Prepare to Be Amazed: Hackers Are Taking AI to the Next Level!

Artificial Intelligence (AI) has come a long way since its inception, and it continues to shape every aspect of our lives. AI helps us make decisions, automate processes, and enhance customer experiences in every industry. However, AI’s true potential is yet to be unleashed, and hackers are taking the lead to push AI to the next level.

AI has already proven to be a valuable tool for businesses to gain insights, improve operations, and detect anomalies. However, AI is only as good as the data it collects and analyses. That’s where hackers come in. Hackers can provide AI with the data it needs to make informed decisions, identify patterns, and even predict future behaviors.

Hackers often use AI to automate the process of collecting data from various sources, such as social media, websites, and public databases. This data is then used to train AI models, making them more accurate and efficient. By using AI, hackers can quickly detect vulnerabilities in computer systems, help companies optimize their operations, and even predict future cyber-attacks.

One of the most significant benefits of AI is that it can learn and adapt to new situations, making it an ideal tool for cybersecurity. Hackers can use AI to monitor network traffic, detect suspicious activity, and even identify potential threats before they happen. This proactive approach to cybersecurity can help prevent major cyber-attacks and save companies millions of dollars in losses.

Moreover, hackers are using AI to develop more sophisticated phishing scams that can easily fool unsuspecting victims. AI can be used to generate convincing emails or messages that appear to be from a legitimate source, making it harder for people to identify phishing attempts. This could have major implications for both individuals and businesses, as cybercriminals could use AI-created phishing scams to steal sensitive data or even access corporate networks.

In conclusion, AI has already proven to be a game-changer in many industries, but hackers are taking it to the next level. By leveraging AI’s capabilities, hackers can collect and analyze data to help companies optimize their operations, detect vulnerabilities, and even prevent cyber-attacks. However, with so much power in the hands of hackers, it’s essential to take a proactive approach to cybersecurity and stay vigilant against potential threats. So, let’s prepare to be amazed, as hackers continue to push the boundaries of what’s possible with AI.

There’s a lot of research into artificial intelligence by large companies (AI), but one online group wants to democratize the process. 

Key Takeaways

• A new collective of developers is building open-source AI models. The group uses massive language training models that it will release under open licenses. Open-source AI could help make the potentially game-changing power of the new technology less prone to biases and errors.

EleutherAI is a recently formed collective of volunteer researchers, engineers, and developers focused on open-source AI research. The organization uses the GPT-Neo and GPT-NeoX codebases to train massive language models that it plans to release under open licenses. 

“Open source data benefits researchers because scientists have more free resources to use to train models and complete research,” Edward Cui, the CEO  of AI company Graviti, told Lifewire in an email interview. His company is not involved in EueutherAI. “We know that scores of AI projects were held up by a general lack of high-quality data from real use cases, so it is vital to establish guidance that ensures data quality, with the help of the participating community.”

This Is the Way

The beginnings of the EleutherAI were humble. Last year, an independent AI researcher named Connor Leahy posted the following message on a Discord server: “Hey guys lets [SIC] give OpenAI a run for their money like the good ol’ days.” 

And so, the group was formed. It now has hundreds of contributors that post their code on the online software repository GitHub. 

Open-sourcing AI efforts aren’t new. In fact, Airbnb’s Airflow workflow management platform and Lyft’s data discovery engine are the outcomes of using open-source tools to enable data teams to do better work with data, pointed out Ali Rehman, project manager for software company CloudiTwins in an email interview with Lifewire. 

“Just as the open-source revolution has led to a transformation of software development, so too has it been driving the development and democratization of data science and artificial intelligence,” Rehman said. “Open source has become a critical enabler of enterprise data science solutions, with the majority of data scientists using open-source tools.”

Opening the Door

Developing open-source AI could help make the potentially game-changing power of the new technology less prone to biases and errors, some observers argue. 

AI research now primarily happens in the open, with nearly all companies, research labs, and universities presenting their results immediately in scholarly publications, Kush Varshney, an AI researcher at IBM, told Lifewire in an email interview. 

“This open community is essential, as it provides enhanced levels of checks and balances to ensure AI is being researched, created, deployed, and applied responsibly,” Varshney added. “This is especially critical in situations where these systems can influence the lives of our most vulnerable members of society. This openness applies not only to general machine learning and deep learning algorithms but also to elements of trustworthy AI.”

Rehman said that one of the critical differences between proprietary and open-source software is flexibility and customization. Proprietary AI research will have issues with security, updates, and optimizations. 

“This is because the open-source community-based approach gets valuable input from thousands of industry experts that identify potential security vulnerabilities which are then remediated more quickly,” Rehman added. “The consensus of the community means that quality is guaranteed and new opportunities are more easily identified.”

Another issue is that proprietary AI research will not be interoperable, meaning that it cannot work with various data formats and will likely have vendor lock-in, which prevents companies from testing and trying the software before committing to a solution, Rehman said. 

But not every aspect of AI research needs to be open-source, Chris Kent, the CEO of the medical AI company Reveal Surgical, told Lifewire in an email interview. “It’s important to protect the economic incentives that drive the commercial development of key applications of AI,” he said. 

However, research into AI needs a robust open-source component, Kent said. He added that open source works to build trust and use datasets that are not or should not be controlled by single institutions or companies. 

“An open-source approach is the best way to identify and compensate for underlying bias that may exist in training sets and will lead to more holistic, creative, and reliable applications of AI,” Kent said.

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