled egg replacements and cookie dough, as solid foods require a completely different level of complexity to replicate. Utilising an algorithm known as Giuseppe, NotCo aims to provide a scalable and accessible solution for using meat. Livestock farming is one of the biggest and most resource-intensive markets in the world, and many estimates peg it as occupying 60% of food-related greenhouse emissions. Solutions such as this would not only make the world greener, but also free up the land and water used by livestock. Nano-compute Will Power The Future AI Currently, compute is one of the biggest requirements of AI, alongside data to train the model. Now, advancements are being made in nanotechnology that could surpass the compute power offered by GPUs, leading to large-scale breakthroughs in systems utilizing models such as depp learning. Nano-enabled hardware has long been the goal of many computer manufacturers, as they are in the pursuit of shrinking hardware while extending its capabilities. IBM demonstrated it with the creation of the first artificial phase-change neurons, while other giants have shown interest in the field.
By Surya Kumar4 years ago in 01
How Enterprises Can Save Their Data from Gen AI Black Hole
Recently, Samsung employees came under fire for leaking confidential company information through an unorthodox method: ChatGPT. Since then, companies handling sensitive information have sent out memos cracking down on employees’ usage of ChatGPT, but OpenAI’s chatbot chaos is just about beginning. Generative AI has a lot of potential to disrupt the enterprise space, so it’s no wonder that this technology is now beginning to seep into companies’ tech stacks. However, as with any emerging technology, it comes with its own share of pitfalls, the biggest among them being data security. For enterprises that still wish to capitalise on the positives of generative AI, such as auto-coding platforms (Amazon CodeWhisper, Replit Ghostwriter, GitHub Copilot, etc), realistic text-to-speech algorithms (ElevenLabs Prime AI voice, OpenAI Whisper etc.), and image generation (DALL.E, Midjourney, Stable Diffusion, etc), there are a few steps they can take to ensure the safety of their data Safeguarding data Opacity of data policies from AI vendors presents a big challenge for companies that wish to leverage generative AI. Before feeding private information into any generative AI, users must first have a complete idea of where the data is going. Robert Blumofe, executive vice president and CTO at Akamai, stated, “Nobody should use or provide private information to a tool like this until you have a clear statement from the vendor on how they will use the information. Do they store, save, or share it? And does it become available to the public copy of the tool?” There are various levels of data leakage associated with AI services. Maintaining logs, storing, or saving inputs from companies is the least risky, but indexing this information as training data is the riskiest. This means that the data could end up in a public version of the same tool, causing data leaks. For companies compliant with the Global Data Privacy Regulation (GDPR), handing over data to any non-transparent AI data provider is a big no-no. Some GDPR regulators have also gone so far as to take a hardline stance against AI tools until they are safer with data, as seen by Italy’s ban of ChatGPT. Hence, the first step to ensuring data security with any AI vendor is to do due diligence and find a statement that clearly describes how the data will be used. Decision-makers must look at statements such as this one provided by Amazon Bedrock, which clearly states that user data will not be used and will not leave their private cloud. Self-hosted models vs responsible vendors While it may be tempting to just go with the pack and get a subscription to OpenAI’s APIs, the generative AI field is vast and growing quickly, especially when it comes to enterprise-focused use cases. Many key decision makers have also raised concerns as to how the technology can be used in their organisation. Brandon Jung, VP, ecosystem and business development, at Tabnine, told AIM, “People say, if we have a model, the model has to live in the same place and have the same level of security as our code, understandably. Lot of companies might first install and use the enterprise models, and then do custom models.” To understand how much of an impact a self-hosted AI can have, we can look at companies providing AI training services, such as Stability AI, one of the prominent open-source model providers in the AI field today. Emad Mostaque, the CEO of Stability AI, stated, “Dozens of major companies want models they own based on their own data and want to pay us to do it [train them]… It’s a pretty good model. We also build custom models for large companies and governments.” These kinds of fine-tuned models might be a good in-between for the security provided by self-trained and self-hosted models and the convenience of off-the-shelf APIs, similar to RedHat’s business model. There is also an alternative in services like Amazon BedRock or Azure’s OpenAI cloud services, which maintain the data privacy of the underlying cloud services while allowing companies to adopt generative AI. Even as all these advancements take place, one thing is clear: The AI cat is out of the bag. There is no going back for the enterprise, as the impact of AI goes beyond generating media. Whether it is automating repetitive tasks or transforming the data warehousing process, enterprises have to adapt to AI or fall behind. With the number of choices in the field and the current pace of innovation, decision-makers must keep data security in mind when picking AI solutions. Start writing...
By Surya Kumar4 years ago in 01
LangChain is the Android to OpenAI’s iOS
Earlier this month, LangChain, a Python framework for LLMs, received seed funding to the tune of $10 million from Benchmark. Soon after, the startup received another round of funding in the range of $20 to $25 million from Sequoia, quoting a valuation of $200 million. At its base, the main offering of LangChain is an abstraction wrapper that makes it easier for programmers to integrate LLMs into their programs. While it provides a relatively simple interface for LLMs, many developers swear against using LangChain in a production environment due to its anachronisms. However, the fact remains that many developers treat LangChain as the go-to framework in their applications, with the project garnering 20,000 stars on GitHub. This evokes a question on the valuation of the project — is an open-source abstraction wrapper really worth $200 million? What makes LangChain great? The framework was released in October 2022, in the midst of the generative AI wave. Based on the belief their true potential lay in bringing LLMs together with other applications, Harrison Chase, a programmer, developed this library. At first, it only had support for the OpenAI and Cohere API, along with a Python interpreter. Today, the project has blossomed to support over 20 model providers and hosting platforms, over 50 document loaders, more than 10 vector databases, and over 15 tools commonly used by LLMs. Developers have stated that what NumPy and Pandas did for machine learning, LangChain has done for LLMs, greatly increasing their usability and functionality. By using LangChain, developers can empower their applications by connecting them to an LLM, or leverage a large dataset by connecting an LLM to it. The main plus point of LangChain is its ability to create a chain of commands, an intuitive way to relay instructions to an LLM. Each command or ‘link’ of this chain can either call an LLM or a different utility, allowing for the creation of AI agents that can decide information flow based on user input. In addition to chaining, the package also has ways to implement LLM memory and built in benchmarks to evaluate the potential utility of an LLM. Even though LangChain is a fully featured and well supported framework, it is said to be notoriously unusable in a production environment. While the developers have created a support channel for those using this framework in a production environment, certain developers have spoken against using it. User Karrot_Kream on the Hacker News forum stated, “There also seems to be really poor observability in the code and performance seems to be an afterthought. I tell friends who ask about LangChain that it’s great to experiment with but not something I’d put into production.” Indeed, the dominant perspective is that while LangChain does bring some value to the table, especially in terms of integrating an LLM with minimal effort, serious users would rather just write their own wrapper. Moreover, large language models have evolved since the first launch of LangChain, from simple text generators to more fully-featured and nuanced programs. While there is still consensus that LangChain does offer some value, it seems that the value proposition is slowly shrinking.
By Surya Kumar4 years ago in 01
SITEBLOW REVIEW: IN MY OPINION, IS IT VALID TO USE? OTOS, DISCOUNT COUPONS AND PRICING OPTIONS
SITEBLOW OTOS, DISCOUNT COUPONS AND PRICING OPTIONS Coupon code: Siteblow3 ($3 off) source OTOS : https://outofboxreview.com/siteblow-review-otos-discount-coupons-and-pricing-is-it-valid-to-use/
By outofboxreview4 years ago in 01
Russia is developing technologies that rely on artificial intelligence to monitor phenomena that affect the climate
According to the available information, "Russian scientists were able to develop software that relies on artificial intelligence to monitor the El Niño and La Niña phenomena that affect climate change on Earth throughout the year. Experts from the Russian company Yandex contributed to the aforementioned project to use the Yandex.Cloud electronic cloud to document data."
By News Correct4 years ago in 01
Rust Programming Language: The Double-Edged Sword of Cybersecurity
Rust is a relatively new programming language that has gained immense popularity in recent years. It is a systems programming language that is designed to be fast, efficient, and secure. However, as with any technology, there is a dark side to Rust that is becoming increasingly apparent. In this blog, we will explore how cybercriminals are using Rust programming languages to carry out their nefarious activities.
By Arpan Chowdhury4 years ago in 01
Future of Digital Creator
How coding is used in cybersecurity is in the development of firewalls. Cybersecurity refers to the practice of protecting computer systems, networks, and sensitive information from unauthorized access, theft, damage, or disruption. With the increasing reliance on technology for communication, commerce, and other activities, cybersecurity has become an essential aspect of modern life.
By Sarah Ahmad4 years ago in 01
