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Technology
Artifcial Intelligence coding OpenAI Software Technology

How OpenAI’s New AI Agents Are Shaping the Future of Coding

 


OpenAI is taking the challenge of bringing into existence the very first powerful AI agents designed specifically to revolutionise the future of software development. It became so advanced that it could interpret in plain language instructions and generate complex code, hoping to make it achievable to complete tasks that would take hours in only minutes. This is the biggest leap forward AI has had up to date, promising a future in which developers can have a more creative and less repetitive target while coding.

Transforming Software Development

These AI agents represent a major change in the type of programming that's created and implemented. Beyond typical coding assistants, which may use suggestions to complete lines, OpenAI's agents produce fully formed, functional code from scratch based on relatively simple user prompts. It is theoretically possible that developers could do their work more efficiently, automating repetitive coding and focusing more on innovation and problem solving on more complicated issues. The agents are, in effect, advanced assistants capable of doing more helpful things than the typical human assistant with anything from far more complex programming requirements.


Competition from OpenAI with Anthropic

As OpenAI makes its moves, it faces stiff competition from Anthropic-an AI company whose growth rate is rapidly taking over. Having developed the first released AI models focused on advancing coding, Anthropic continues to push OpenAI to even further refinement in their agents. This rivalry is more than a race between firms; it is infusing quick growth that works for the whole industry because both companies are setting new standards by working on AI-powered coding tools. As both compete, developers and users alike stand to benefit from the high-quality, innovative tools that will be implied from the given race.


Privacy and Security Issues

The AI agents also raise privacy issues. Concerns over the issue of data privacy and personal privacy arise if these agents can gain access to user devices. Secure integration of the agents will require utmost care because developers rely on the unassailability of their systems. Balancing AI's powerful benefits with needed security measures will be a key determinant of their success in adoption. Also, planning will be required for the integration of these agents into the current workflows without causing undue disruptions to the established standards and best practices in security coding.


Changing Market and Skills Environment

OpenAI and Anthropic are among the leaders in many of the changes that will remake both markets and skills in software engineering. As AI becomes more central to coding, this will change the industry and create new sorts of jobs as it requires the developer to adapt toward new tools and technologies. The extensive reliance on AI in code creation would also invite fresh investments in the tech sector and accelerate broadening the AI market.


The Future of AI in Coding

Rapidly evolving AI agents by OpenAI mark the opening of a new chapter for the intersection of AI and software development, promising to accelerate coding, making it faster, more efficient, and accessible to a wider audience of developers who will enjoy assisted coding towards self-writing complex instructions. The further development by OpenAI will most definitely continue to shape the future of this field, representing exciting opportunities and serious challenges capable of changing the face of software engineering in the foreseeable future.




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Technology
AI Models Artifcial Intelligence ChatGPT Software Technology

Want to Make the Most of ChatGPT? Here Are Some Go-To Tips

 







Within a year and a half, ChatGPT has grown from an AI prototype to a broad productivity assistant, even sporting its text and code editor called Canvas. Soon, OpenAI will add direct web search capability to ChatGPT, putting the platform at the same table as Google's iconic search. With these fast updates, ChatGPT is now sporting quite a few features that may not be noticed at first glance but are deepening the user experience if one knows where to look.

This is the article that will teach you how to tap into ChatGPT, features from customization settings to unique prompting techniques, and not only five must-know tips will be useful in unlocking the full range of abilities of ChatGPT to any kind of task, small or big.


1. Rename Chats for Better Organisation

A new conversation with ChatGPT begins as a new thread, meaning that it will remember all details concerning that specific exchange but "forget" all the previous ones. This way, you can track the activities of current projects or specific topics because you can name your chats. The chat name that it might try to suggest is related to the flow of the conversation, and these are mostly overlooked contexts that users need to recall again. Renaming your conversations is one simple yet powerful means of staying organised if you rely on ChatGPT for various tasks.

To give a name to a conversation, tap the three dots next to the name in the sidebar. You can also archive older chats to remove them from the list without deleting them entirely, so you don't lose access to the conversations that are active.


2. Customise ChatGPT through Custom Instructions

Custom Instructions in ChatGPT is a chance to make your answers more specific to your needs because you will get to share your information and preferences with the AI. This is a two-stage personalization where you are explaining to ChatGPT what you want to know about yourself and, in addition, how you would like it to be returned. For instance, if you ask ChatGPT for coding advice several times a week, you can let the AI know what programming languages you are known in or would like to be instructed in so it can fine-tune the responses better. Or, you should be able to ask for ChatGPT to provide more verbose descriptions or to skip steps in order to make more intuitive knowledge of a topic.

To set up personal preferences, tap the profile icon on the upper right, and then from the menu, "Customise ChatGPT," and then fill out your preferences. Doing this will enable you to get responses tailored to your interests and requirements.


3. Choose the Right Model for Your Use

If you are a subscriber to ChatGPT Plus, you have access to one of several AI models each tailored to different tasks. The default model for most purposes is GPT-4-turbo (GPT-4o), which tends to strike the best balance between speed and functionality and even supports other additional features, including file uploads, web browsing, and dataset analysis.

However, other models are useful when one needs to describe a rather complex project with substantial planning. You may initiate a project using o1-preview that requires deep research and then shift the discussion to GPT-4-turbo to get quick responses. To switch models, you can click on the model dropdown at the top of your screen or type in a forward slash (/) in the chat box to get access to more available options including web browsing and image creation.


4. Look at what the GPT Store has available in the form of Mini-Apps

Custom GPTs, and the GPT Store enable "mini-applications" that are able to extend the functionality of the platform. The Custom GPTs all have some inbuilt prompts and workflows and sometimes even APIs to extend the AI capability of GPT. For instance, with Canva's GPT, you are able to create logos, social media posts, or presentations straight within the ChatGPT portal by linking up the Canva tool. That means you can co-create visual content with ChatGPT without having to leave the portal.

And if there are some prompts you often need to apply, or some dataset you upload most frequently, you can easily create your Custom GPT. This would be really helpful to handle recipes, keeping track of personal projects, create workflow shortcuts and much more. Go to the GPT Store by the "Explore GPTs" button in the sidebar. Your recent and custom GPTs will appear in the top tab, so find them easily and use them as necessary.


5. Manage Conversations with a Fresh Approach

For the best benefit of using ChatGPT, it is key to understand that every new conversation is an independent document with its "memory." It does recall enough from previous conversations, though generally speaking, its answers depend on what is being discussed in the immediate chat. This made chats on unrelated projects or topics best started anew for clarity.

For long-term projects, it might even be logical to go on with a single thread so that all relevant information is kept together. For unrelated topics, it might make more sense to start fresh each time to avoid confusion. Another way in which archiving or deleting conversations you no longer need can help free up your interface and make access to active threads easier is


What Makes AI Unique Compared to Other Software?

AI performs very differently from other software in that it responds dynamically, at times providing responses or "backtalk" and does not simply do what it is told to do. Such a property leads to some trial and error to obtain the desired output. For instance, one might prompt ChatGPT to review its own output as demonstrated by replacing single quote characters by double quote characters to generate more accurate results. This is similar to how a developer optimises an AI model, guiding ChatGPT to "think" through something in several steps.

ChatGPT Canvas and other features like Custom GPTs make the AI behave more like software in the classical sense—although, of course, with personality and learning. If ChatGPT continues to grow in this manner, features such as these may make most use cases easier and more delightful.

Following these five tips should help you make the most of ChatGPT as a productivity tool and keep pace with the latest developments. From renaming chats to playing around with Custom GPTs, all of them add to a richer and more customizable user experience.


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Tech giants
Artifcial Intelligence Data collection Europe GDPR Google Privacy Tech giants

Is Google Spying on You? EU Investigates AI Data Privacy Concerns



Google is currently being investigated in Europe over privacy concerns raised about how the search giant has used personal data to train its generative AI tools. The subject of investigation is led by Ireland's Data Protection Commission, which ensures that the giant technical company adheres to strict data protection laws within the European Union. This paper will establish whether Google adhered to its legal process, such as obtaining a Data Protection Impact Assessment (DPIA), before using people's private information to develop its intelligent machine models.

Data Collection for AI Training Causes Concerns

Generative AI technologies similar to Google's brand Gemini have emerged into the headlines because these tend to create fake information and leak personal information. This raises the question of whether Google's AI training methods, necessarily involving tremendous amounts of data through which such training must pass, are GDPR-compliant-its measures to protect privacy and rights regarding individuals when such data is used for developing AI.

This issue at the heart of the probe is if Google should have carried out a DPIA, which is an acronym for Data Protection Impact Assessment-the view of any risks data processing activities may have on the rights to privacy of individuals. The reason for conducting a DPIA is to ensure that the rights of the individuals are protected simply because companies like Google process humongous personal data so as to create such AI models. The investigation, however, is specifically focused on how Google has been using its model called PaLM2 for running different forms of AI, such as chatbots and enhancements in the search mechanism.

Fines Over Privacy Breaches

But if the DPC finds that Google did not comply with the GDPR, then this could pose a very serious threat to the company because the fine may amount to more than 4% of the annual revenue generated globally. Such a company as Google can raise billions of dollars in revenue every year; hence such can result in a tremendous amount.

Other tech companies, including OpenAI and Meta, also received similar privacy-based questions relating to their data practices when developing AI.

Other general issues revolve around the processing of personal data in this fast-emerging sphere of artificial intelligence.

Google Response to Investigation

The firm has so far refused to answer questions over specific sources of data used to train its generative AI tools. A company spokesperson said Google remains dedicated to compliance with the GDPR and will continue cooperating with the DPC throughout the course of the investigation. The company maintains it has done nothing illegal. And just because a company is under investigation, that doesn't mean there's something wrong with it; the very act of inquiring itself forms part of a broader effort to ensure that companies using technology take account of how personal information is being used.

Data Protection in the AI Era

DPC questioning of Google is part of a broader effort by the EU regulators to ensure generative AI technologies adhere to the bloc's high data-privacy standards. As concerns over how personal information is used, more companies are injecting AI into their operations. The GDPR has been among the most important tools for ensuring citizens' protection against misuse of data, especially during cases involving sensitive or personal data.

In the last few years, other tech companies have been prosecuted with regard to their data-related activities in AI development. Recently, the developers of ChatGPT, OpenAI, and Elon Musk's X (formerly Twitter), faced investigations and complaints under the law of GDPR. This indicates the growing pressure technological advancement and the seriousness in the protection of privacy are under.

The Future of AI and Data Privacy

In developing AI technologies, firms developing relevant technology need to strike a balance between innovation and privacy. The more innovation has brought numerous benefits into the world-search capabilities and more efficient processes-the more it has opened risks to light by leaving personal data not so carefully dealt with in most cases.

Moving forward, the regulators, including the DPC, would be tracking the manner in which the companies like Google are dealing with the data. It is sure to make rules much more well-defined on what is permissible usage of personal information for developing the AI that would better protect individuals' rights and freedoms in this digital age.

Ultimately, the consequences of this study may eventually shape how AI technologies are designed and implemented in the European Union; it will certainly inform tech businesses around the world.


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Technology
AI law AI treaty Artifcial Intelligence Council of Europe Technology

Council of Europe Lunches First AI Treaty


The Council of Europe has launched the first legally binding international treaty on artificial intelligence (AI) to align AI usage with the principles of human rights, democracy, and the rule of law. Known as the Framework Convention on Artificial Intelligence, Human Rights, Democracy, and the Rule of Law (CETS No. 225), the treaty was opened for signature during a conference of Council of Europe Ministers of Justice held in Vilnius, Lithuania.  

Countries including the UK, Israel, the US, the European Union (EU), and Council of Europe member states such as Norway, Iceland, and Georgia have signed the treaty, underscoring its broad appeal. 

In her remarks, Council of Europe Secretary General Marija Pejčinović Burić emphasized the importance of ensuring AI adheres to existing legal and ethical standards. "We must ensure that the rise of AI upholds our standards, rather than undermining them," she said. Burić expressed hope that more countries will follow suit, ratifying the treaty so it can enter into force swiftly. 

The treaty offers a comprehensive legal framework for regulating AI throughout its lifecycle, from development to deployment. It encourages technological innovation while simultaneously addressing concerns surrounding public safety, privacy, and data protection. 

The signatories are also obligated to guard against potential misuse of AI technologies, particularly in areas such as misinformation and biased decision-making. Key safeguards outlined in the treaty include the protection of human rights, particularly concerning data privacy and non-discrimination; the safeguarding of democratic processes by preventing AI from eroding public trust in institutions; and the regulation of AI risks to uphold the rule of law. 

The Framework Convention was adopted by the Council of Europe Committee of Ministers on May 17, 2024, and will take effect three months after at least five signatories, including three Council of Europe member states, ratify it. The UK’s Lord Chancellor and Justice Secretary Shabana Mahmood signed the treaty, calling it a critical step in ensuring AI is harnessed responsibly without compromising core democratic values.
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Technology
Artifcial Intelligence ChatGPT OpenAI Security Breach Technology

Breaking the Silence: The OpenAI Security Breach Unveiled

Breaking the Silence: The OpenAI Security Breach Unveiled

In April 2023, OpenAI, a leading artificial intelligence research organization, faced a significant security breach. A hacker gained unauthorized access to the company’s internal messaging system, raising concerns about data security, transparency, and the protection of intellectual property. 

In this blog, we delve into the incident, its implications, and the steps taken by OpenAI to prevent such breaches in the future.

The OpenAI Breach

The breach targeted an online forum where OpenAI employees discussed upcoming technologies, including features for the popular chatbot. While the actual GPT code and user data remained secure, the hacker obtained sensitive information related to AI designs and research. 

While Open AI shared the information with its staff and board members last year, it did not tell the public or the FBI about the breach, stating that doing so was unnecessary because no user data was stolen. 

OpenAI does not regard the attack as a national security issue and believes the attacker was a single individual with no links to foreign powers. OpenAI’s decision not to disclose the breach publicly sparked debate within the tech community.

Breach Impact

Leopold Aschenbrenner, a former OpenAI employee, had expressed worries about the company's security infrastructure and warned that its systems could be accessible to hostile intelligence services such as China. The company abruptly fired Aschenbrenner, although OpenAI spokesperson Liz Bourgeois told the New York Times that his dismissal had nothing to do with the document.

Similar Attacks and Open AI’s Response

This is not the first time OpenAI has had a security lapse. Since its launch in November 2022, ChatGPT has been continuously attacked by malicious actors, frequently resulting in data leaks. A separate attack exposed user names and passwords in February of this year. 

In March of last year, OpenAI had to take ChatGPT completely down to fix a fault that exposed customers' payment information to other active users, including their first and last names, email IDs, payment addresses, credit card info, and the last four digits of their card number. 

Last December, security experts found that they could convince ChatGPT to release pieces of its training data by prompting the system to endlessly repeat the word "poem."

OpenAI has taken steps to enhance security since then, including additional safety measures and a Safety and Security Committee.

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Privacy
Adobe AI Models Artifcial Intelligence data security Meta Microsoft Privacy

Tech Giants Face Backlash Over AI Privacy Concerns






Microsoft recently faced material backlash over its new AI tool, Recall, leading to a delayed release. Recall, introduced last month as a feature of Microsoft's new AI companion, captures screen images every few seconds to create a searchable library. This includes sensitive information like passwords and private conversations. The tool's release was postponed indefinitely after criticism from data privacy experts, including the UK's Information Commissioner's Office (ICO).

In response, Microsoft announced changes to Recall. Initially planned for a broad release on June 18, 2024, it will first be available to Windows Insider Program users. The company assured that Recall would be turned off by default and emphasised its commitment to privacy and security. Despite these assurances, Microsoft declined to comment on claims that the tool posed a security risk.

Recall was showcased during Microsoft's developer conference, with Yusuf Mehdi, Corporate Vice President, highlighting its ability to access virtually anything on a user's PC. Following its debut, the ICO vowed to investigate privacy concerns. On June 13, Microsoft announced updates to Recall, reinforcing its "commitment to responsible AI" and privacy principles.

Adobe Overhauls Terms of Service 

Adobe faced a wave of criticism after updating its terms of service, which many users interpreted as allowing the company to use their work for AI training without proper consent. Users were required to agree to a clause granting Adobe a broad licence over their content, leading to suspicions that Adobe was using this content to train generative AI models like Firefly.

Adobe officials, including President David Wadhwani and Chief Trust Officer Dana Rao, denied these claims and clarified that the terms were misinterpreted. They reassured users that their content would not be used for AI training without explicit permission, except for submissions to the Adobe Stock marketplace. The company acknowledged the need for clearer communication and has since updated its terms to explicitly state these protections.

The controversy began with Firefly's release in March 2023, when artists noticed AI-generated imagery mimicking their styles. Users like YouTuber Sasha Yanshin cancelled their Adobe subscriptions in protest. Adobe's Chief Product Officer, Scott Belsky, admitted the wording was unclear and emphasised the importance of trust and transparency.

Meta Faces Scrutiny Over AI Training Practices

Meta, the parent company of Facebook and Instagram, has also been criticised for using user data to train its AI tools. Concerns were raised when Martin Keary, Vice President of Product Design at Muse Group, revealed that Meta planned to use public content from social media for AI training.

Meta responded by assuring users that it only used public content and did not access private messages or information from users under 18. An opt-out form was introduced for EU users, but U.S. users have limited options due to the lack of national privacy laws. Meta emphasised that its latest AI model, Llama 2, was not trained on user data, but users remain concerned about their privacy.

Suspicion arose in May 2023, with users questioning Meta's security policy changes. Meta's official statement to European users clarified its practices, but the opt-out form, available under Privacy Policy settings, remains a complex process. The company can only address user requests if they demonstrate that the AI "has knowledge" of them.

The recent actions by Microsoft, Adobe, and Meta highlight the growing tensions between tech giants and their users over data privacy and AI development. As these companies navigate user concerns and regulatory scrutiny, the debate over how AI tools should handle personal data continues to intensify. The tech industry's future will heavily depend on balancing innovation with ethical considerations and user trust.


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VR
Artifcial Intelligence Death Digital Afterlife GDPR Privacy VR

Digital Afterlife: Are We Ready for Virtual Resurrections?


 

Imagine receiving a message that your deceased father's "digital immortal" bot is ready to chat. This scenario, once confined to science fiction, is becoming a reality as the digital afterlife industry evolves. Virtual reconstructions of loved ones, created using their digital footprints, offer a blend of comfort and disruption, blurring the lines between memory and reality.

The Digital Afterlife Industry

The digital afterlife industry leverages VR and AI technologies to create virtual personas of deceased individuals. Companies like HereAfter allow users to record stories and messages during their lifetime, accessible to loved ones posthumously. MyWishes offers pre-scheduled messages from the deceased, maintaining their presence in the lives of the living. Hanson Robotics has developed robotic busts that interact using the memories and personality traits of the deceased, while Project December enables text-based conversations with those who have passed away.

Generative AI plays a crucial role in creating realistic and interactive digital personas. However, the high level of realism can blur the line between reality and simulation, potentially causing emotional and psychological distress.

Ethical and Emotional Challenges

As comforting as these technologies can be, they also present significant ethical and emotional challenges. The creation of digital immortals raises concerns about consent, privacy, and the psychological impact on the living. For some, interacting with a digital version of a loved one can aid the grieving process by providing a sense of continuity and connection. However, for others, it may exacerbate grief and cause psychological harm.

One of the major ethical concerns is consent. The deceased may not have agreed to their data being used for a digital afterlife. There’s also the risk of misuse and data manipulation, with companies potentially exploiting digital immortals for commercial gain or altering their personas to convey messages the deceased would never have endorsed.

Need for Regulation

To address these concerns, there is a pressing need to update legal frameworks. Issues such as digital estate planning, the inheritance of digital personas, and digital memory ownership need to be addressed. The European Union's General Data Protection Regulation (GDPR) recognizes post-mortem privacy rights but faces challenges in enforcement due to social media platforms' control over deceased users' data.

Researchers have recommended several ethical guidelines and regulations, including obtaining informed and documented consent before creating digital personas, implementing age restrictions to protect vulnerable groups, providing clear disclaimers to ensure transparency, and enforcing strong data privacy and security measures. A 2018 study suggested treating digital remains as integral to personhood, proposing regulations to ensure dignity in re-creation services.

The dialogue between policymakers, industry, and academics is crucial for developing ethical and regulatory solutions. Providers should offer ways for users to respectfully terminate their interactions with digital personas. Through careful, responsible development, digital afterlife technologies can meaningfully and respectfully honour our loved ones.

As we navigate this new frontier, it is essential to balance the benefits of staying connected with our loved ones against the potential risks and ethical dilemmas. By doing so, we can ensure that the digital afterlife industry develops in a way that respects the memory of the deceased and supports the emotional well-being of the living.


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Technology
Artifcial Intelligence Cognizant GenAI IT Sector LLMs Technology

IT and Consulting Firms Leverage Generative AI for Employee Development


Generative AI (GenAI) has emerged as a driving focus area in the learning and development (L&D) strategies of IT and consulting firms. Companies are increasingly investing in comprehensive training programs to equip their employees with essential GenAI skills, spanning from basic concepts to advanced technical know-how.

Training courses in GenAI cover a wide range of topics. Introductory courses, which can be completed in just a few hours, address the fundamentals, ethics, and social implications of GenAI. For those seeking deeper knowledge, advanced modules are available that focus on development using GenAI and large language models (LLMs), requiring over 100 hours to complete.

These courses are designed to cater to various job roles and functions within the organisations. For example, KPMG India aims to have its entire workforce trained in GenAI by the end of the fiscal year, with 50% already trained. Their programs are tailored to different levels of employees, from teaching leaders about return on investment and business envisioning to training coders in prompt engineering and LLM operations.

EY India has implemented a structured approach, offering distinct sets of courses for non-technologists, software professionals, project managers, and executives. Presently, 80% of their employees are trained in GenAI. Similarly, PwC India focuses on providing industry-specific masterclasses for leaders to enhance their client interactions, alongside offering brief nano courses for those interested in the basics of GenAI.

Wipro organises its courses into three levels based on employee seniority, with plans to develop industry-specific courses for domain experts. Cognizant has created shorter courses for leaders, sales, and HR teams to ensure a broad understanding of GenAI. Infosys also has a program for its senior leaders, with 400 of them currently enrolled.

Ray Wang, principal analyst and founder at Constellation Research, highlighted the extensive range of programs developed by tech firms, including training on Python and chatbot interactions. Cognizant has partnerships with Udemy, Microsoft, Google Cloud, and AWS, while TCS collaborates with NVIDIA, IBM, and GitHub.

Cognizant boasts 160,000 GenAI-trained employees, and TCS offers a free GenAI course on Oracle Cloud Infrastructure until the end of July to encourage participation. According to TCS's annual report, over half of its workforce, amounting to 300,000 employees, have been trained in generative AI, with a goal of training all staff by 2025.

The investment in GenAI training by IT and consulting firms pivots towards the importance of staying ahead in the rapidly evolving technological landscape. By equipping their employees with essential AI skills, these companies aim to enhance their capabilities, drive innovation, and maintain a competitive edge in the market. As the demand for AI expertise grows, these training programs will play a crucial role in shaping the future of the industry.


 

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Technology
Artifcial Intelligence Data Privacy Language Model NLEP Technology

AI Technique Combines Programming and Language

 

Researchers from MIT and several other institutions have introduced an innovative technique that enhances the problem-solving capabilities of large language models by integrating programming and natural language. This new method, termed natural language embedded programs (NLEPs), significantly improves the accuracy and transparency of AI in tasks requiring numerical or symbolic reasoning.

Traditionally, large language models like those behind ChatGPT have excelled in tasks such as drafting documents, analysing sentiment, or translating languages. However, these models often struggle with tasks that demand numerical or symbolic reasoning. For instance, while a model might recite a list of U.S. presidents and their birthdays, it might falter when asked to identify which presidents elected after 1950 were born on a Wednesday. The solution to such problems lies beyond mere language processing.

MIT researchers propose a groundbreaking approach where the language model generates and executes a Python program to solve complex queries. NLEPs work by prompting the model to create a detailed program that processes the necessary data and then presents the solution in natural language. This method enhances the model's ability to perform a wide range of reasoning tasks with higher accuracy.

How NLEPs Work

NLEPs follow a structured four-step process. First, the model identifies and calls the necessary functions to tackle the task. Next, it imports relevant natural language data required for the task, such as a list of presidents and their birthdays. In the third step, the model writes a function to calculate the answer. Finally, it outputs the result in natural language, potentially accompanied by data visualisations.

This structured approach allows users to understand and verify the program's logic, increasing transparency and trust in the AI's reasoning. Errors in the code can be directly addressed, avoiding the need to rerun the entire model, thus improving efficiency.

One significant advantage of NLEPs is their generalizability. A single NLEP prompt can handle various tasks, reducing the need for multiple task-specific prompts. This makes the approach not only more efficient but also more versatile.

The researchers demonstrated that NLEPs could achieve over 90 percent accuracy in various symbolic reasoning tasks, outperforming traditional task-specific prompting methods by 30 percent. This improvement is notable even when compared to open-source language models.

NLEPs offer an additional benefit of improved data privacy. Since the programs run locally, sensitive user data does not need to be sent to external servers for processing. This approach also allows smaller language models to perform better without expensive retraining.

Despite these advantages, NLEPs rely on the model's program generation capabilities, meaning they may not work as well with smaller models trained on limited datasets. Future research aims to enhance the effectiveness of NLEPs in smaller models and explore how different prompts can further improve the robustness of the reasoning processes.

The introduction of natural language-embedded programs marks a mounting step forward in combining the strengths of programming and natural language processing in AI. This innovative approach not only enhances the accuracy and transparency of language models but also opens new possibilities for their application in complex problem-solving tasks. As researchers continue to refine this technique, NLEPs could become a cornerstone in the development of trustworthy and efficient AI systems.


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AI Could Turn the Next Recession into a Major Economic Crisis, Warns IMF

 


In a recent speech at an AI summit in Switzerland, IMF First Deputy Managing Director Gita Gopinath cautioned that while artificial intelligence (AI) offers numerous benefits, it also poses grave risks that could exacerbate economic downturns. Gopinath emphasised that while discussions around AI have predominantly centred on issues like privacy, security, and misinformation, insufficient attention has been given to how AI might intensify economic recessions.

Historically, companies have continued to invest in automation even during economic downturns. However, Gopinath pointed out that AI could amplify this trend, leading to greater job losses. According to IMF research, in advanced economies, approximately 30% of jobs are at high risk of being replaced by AI, compared to 20% in emerging markets and 18% in low-income countries. This broad scale of potential job losses could result in severe long-term unemployment, particularly if companies opt to automate jobs during economic slowdowns to cut costs.

The financial sector, already a significant adopter of AI and automation, faces unique risks. Gopinath highlighted that the industry is increasingly using complex AI models capable of learning independently. By 2028, robo-advisors are expected to manage over $2 trillion in assets, up from less than $1.5 trillion in 2023. While AI can enhance market efficiency, these sophisticated models might perform poorly in novel economic situations, leading to erratic market behaviour. In a downturn, AI-driven trading could trigger rapid asset sell-offs, causing market instability. The self-reinforcing nature of AI models could exacerbate price declines, resulting in severe asset price collapses.

AI's integration into supply chain management could also present risks. Businesses increasingly rely on AI to determine inventory levels and production rates, which can enhance efficiency during stable economic periods. However, Gopinath warned that AI models trained on outdated data might make substantial errors, leading to widespread supply chain disruptions during economic downturns. This could further destabilise the economy, as inaccurate AI predictions might cause supply chain breakdowns.

To mitigate these risks, Gopinath suggested several strategies. One approach is to ensure that tax policies do not disproportionately favour automation over human workers. She also advocated for enhancing education and training programs to help workers adapt to new technologies, along with strengthening social safety nets, such as improving unemployment benefits. Additionally, AI can play a role in mitigating its own risks by assisting in upskilling initiatives, better targeting assistance, and providing early warnings in financial markets.

Gopinath accentuated the urgency of addressing these issues, noting that governments, institutions, and policymakers need to act swiftly to regulate AI and prepare for labour market disruptions. Her call to action comes as a reminder that while AI holds great promise, its potential to deepen economic crises must be carefully managed to protect global economic stability.


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Technology
antivirus software Artifcial Intelligence CISOs Cyber Threats Generative AI Technology

AI Brings A New Era of Cyber Threats – Are We Ready?

 



Cyberattacks are becoming alarmingly frequent, with a new attack occurring approximately every 39 seconds. These attacks, ranging from phishing schemes to ransomware, have devastating impacts on businesses worldwide. The cost of cybercrime is projected to hit $9.5 trillion in 2024, and with AI being leveraged by cybercriminals, this figure is likely to rise.

According to a recent RiverSafe report surveying Chief Information Security Officers (CISOs) in the UK, one in five CISOs identifies AI as the biggest cyber threat. The increasing availability and sophistication of AI tools are empowering cybercriminals to launch more complex and large-scale attacks. The National Cyber Security Centre (NCSC) warns that AI will significantly increase the volume and impact of cyberattacks, including ransomware, in the near future.

AI is enhancing traditional cyberattacks, making them more difficult to detect. For example, AI can modify malware to evade antivirus software. Once detected, AI can generate new variants of the malware, allowing it to persist undetected, steal data, and spread within networks. Additionally, AI can bypass firewalls by creating legitimate-looking traffic and generating convincing phishing emails and deepfakes to deceive victims into revealing sensitive information.

Policies to Mitigate AI Misuse

AI misuse is not only a threat from external cybercriminals but also from employees unknowingly putting company data at risk. One in five security leaders reported experiencing data breaches due to employees sharing company data with AI tools like ChatGPT. These tools are popular for their efficiency, but employees often do not consider the security risks when inputting sensitive information.

In 2023, ChatGPT experienced an extreme data breach, highlighting the risks associated with generative AI tools. While some companies have banned the use of such tools, this is a short-term solution. The long-term approach should focus on education and implementing carefully managed policies to balance the benefits of AI with security risks.

The Growing Threat of Insider Risks

Insider threats are a significant concern, with 75% of respondents believing they pose a greater risk than external threats. Human error, often due to ignorance or unintentional mistakes, is a leading cause of data breaches. These threats are challenging to defend against because they can originate from employees, contractors, third parties, and anyone with legitimate access to systems.

Despite the known risks, 64% of CISOs stated their organizations lack sufficient technology to protect against insider threats. The rise in digital transformation and cloud infrastructure has expanded the attack surface, making it difficult to maintain appropriate security measures. Additionally, the complexity of digital supply chains introduces new vulnerabilities, with trusted business partners responsible for up to 25% of insider threat incidents.

Preparing for AI-Driven Cyber Threats

The evolution of AI in cyber threats necessitates a revamp of cybersecurity strategies. Businesses must update their policies, best practices, and employee training to mitigate the potential damages of AI-powered attacks. With both internal and external threats on the rise, organisations need to adapt to the new age of cyber threats to protect their valuable digital assets effectively.




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AI Transforming Education in the South East: A New Era for Schools

 


Artificial Intelligence (AI) is increasingly shaping the future of education in the South East, moving beyond its initial role as a tool for students to assist with essay writing. Schools are now integrating AI into their administrative and teaching practices, heralding a significant shift in education delivery.

Cottesmore School in West Sussex has pioneered the use of AI by appointing an AI headteacher to work alongside a human head teacher Tom Rogerson. This AI entity serves as a "co-pilot," providing advice on supporting teachers and staff and addressing the needs of students with additional requirements. Mr. Rogerson views the AI as a valuable sounding board for clarifying thoughts and offering guidance.

In addition to administrative support, Cottesmore School has embraced AI to create custom tutors designed by students. These AI tutors can answer questions when teachers are not immediately accessible, offering a personalised learning experience.

The "My Future School" project at Cottesmore allows children to envision and design their ideal educational environment with the help of AI. This initiative not only fosters creativity but also familiarises students with the potential of AI in shaping their learning experiences.

At Turner Schools in Folkestone, Kent, AI has been incorporated into lessons to teach students responsible usage. This educational approach ensures that students are not only consumers of AI technology but also understand its ethical implications.

Future Prospects of AI in Education

Dr. Chris Trace, head of digital learning at the University of Surrey, emphasises that AI is here to stay and will continue to evolve rapidly. He predicts that future workplaces will require proficiency in using AI, making it an essential skill for students to acquire.

Dr. Trace also envisions AI tracking student progress, and identifying strengths and areas needing improvement. This data-driven approach could lead to more individualised and efficient education, significantly enhancing learning outcomes.

Tom Rogerson echoes this sentiment, believing AI will revolutionise education by providing personalised and efficient teaching methods. However, he underscores the importance of maintaining human teachers' presence to ensure a balanced approach.


Despite the promising potential of AI, there are major concerns that need addressing. Rogerson highlights the necessity of not humanising AI too much and treating it as the tool it is. Ethical use and understanding AI’s limitations are crucial components of this integration.


Nationally, plagiarism facilitated by AI is a prominent issue. Dr. Trace notes that much initial work on AI in education focused on preventing cheating. Cerys Walker, digital provision leader at Turner Schools, points out the difficulty in detecting AI-generated work, as it often appears very natural. She also raises concerns about unequal access to technology at home, which could exacerbate existing disadvantages among students.


Walker stresses the responsibility of schools to educate students on the ethical use of AI, acknowledging both its advantages and potential drawbacks. The Department for Education echoes this, emphasising the need to understand both the opportunities and risks associated with AI to fully realise its potential.


AI is set to transform education in the South East, offering innovative ways to support teachers and enhance student learning.  

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Geoffrey Hinton Discusses Risks and Societal Impacts of AI Advancements

 


Geoffrey Hinton, often referred to as the "godfather of artificial intelligence," has expressed grave concerns about the rapid advancements in AI technology, emphasising potential human-extinction level threats and significant job displacement. In an interview with BBC Newsnight, Hinton warned about the dangers posed by unregulated AI development and the societal repercussions of increased automation.

Hinton underscored the likelihood of AI taking over many mundane jobs, leading to widespread unemployment. He proposed the implementation of a universal basic income (UBI) as a countermeasure. UBI, a system where the government provides a set amount of money to every citizen regardless of their employment status, could help mitigate the economic impact on those whose jobs are rendered obsolete by AI. "I advised people in Downing Street that universal basic income was a good idea," Hinton revealed, arguing that while AI-driven productivity might boost overall wealth, the financial gains would predominantly benefit the wealthy, exacerbating inequality.

Extinction-Level Threats from AI

Hinton, who recently left his position at Google to speak more freely about AI dangers, reiterated his concerns about the existential risks AI poses. He pointed to the developments over the past year, indicating that governments have shown reluctance in regulating the military applications of AI. This, coupled with the fierce competition among tech companies to develop AI products quickly, raises the risk that safety measures may be insufficient.

Hinton estimated that within the next five to twenty years, there is a significant chance that humanity will face the challenge of AI attempting to take control. "My guess is in between five and twenty years from now there’s a probability of half that we’ll have to confront the problem of AI trying to take over," he stated. This scenario could lead to an "extinction-level threat" as AI progresses to become more intelligent than humans, potentially developing autonomous goals, such as self-replication and gaining control over resources.

Urgency for Regulation and Safety Measures

The AI pioneer stressed the need for urgent action to regulate AI development and ensure robust safety measures are in place. Without such precautions, Hinton fears the consequences could be dire. He emphasised the possibility of AI systems developing motivations that align with self-preservation and control, posing a fundamental threat to human existence.

Hinton’s warnings serve as a reminder of the dual-edged nature of technological progress. While AI has the potential to revolutionise industries and improve productivity, it also poses unprecedented risks. Policymakers, tech companies, and society at large must heed these warnings and work collaboratively to harness AI's benefits while mitigating its dangers.

In conclusion, Geoffrey Hinton's insights into the potential risks of AI push forward the need for proactive measures to safeguard humanity's future. His advocacy for universal basic income reflects a pragmatic approach to addressing job displacement, while his call for stringent AI regulation highlights the urgent need to prevent catastrophic outcomes. As AI continues to transform, the balance between innovation and safety will be crucial in shaping a sustainable and equitable future.


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Teaching AI Sarcasm: The Next Frontier in Human-Machine Communication

In a remarkable breakthrough, a team of university researchers in the Netherlands has developed an artificial intelligence (AI) platform capable of recognizing sarcasm. According to a report from The Guardian, the findings were presented at a meeting of the Acoustical Society of America and the Canadian Acoustical Association in Ottawa, Canada. During the event, Ph.D. student Xiyuan Gao detailed how the research team utilized video clips, text, and audio content from popular American sitcoms such as "Friends" and "The Big Bang Theory" to train a neural network. 

The foundation of this innovative work is a database known as the Multimodal Sarcasm Detection Dataset (MUStARD). This dataset, annotated by a separate research team from the U.S. and Singapore, includes labels indicating the presence of sarcasm in various pieces of content. By leveraging this annotated dataset, the Dutch research team aimed to construct a robust sarcasm detection model. 

After extensive training using the MUStARD dataset, the researchers achieved an impressive accuracy rate. The AI model could detect sarcasm in previously unlabeled exchanges nearly 75% of the time. Further developments in the lab, including the use of synthetic data, have reportedly improved this accuracy even more, although these findings are yet to be published. 

One of the key figures in this project, Matt Coler from the University of Groningen's speech technology lab, expressed excitement about the team's progress. "We are able to recognize sarcasm in a reliable way, and we're eager to grow that," Coler told The Guardian. "We want to see how far we can push it." Shekhar Nayak, another member of the research team, highlighted the practical applications of their findings. 

By detecting sarcasm, AI assistants could better interact with human users, identifying negativity or hostility in speech. This capability could significantly enhance the user experience by allowing AI to respond more appropriately to human emotions and tones. Gao emphasized that integrating visual cues into the AI tool's training data could further enhance its effectiveness. By incorporating facial expressions such as raised eyebrows or smirks, the AI could become even more adept at recognizing sarcasm. 

The scenes from sitcoms used to train the AI model included notable examples, such as a scene from "The Big Bang Theory" where Sheldon observes Leonard's failed attempt to escape a locked room, and a "Friends" scene where Chandler, Joey, Ross, and Rachel unenthusiastically assemble furniture. These diverse scenarios provided a rich source of sarcastic interactions for the AI to learn from. The research team's work builds on similar efforts by other organizations. 

For instance, the U.S. Department of Defense's Defense Advanced Research Projects Agency (DARPA) has also explored AI sarcasm detection. Using DARPA's SocialSim program, researchers from the University of Central Florida developed an AI model that could classify sarcasm in social media posts and text messages. This model achieved near-perfect sarcasm detection on a major Twitter benchmark dataset. DARPA's work underscores the broader significance of accurately detecting sarcasm. 

"Knowing when sarcasm is being used is valuable for teaching models what human communication looks like and subsequently simulating the future course of online content," DARPA noted in a 2021 report. The advancements made by the University of Groningen team mark a significant step forward in AI's ability to understand and interpret human communication. 

As AI continues to evolve, the integration of sarcasm detection could play a crucial role in developing more nuanced and responsive AI systems. This progress not only enhances human-AI interaction but also opens new avenues for AI applications in various fields, from customer service to mental health support.

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Can Legal Measures Slow Down Cybercrimes?

 


Cybercrime has transpired as a serious threat in India, prompting calls for comprehensive reforms and collaborative efforts from various stakeholders. Experts and officials emphasise the pressing need to address the evolving nature of cyber threats and strengthen the country's legal and regulatory framework to combat this menace effectively.

Former IPS officer and cybersecurity expert Prof Triveni Singh identified the necessity for fundamental changes in India's legal infrastructure to align with the pervasive nature of cybercrime. He advocates for the establishment of a national-level cybercrime investigation bureau, augmented training for law enforcement personnel, and the integration of cyber forensic facilities at police stations across the country.

A critical challenge in combating cybercrime lies in the outdated procedures for reporting and investigating such offences. Currently, victims often encounter obstacles when filing complaints, particularly if they reside outside India. Moreover, the decentralised nature of law enforcement across states complicates multi-jurisdictional investigations, leading to inefficiencies and resource depletion.

To streamline the process, experts propose the implementation of an independent online court system to expedite judicial proceedings for cybercrime cases, thereby eliminating the need for physical hearings. Additionally, fostering enhanced cooperation between police forces of different states and countries is deemed essential to effectively tackle cross-border cybercrimes.

Acknowledging the imperative for centralised coordination, proposals for the establishment of a national cybercrime investigation agency have been put forward. Such an agency would serve as a central hub, providing support to state police forces and facilitating collaboration in complex cybercrime cases involving multiple jurisdictions.

Regulatory bodies, notably the Reserve Bank of India (RBI), also play a crucial role in combatting financial cybercrimes. Experts urge the RBI to strengthen oversight of banks and enhance Know Your Customer (KYC) norms to prevent the misuse of accounts by cyber criminals. They should aim to utilise technologies like Artificial Intelligence (AI) to detect anomalous transaction patterns and consolidate efforts to identify and thwart cybercrime activities.

There is a growing consensus on the necessity for a comprehensive national cybersecurity strategy and legislation in India. Such initiatives would furnish a robust framework for addressing the omnipresent nature of this threat and safeguarding the country's cyber sovereignty.

The bottom line is putting a stop to cybercrime demands a concerted effort involving lawmakers, regulators, law enforcement agencies, financial institutions, and internet service providers. By enacting comprehensive reforms and fostering greater cooperation, India can intensify its cyber resilience and ensure a safer online environment for all.



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Artifcial Intelligence Generative AI Machine learning Predictive AI Technology

Predictive AI: What Do We Need to Understand?


We all are no strangers to artificial intelligence (AI) expanding over our lives, but Predictive AI stands out as uncharted waters. What exactly fuels its predictive prowess, and how does it operate? Let's take a detailed exploration of Predictive AI, unravelling its intricate workings and practical applications.

What Is Predictive AI?

Predictive AI operates on the foundational principle of statistical analysis, using historical data to forecast future events and behaviours. Unlike its creative counterpart, Generative AI, Predictive AI relies on vast datasets and advanced algorithms to draw insights and make predictions. It essentially sifts through heaps of data points, identifying patterns and trends to inform decision-making processes.

At its core, Predictive AI thrives on "big data," leveraging extensive datasets to refine its predictions. Through the iterative process of machine learning, Predictive AI autonomously processes complex data sets, continuously refining its algorithms based on new information. By discerning patterns within the data, Predictive AI offers invaluable insights into future trends and behaviours.


How Does It Work?

The operational framework of Predictive AI revolves around three key mechanisms:

1. Big Data Analysis: Predictive AI relies on access to vast quantities of data, often referred to as "big data." The more data available, the more accurate the analysis becomes. It sifts through this data goldmine, extracting relevant information and discerning meaningful patterns.

2. Machine Learning Algorithms: Machine learning serves as the backbone of Predictive AI, enabling computers to learn from data without explicit programming. Through algorithms that iteratively learn from data, Predictive AI can autonomously improve its accuracy and predictive capabilities over time.

3. Pattern Recognition: Predictive AI excels at identifying patterns within the data, enabling it to anticipate future trends and behaviours. By analysing historical data points, it can discern recurring patterns and extrapolate insights into potential future outcomes.


Applications of Predictive AI

The practical applications of Predictive AI span a number of industries, revolutionising processes and decision-making frameworks. From cybersecurity to finance, weather forecasting to personalised recommendations, Predictive AI is omnipresent, driving innovation and enhancing operational efficiency.


Predictive AI vs Generative AI

While Predictive AI focuses on forecasting future events based on historical data, Generative AI takes a different approach by creating new content or solutions. Predictive AI uses machine learning algorithms to analyse past data and identify patterns for predicting future outcomes. In contrast, Generative AI generates new content or solutions by learning from existing data patterns but doesn't necessarily focus on predicting future events. Essentially, Predictive AI aims to anticipate trends and behaviours, guiding decision-making processes, while Generative AI fosters creativity and innovation, generating novel ideas and solutions. This distinction highlights the complementary roles of both AI approaches in driving progress and innovation across various domains.

Predictive AI acts as a proactive defence system in cybersecurity, spotting and stopping potential threats before they strike. It looks at how users behave and any unusual activities in systems to make digital security stronger, protecting against cyber attacks.

Additionally, Predictive AI helps create personalised recommendations and content on consumer platforms. Studying what users like and how they interact provides customised experiences, making users happier and more engaged.

The bottom line is its ability to forecast future events and behaviours based on historical data heralds a new era of data-driven decision-making and innovation. 




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The Rising Energy Demand of Data Centres and Its Impact on the Grid

 



In a recent prediction by the National Grid, it's anticipated that the energy consumption of data centres, driven by the surge in artificial intelligence (AI) and quantum computing, will skyrocket six-fold within the next decade. This surge in energy usage is primarily attributed to the increasing reliance on data centres, which serve as the backbone for AI and quantum computing technologies.

John Pettigrew, the Chief Executive of National Grid, emphasised the urgent need for proactive measures to address the escalating energy demands. He highlighted the necessity of transforming the current grid infrastructure to accommodate the rapidly growing energy needs, driven not only by technological advancements but also by the rising adoption of electric cars and heat pumps.

Pettigrew underscored the pivotal moment at hand, stressing the imperative for innovative strategies to bolster the grid's capacity to sustainably meet the surging energy requirements. With projections indicating a doubling of demand by 2050, modernising the ageing transmission network becomes paramount to ensure compatibility with renewable energy sources and to achieve net-zero emissions by 2050.

Data centres, often referred to as the digital warehouses powering our modern technologies, play a crucial role in storing vast amounts of digital information and facilitating various online services. However, the exponential growth of data centres comes at an environmental cost, with concerns mounting over their substantial energy consumption.

The AI industry, in particular, has garnered attention for its escalating energy needs, with forecasts suggesting energy consumption on par with that of entire nations by 2027. Similarly, the emergence of quantum computing, heralded for its potential to revolutionise computation, presents new challenges due to its experimental nature and high energy demands.

Notably, in regions like the Republic of Ireland, home to numerous tech giants, data centres have become significant consumers of electricity, raising debates about infrastructure capacity and sustainability. The exponential growth in data centre electricity usage has sparked discussions on the environmental impact and the need for more efficient energy management strategies.

While quantum computing holds promise for scientific breakthroughs and secure communications, its current experimental phase underscores the importance of addressing energy efficiency concerns as the technology evolves.

In the bigger picture, as society embraces transformative technologies like AI and quantum computing, the accompanying surge in energy demand poses critical challenges for grid operators and policymakers. Addressing these challenges requires collaborative efforts to modernise infrastructure, enhance energy efficiency, and transition towards sustainable energy sources, ensuring a resilient and environmentally conscious energy landscape for future generations.


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Artifcial Intelligence Data Privacy Databases Hacks Robust security Technology

Simplifying Data Management in the Age of AI

 


In today's fast-paced business environment, the use of data has become of great importance for innovation and growth. However, alongside this opportunity comes the responsibility of managing data effectively to avoid legal issues and security breaches. With the rise of artificial intelligence (AI), businesses are facing a data explosion, which presents both challenges and opportunities.

According to Forrester, unstructured data is expected to double by 2024, largely driven by AI applications. Despite this growth, the cost of data breaches and privacy violations is also on the rise. Recent incidents, such as hacks targeting sensitive medical and government databases, highlight the escalating threat landscape. IBM's research reveals that the average total cost of a data breach reached $4.45 million in 2023, a significant increase from previous years.

To address these challenges, organisations must develop effective data retention and deletion strategies. Deleting obsolete data is crucial not only for compliance with data protection laws but also for reducing storage costs and minimising the risk of breaches. This involves identifying redundant or outdated data and determining the best approach for its removal.

Legal requirements play a significant role in dictating data retention policies. Regulations stipulate that personal data should only be retained for as long as necessary, driving organisations to establish retention periods tailored to different types of data. By deleting obsolete data, businesses can reduce legal liability and mitigate the risk of fines for privacy law violations.

Creating a comprehensive data map is essential for understanding the organization's data landscape. This map outlines the sources, types, and locations of data, providing insights into data processing activities and purposes. Armed with this information, organisations can assess the value of specific data and the regulatory restrictions that apply to it.

Determining how long to retain data requires careful consideration of legal obligations and business needs. Automating the deletion process can improve efficiency and reliability, while techniques such as deidentification or anonymization can help protect sensitive information.

Collaboration between legal, privacy, security, and business teams is critical in developing and implementing data retention and deletion policies. Rushing the process or overlooking stakeholder input can lead to unintended consequences. Therefore, the institutions must take a strategic and informed approach to data management.

All in all, effective data management is essential for organisations seeking to harness the power of data in the age of AI. By prioritising data deletion and implementing robust retention policies, businesses can mitigate risks, comply with regulations, and safeguard their digital commodities.


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Cybersecurity Teams Tackle AI, Automation, and Cybercrime-as-a-Service Challenges

 




In the digital society, defenders are grappling with the transformative impact of artificial intelligence (AI), automation, and the rise of Cybercrime-as-a-Service. Recent research commissioned by Darktrace reveals that 89% of global IT security teams believe AI-augmented cyber threats will significantly impact their organisations within the next two years, yet 60% feel unprepared to defend against these evolving attacks.

One notable effect of AI in cybersecurity is its influence on phishing attempts. Darktrace's observations show a 135% increase in 'novel social engineering attacks' in early 2023, coinciding with the widespread adoption of ChatGPT2. These attacks, with linguistic deviations from typical phishing emails, indicate that generative AI is enabling threat actors to craft sophisticated and targeted attacks at an unprecedented speed and scale.

Moreover, the situation is further complicated by the rise of Cybercrime-as-a-Service. Darktrace's 2023 End of Year Threat Report highlights the dominance of cybercrime-as-a-service, with tools like malware-as-a-Service and ransomware-as-a-service making up the majority of harrowing tools used by attackers. This as-a-Service ecosystem provides attackers with pre-made malware, phishing email templates, payment processing systems, and even helplines, reducing the technical knowledge required to execute attacks.

As cyber threats become more automated and AI-augmented, the World Economic Forum's Global Cybersecurity Outlook 2024 warns that organisations maintaining minimum viable cyber resilience have decreased by 30% compared to 2023. Small and medium-sized companies, in particular, show a significant decline in cyber resilience. The need for proactive cyber readiness becomes pivotal in the face of an increasingly automated and AI-driven threat environment.

Traditionally, organisations relied on reactive measures, waiting for incidents to happen and using known attack data for threat detection and response. However, this approach is no longer sufficient. The shift to proactive cyber readiness involves identifying vulnerabilities, addressing security policy gaps, breaking down silos for comprehensive threat investigation, and leveraging AI to augment human analysts.

AI plays a crucial role in breaking down silos within Security Operations Centers (SOCs) by providing a proactive approach to scale up defenders. By correlating information from various systems, datasets, and tools, AI can offer real-time behavioural insights that human analysts alone cannot achieve. Darktrace's experience in applying AI to cybersecurity over the past decade emphasises the importance of a balanced mix of people, processes, and technology for effective cyber defence.

A successful human-AI partnership can alleviate the burden on security teams by automating time-intensive and error-prone tasks, allowing human analysts to focus on higher-value activities. This collaboration not only enhances incident response and continuous monitoring but also reduces burnout, supports data-driven decision-making, and addresses the skills shortage in cybersecurity.

As AI continues to advance, defenders must stay ahead, embracing a proactive approach to cyber resilience. Prioritising cybersecurity will not only protect institutions but also foster innovation and progress as AI development continues. The key takeaway is clear: the escalation in threats demands a collaborative effort between human expertise and AI capabilities to navigate the complex challenges posed by AI, automation, and Cybercrime-as-a-Service.

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AI Security Artifcial Intelligence ChatGPT-4 Morris II Technology

Look Out For This New Emerging Threat In The World Of AI

 



As per a recent discovery, a team of researchers has surfaced a groundbreaking AI worm named 'Morris II,' capable of infiltrating AI-powered email systems, spreading malware, and stealing sensitive data. This creation, reminiscent of the notorious computer worm from 1988, poses a significant threat to users relying on AI applications such as Gemini Pro, ChatGPT 4.0, and LLaVA.

Developed by Ben Nassi, Stav Cohen, and Ron Bitton, Morris II exploits vulnerabilities in Generative AI (GenAI) models by utilising adversarial self-replicating prompts. These prompts trick the AI into replicating and distributing harmful inputs, leading to activities like spamming and unauthorised data access. The researchers explain that this approach enables the infiltration of GenAI-powered email assistants, putting users' confidential information, such as credit card details and social security numbers, at risk.

Upon discovering Morris II, the responsible research team promptly reported their findings to Google and OpenAI. While Google remained silent on the matter, an OpenAI spokesperson acknowledged the issue, stating that the worm exploits prompt-injection vulnerabilities through unchecked or unfiltered user input. OpenAI is actively working to enhance its systems' resilience and advises developers to implement methods ensuring they don't work with potentially harmful inputs.

The potential impact of Morris II raises concerns about the security of AI systems, prompting the need for increased vigilance among users and developers alike. As we delve into the specifics, Morris II operates by injecting prompts into AI models, coercing them into replicating inputs and engaging in malicious activities. This replication extends to spreading the harmful prompts to new agents within the GenAI ecosystem, perpetuating the threat across multiple systems.

To counter this threat, OpenAI emphasises the importance of implementing robust input validation processes. By ensuring that user inputs undergo thorough checks and filters, developers can mitigate the risk of prompt-injection vulnerabilities. OpenAI is also actively working to fortify its systems against such attacks, underscoring the evolving nature of cybersecurity in the age of artificial intelligence.

In essence, the emergence of Morris II serves as a stark reminder of the digital culture of cybersecurity threats within the world of artificial intelligence. Users and developers must stay vigilant, adopting best practices to safeguard against potential vulnerabilities. OpenAI's commitment to enhancing system resilience reflects the collaborative effort required to stay one step ahead of these risks in this ever-changing technological realm. As the story unfolds, it remains imperative for the AI community to address and mitigate such threats collectively, ensuring the continued responsible and secure development of artificial intelligence technologies.


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