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Big Tech’s Data-Driven AI: Transparency, Consent, and Your Privacy

In the evolving world of AI, data transparency and user privacy are gaining significant attention as companies rely on massive amounts of information to fuel their AI models. While Big Tech giants need enormous datasets to train their AI systems, legal frameworks increasingly require these firms to clarify what they do with users’ personal data. Today, many major tech players use customer data to train AI models, but the specifics often remain obscure to the average user. 

In some instances, companies operate on an “opt-in” model where data usage requires explicit user consent. In others, it’s “opt-out”—data is used automatically unless the user takes steps to decline, and even this may vary based on regional regulations. For example, Meta’s data-use policies for Facebook and Instagram are “opt-out” only in Europe and Brazil, not the U.S., where laws like California’s Consumer Privacy Act enforce more transparency but allow limited control. 

The industry’s quest for data has led to a “land grab,” as companies race to stockpile information before emerging laws impose stricter guidelines. This data frenzy affects users differently across sectors: consumer platforms like social media often limit users’ choice to restrict data use, while enterprise software clients expect privacy guarantees.  

Controversy around data use has even caused some firms to change course. Adobe, following backlash over potentially using business customer data for training, pledged not to employ it for AI model development. Similarly, Apple has crafted a privacy-first architecture for its AI, promising on-device processing whenever possible and, when necessary, private cloud storage. Microsoft’s AI, including its Copilot+ features, has faced scrutiny as well. 

Privacy concerns delayed some features, prompting the company to refine how data like screenshots and app usage are managed. OpenAI, a leader in generative AI, offers varied data-use policies for free and paid users, giving businesses greater control over data than typical consumers.

Are Big Tech Companies Getting Rich from AI?

 


Big Tech companies like Amazon, Microsoft, and Alphabet have showcased impressive earnings, with a substantial boost from their advancements in artificial intelligence (AI) technology. Amazon's quarterly report revealed a 13% increase in net sales, primarily attributed to its AWS cloud computing segment, which saw a 17% sales boost, fueled by new AI functions like Amazon Q AI assistant and Amazon Bedrock generative AI service. Similarly, Alphabet's stock price surged nearly 10% following its robust earnings report, emphasising its AI-driven results. Microsoft also exceeded expectations, with its AI-heavy intelligent cloud division witnessing a 21% increase in revenue.

The Federal Communications Commission (FCC) has reinstated net neutrality rules, ensuring equal treatment of internet content by service providers. This move aims to prevent blocking, slowing down, or charging more for faster service for certain content, reinstating regulations repealed in 2017. Advocates argue that net neutrality preserves fair access, while opponents express concerns over regulatory burdens on broadband providers.

Strategies for Addressing Ransomware Threats

Ransomware attacks continue to pose a considerable threat to businesses, highlighting the unavoidable need for proactive measures. Halcyon CEO Jon Miller emphasises the importance of understanding ransomware risks and implementing robust backup systems. Having a clear plan of action in case of an attack is essential, including measures to minimise disruption and restore systems efficiently. While paying ransom may be a last resort in certain scenarios, it often leads to repeated targeting and underscores the necessity of enhancing overall security posture. Collaboration among companies and sharing of threat intelligence can also strengthen defences against ransomware attacks.

Meta's AI-enabled Smart Glasses

Meta's collaboration with Ray-Ban resulted in AI-enabled smart glasses, offering a seamless interface between the physical and online world. Priced at $299, these glasses provide enhanced functionalities like connecting with smartphones, music streaming, and camera features. Despite some limitations in identifying objects, these glasses signify a potential gateway to widespread adoption of virtual reality (VR) technology.

IBM and Nvidia Announce Major Acquisitions

IBM's acquisition of HashiCorp for $6.4 billion aims to bolster its cloud solutions with HashiCorp's expertise in managing cloud systems and applications. Similarly, Nvidia's purchase of GPU orchestrator Run:ai enhances its capabilities in efficiently utilising chips for processing needs, further solidifying its competitive edge.

As businesses increasingly adopt AI technology, collaborative decision-making and comprehensive training initiatives are essential for successful implementation. IBM's survey suggests that 40% of employees will require AI-related training and reskilling in the next three years, emphasising the urgency of investing in workforce development.

In essence, the recent earnings reports and strategic moves by tech giants translate the decisive role of AI in driving innovation and financial growth. However, amidst technological advancements, addressing cybersecurity threats like ransomware and ensuring equitable access to the internet remain crucial considerations for businesses and policymakers alike.


AI Developed to Detect Invasive Asian Hornets

 



Researchers at the University of Exeter have made an exceptional breakthrough in combating the threat of unsettling Asian hornets by developing an artificial intelligence (AI) system. Named VespAI, this automated system boasts the capability to identify Asian hornets with exceptional accuracy, per the findings of the university’s recent study.

Dr. Thomas O'Shea-Wheller, from the Environment and Sustainability Institute from Exter's Penryn Campus in Cornwall, highlighted the system's user-friendly nature, emphasising its potential for widespread adoption, from governmental agencies to individual beekeepers. He described the aim as creating an affordable and adaptable solution to address the pressing issue of invasive species detection.

How VespAI Works

VespAI operates using a compact processor and remains inactive until its sensors detect an insect within the size range of an Asian hornet. Once triggered, the AI algorithm aanalyses aptured images to determine whether the insect is an Asian hornet (Vespa velutina) or a native European hornet (Vespa crabro). If an Asian hornet is identified, the system sends an image alert to the user for confirmation.

Record Numbers of Sightings

The development of VespAI is a response to a surge in Asian hornet sightings not only across the UK but also in mainland Europe. In 2023, record numbers of these invasive hornets were observed, posing a significant threat to honeybee populations and biodiversity. With just one hornet capable of consuming up to 50 bees per day, the urgency for effective surveillance and response strategies is paramount.

Addressing Misidentification

Dr. Peter Kennedy, the mastermind behind VespAI, emphasised the system's ability to mitigate misidentifications, which have been prevalent in previous reports. By providing accurate and automated surveillance, VespAI aims to improve the efficiency of response efforts while minimising environmental impact.

What The Testing Indicate?

The effectiveness of VespAI was demonstrated through testing in Jersey, an area prone to Asian hornet incursions due to its proximity to mainland Europe. The system's high accuracy ensures that no Asian hornets are overlooked, while also preventing misidentification of other species.

Interdisciplinary Collaboration

The development of VespAI involved collaboration between biologists and data scientists from various departments within the University of Exeter. This interdisciplinary approach enabled the integration of biological expertise with cutting-edge AI technology, resulting in a versatile and robust solution.

The breakthrough AI system, dubbed VespAI, as detailed in their recent paper titled “VespAI: a deep learning-based system for the detection of invasive hornets,” published in the journal Communications Biology. This publication highlights the notable discovery made by the researchers in confronting the growing danger of invasive species. As we see it, this innovative AI system offers hope for protecting ecosystems and biodiversity from the threats posed by Asian hornets.


The Pros and Cons of Large Language Models

 


In recent years, the emergence of Large Language Models (LLMs), commonly referred to as Smart Computers, has ushered in a technological revolution with profound implications for various industries. As these models promise to redefine human-computer interactions, it's crucial to explore both their remarkable impacts and the challenges that come with them.

Smart Computers, or LLMs, have become instrumental in expediting software development processes. Their standout capability lies in the swift and efficient generation of source code, enabling developers to bring their ideas to fruition with unprecedented speed and accuracy. Furthermore, these models play a pivotal role in advancing artificial intelligence applications, fostering the development of more intelligent and user-friendly AI-driven systems. Their ability to understand and process natural language has democratized AI, making it accessible to individuals and organizations without extensive technical expertise. With their integration into daily operations, Smart Computers generate vast amounts of data from nuanced user interactions, paving the way for data-driven insights and decision-making across various domains.

Managing Risks and Ensuring Responsible Usage

However, the benefits of Smart Computers are accompanied by inherent risks that necessitate careful management. Privacy concerns loom large, especially regarding the accidental exposure of sensitive information. For instance, models like ChatGPT learn from user interactions, raising the possibility of unintentional disclosure of confidential details. Organisations relying on external model providers, such as Samsung, have responded to these concerns by implementing usage limitations to protect sensitive business information. Privacy and data exposure concerns are further accentuated by default practices, like ChatGPT saving chat history for model training, prompting the need for organizations to thoroughly inquire about data usage, storage, and training processes to safeguard against data leaks.

Addressing Security Challenges

Security concerns encompass malicious usage, where cybercriminals exploit Smart Computers for harmful purposes, potentially evading security measures. The compromise or contamination of training data introduces the risk of biased or manipulated model outputs, posing significant threats to the integrity of AI-generated content. Additionally, the resource-intensive nature of Smart Computers makes them prime targets for Distributed Denial of Service (DDoS) attacks. Organisations must implement proper input validation strategies, selectively restricting characters and words to mitigate potential attacks. API rate controls are essential to prevent overload and potential denial of service, promoting responsible usage by limiting the number of API calls for free memberships.

A Balanced Approach for a Secure Future

To navigate these challenges and anticipate future risks, organisations must adopt a multifaceted approach. Implementing advanced threat detection systems and conducting regular vulnerability assessments of the entire technology stack are essential. Furthermore, active community engagement in industry forums facilitates staying informed about emerging threats and sharing valuable insights with peers, fostering a collaborative approach to security.

All in all, while Smart Computers bring unprecedented opportunities, the careful consideration of risks and the adoption of robust security measures are essential for ensuring a responsible and secure future in the era of these groundbreaking technologies.





Mobile Privacy Milestone: Gmail Introduces Client-Side Encryption for Android and iOS

 


Encryption is one of the most important mechanisms for protecting data exchanged between individuals, especially when the information exchange occurs over e-mail and is quite sensitive. As a result, it can be complicated for users to be able to achieve this when they use public resources such as the internet. 

Now that Gmail has added client-side encryption to its mobile platform, users may feel safer when sending emails with Gmail on their mobile devices. Earlier this year, Google announced that it would be supporting Android and iOS mobile devices with client-side encryption in Gmail too. 

Using Google's client-side encryption (CSE) feature, which gives users more control over encryption keys and data access, Gmail can now be used on Android and iOS devices, as well as web browsers. In the past few months, Gmail's web version has been upgraded to support client-side encryption. This app lets users read and write encrypted emails directly from their smartphones and tablets. 

In addition to the Education Plus and Enterprise Plus editions of Google Workspace, the Education Standard edition also offers the feature. Workspace editions that don't support client-side encryption, such as Essentials, Business Starter, Business Standard Plus, Business Pro Plus, etc., do not support client-side encryption. 

Furthermore, users who have personal Google accounts are not able to access it. For those using email via desktop through Gmail, client-side encryption will be available at the end of 2022 on a trial basis. Workspace users with a subscription to Enterprise Plus, Education Plus, or Education Standard were the only ones able to take advantage of this feature at that time. 

Client-side encryption also prevented certain features from working, including the multi-send mode, signatures, and Smart Compose, which all functioned properly when using client-side encryption. A more robust version of the feature has been added to the Google Play Store since then. 

The company added the capability to allow users to see contacts even if they are unable to exchange encrypted emails so that they can keep in touch. There is also a security alert that appears in Google Mail when users receive attachments that are suspicious or that cannot be opened because of security concerns. 

While client-side encryption will now be available under the Enterprise Plus, Education Plus, and Education Standard Workspace accounts shortly, it has remained relatively exclusive. This type of Workspace account will also be the only kind of account that will be able to take advantage of the mobile rollout of client-side encryption. 

By using the S/MIME protocol, Google said that it will allow its users to encrypt and digitally sign their emails before sending them to Google servers so that they adhere to compliance and regulatory requirements. This feature lets users access and work with your most sensitive data from anywhere with their mobile devices. 

The blue lock icon present in the subject field of Gmail for Android or iOS users allows them to enable client-side encryption while they are writing a Gmail email for Android or iOS devices. Administrators will, however, have to enable access to the feature through their CSE administration interface, as it is disabled by default. 

During the past week, the search giant celebrated its 25th anniversary by letting teens (age 13 and above) try out its generative search service. The company also announced a new tool called Google-Extended that would enable website administrators to control how Google's Bard AI can be trained on their content. It allows website administrators to control whether or not Google can access their content. 

In addition to pulling the plug on Gmail's basic HTML version, which used to support legacy browsers and users with slow connections and could be used to support legacy browsers, Google will also drop the automatic loading of Gmail's Basic view, instead loading the Standard view by default early next year. Customers who are using Google Workspace Enterprise Plus, Education Plus, and Education Standard will be able to take advantage of this feature. 

Using AI for Loans and Mortgages is Big Risk, Warns EU Boss

 

The mortgage lending sector is experiencing a significant revolution driven by advanced technologies like artificial intelligence (AI) and machine learning. These cutting-edge technologies hold immense potential to revolutionize the lending process. 
However, alongside the benefits, there are also valid concerns surrounding the potential implications for human employment and the need to mitigate bias and discrimination in AI-driven decision-making. 

In an interview with the BBC, Margrethe Vestager, who is the European Commission's executive vice president, emphasized the importance of implementing "guardrails" to address the significant risks associated with technology, particularly in the context of artificial intelligence (AI). 

She highlighted the need for such precautions, especially when AI is involved in decision-making processes that directly impact individuals' livelihoods, such as determining their eligibility for a mortgage. 

How is AI benefiting Mortgage Lending Industry? 

1. Better customer experience: AI enables personalized customer experiences, allowing mortgage advisors to understand customer needs better and enhance their overall experience. 

2. Automation of routine tasks: AI automates repetitive tasks like data entry and document processing, freeing up time for mortgage advisors to focus on more strategic activities. 

3. Predictive analytics: AI analyzes data from multiple sources to provide insights into market trends and customer behavior, empowering mortgage advisors to make informed decisions and anticipate market changes. 

4. Boost risk assessment: AI algorithms analyze vast amounts of data, helping mortgage companies make better risk assessments and underwriting decisions, reducing loan defaults, and improving efficiency. 

5. Process optimization: AI identifies areas for process improvement by analyzing past transactions, enabling mortgage companies to streamline processes, reduce costs, and increase efficiency. 

6. Fraud identification: AI uses machine learning to detect potential fraud in mortgage applications, safeguarding both mortgage advisors and customers and ensuring the integrity of the lending process. 

7. Document management: AI automates document management, simplifying storage, retrieval, and management of customer information and loan documents, minimizing errors, and improving efficiency. 

8. Overcoming sales obstacles: AI tools like ChatGPT can assist in generating content ideas, helping mortgage professionals overcome content blocks, and leveraging video and social media for effective sales strategies. 

What are the risks of AI according to the Margrethe Vestager? 

Recently,  Margrethe Vestager, said that implementing "guardrails" is crucial to mitigate the significant risks associated with technology. Specifically, she emphasized the importance of having these measures in place when AI is employed to make decisions that directly impact individuals' livelihoods, such as determining their eligibility for a mortgage. 

Although the risk of extinction due to artificial intelligence (AI) is minimal, there are other pressing concerns to address. Discrimination is a prominent issue, where individuals might not receive fair treatment based on their true identities. 

Margrethe Vestager emphasized the need to prevent bias related to gender, race, or location when AI systems are employed by banks for mortgage assessments or by social services in local communities. It is essential to prioritize fairness and equal treatment to ensure everyone is respected and valued.

Ethical Issues Mount as AI Takes Bigger Decision-Making Role in Multiple Sectors

 

Even if we don't always acknowledge it, artificial intelligence (AI) has ingrained itself so deeply into our daily lives that it's difficult to resist. 

While ChatGPT and the use of algorithms in social media have received a lot of attention, law is a crucial area where AI has the potential to make a difference. Even though it may seem far-fetched, we must now seriously examine the possibility of AI determining guilt in courtroom procedures. 

The reason for this is that it calls into question whether using AI in trials can still be done fairly. To control the use of AI in criminal law, the EU has passed legislation.

There are already algorithms in use in North America that facilitate fair trials. The Pre-Trial Risk Assessment Instrument (PTRA), the Public Safety Assessment (PSA), and Compas are a few of these. The employment of AI technology in the UK criminal justice system was examined in a study produced by the House of Lords in November 2022. 

Empowering algorithms

On the one hand, it would be intriguing to observe how AI can greatly improve justice over time, for example by lowering the cost of court services or conducting judicial proceedings for small violations. AI systems are subject to strict restrictions and can avoid common psychological pitfalls. Some may even argue that they are more impartial than human judges.

Algorithms can also produce data that can be used by lawyers to find case law precedents, streamline legal processes, and assist judges. 

On the other hand, routine automated judgements made by algorithms might result in a lack of originality in legal interpretation, which might impede or halt the advancement of the legal system. 

The artificial intelligence (AI) technologies created for use in a trial must adhere to a variety of European law documents that outline requirements for upholding human rights. Among them are the Procedural European Commission for the Efficiency of Justice, the 2018 European Ethical Charter on the use of Artificial Intelligence in Judicial Systems and their Environment, and other laws passed in previous years to create an effective framework on the use and limitations of AI in criminal justice. We also need effective supervision tools, though, including committees and human judges. 

Controlling and regulating AI is difficult and involves many different legal areas, including labour law, consumer protection law, competition law, and data protection legislation. The General Data Protection Regulation, which includes the fundamental principle for justice and accountability, for instance, directly applies to choices made by machines.

The GDPR has rules to stop people from being subject to decisions made entirely by machines with no human input. This principle has also been discussed in other legal disciplines. The problem is already here; in the US, "risk-assessment" technologies are used to support pre-trial determinations of whether a defendant should be freed on bond or detained pending trial.

Sociocultural reforms in mind? 

Given that law is a human science, it is important that AI technologies support judges and solicitors rather than taking their place. Justice follows the division of powers, exactly like in contemporary democracies. This is the guiding principle that establishes a distinct division between the legislative branch, which creates laws, and the judicial branch, which consists of the system of courts. This is intended to defend against tyranny and protect civil freedoms. 

By questioning human laws and the decision-making process, the use of AI in courtroom decisions may upend the balance of power between the legislative and the judiciary. As a result, AI might cause a shift in our values. 

Additionally, as all forms of personal data may be used to predict, analyse, and affect human behaviour, using AI may redefine what is and is not acceptable activity, sometimes without any nuance.

Also simple to envision is the evolution of AI into a collective intelligence. In the world of robotics, collective AI has silently emerged. In order to fly in formation, drones, for instance, can communicate with one another. In the future, we might envision an increasing number of machines interacting with one another to carry out various jobs. 

The development of an algorithm for fair justice may indicate that we value an algorithm's abilities above those of a human judge. We could even be willing to put our own lives in this tool's hands. Maybe one day we'll develop into a civilization like that shown in Isaac Asimov's science fiction book series The Robot Cycle, where robots have intelligence on par with people and manage many facets of society. 

Many individuals are afraid of a world where important decisions are left up to new technology, maybe because they think that it might take away what truly makes us human. However, AI also has the potential to be a strong tool for improving our daily lives. 

Intelligence is not a state of perfection or flawless rationality in human reasoning. For instance, mistakes play a significant part in human activity. They enable us to advance in the direction of real solutions that advance our work. It would be prudent to keep using human thinking to control AI if we want to expand its application in our daily lives.

Here's How ChatGPT is Charging the Landscape of Cyber Security

 

Security measures are more important than ever as the globe gets more interconnected. Organisations are having a difficult time keeping up with the increasingly sophisticated cyberattacks. Artificial intelligence (AI) is now a major player in such a situation. ChatGPT, a language paradigm that is revolutionising cybersecurity, is one of the most notable recent developments in this field. In the cybersecurity sector, AI has long been prevalent. The future, however, is being profoundly impacted by generative AI and ChatGPT. 

The five ways that ChatGPT is fundamentally altering cybersecurity are listed below. 

Improved threat detection 

With the use of ChatGPT's natural language processing (NLP) capabilities, an extensive amount of data, such as security logs, network traffic, and user activity, can be analysed and comprehended. ChatGPT can identify patterns and anomalies that can point to a cybersecurity issue using machine learning algorithms, assisting security teams in thwarting assaults before they take place. 

Superior incident response 

Time is crucial when a cybersecurity problem happens. Organisations may be able to react to threats more rapidly and effectively because to ChatGPT's capacity to handle and analyse massive amounts of data properly and swiftly. For instance, ChatGPT can assist in determining the main reason for a security breach, offer advice on how to stop the assault, and make recommendations on how to avoid future occurrences of the same thing. 

Security operations automation

In order to free up security professionals to concentrate on more complicated problems, ChatGPT can automate common security tasks like patch management and vulnerability detection. In addition to increasing productivity, this lowers the possibility of human error.

Improved threat intelligence

To stay one step ahead of cybercriminals, threat intelligence is essential. Organisations may benefit from ChatGPT's capacity to swiftly and precisely detect new risks and vulnerabilities by using its ability to evaluate enormous amounts of data and spot trends. This can assist organisations in more effectively allocating resources and prioritising their security efforts.

Proactive threat assessment 

Through data analysis and pattern recognition, ChatGPT can assist security teams in spotting possible threats before they become serious problems. Security teams may then be able to actively look for dangers and take action before they have a chance to do much harm.

Is there an opposite side? 

In order to create more sophisticated social engineering or phishing assaults, ChatGPT can have an impact on the cybersecurity landscape. Such assaults are used to hoodwink people into disclosing private information or performing acts that could jeopardise their security. AI language models like ChatGPT have the potential to be utilised to construct more convincing and successful phishing and social engineering assaults since they can produce persuasive and natural-sounding language. 

Bottom line

ChatGPT is beginning to show tangible advantages as well as implications in cybersecurity. Although technology has the potential to increase security, it also presents new problems and hazards that need to be dealt with. Depending on how it is applied and incorporated into different cybersecurity systems and procedures, it will have an impact on the cybersecurity landscape. Organisations can protect their sensitive data and assets and stay one step ahead of cyberthreats by utilising the potential of AI. We can anticipate seeing ChatGPT and other AI tools change the cybersecurity scene in even more ground-breaking ways as technology advances.

Ransomware Threats in 2023: Increasing and Evolving

Cybersecurity threats are increasing every year, and 2023 is no exception. In February 2023, there was a surge in ransomware attacks, with NCC Group reporting a 67% increase in such attacks compared to January. The attacks targeted businesses of all sizes and industries, emphasizing the need for organizations to invest in robust cybersecurity measures.

The majority of these attacks were carried out by the Conti and LockBit 2.0 groups, with the emergence of new tactics such as social engineering and fileless malware to evade traditional security measures. This emphasizes the need for organizations to address persistent social engineering vulnerabilities through employee training and education.

A proactive approach to cybersecurity is vital for organizations, with the need for leaders to prioritize and invest in robust incident response plans. It's essential to have a culture of security where employees are trained to recognize and report suspicious activity.

According to a Security Intelligence article, the increasing frequency of global cyber attacks is due to several reasons, including the rise of state-sponsored attacks, the increasing use of AI and machine learning by hackers, and the growing threat of ransomware.

The threat of ransomware attacks is expected to continue in 2023, and companies need to have a strategy in place to mitigate the risk. It includes implementing robust security measures, training employees to identify and avoid social engineering tactics, and regularly backing up critical data. As cybersecurity expert Steve Durbin suggests, "Ransomware is not going away anytime soon, and companies need to have a strategy in place to mitigate the risk."

To safeguard themselves against the risk of ransomware attacks, organizations must be proactive. Companies need to focus and invest in strong incident response plans, employee education and training, and regular data backups in light of the rise in assaults. By adopting these actions, businesses can lessen the effects of ransomware attacks and safeguard their most important assets.


Cybercriminals Use ChatGPT to Ease Their Operations

 

Cybercriminals have already leveraged the power of AI to develop code that may be used in a ransomware attack, according to Sergey Shykevich, a lead ChatGPT researcher at the cybersecurity firm Checkpoint security.

Threat actors can use the capabilities of AI in ChatGPT to scale up their current attack methods, many of which depend on humans. Similar to how they aid cybercriminals in general, AI chatbots also aid a subset of them known as romance scammers. An earlier McAfee investigation noted that cybercriminals frequently have lengthy discussions in order to seem trustworthy and entice unwary victims. AI chatbots like ChatGPT can help the bad guys by producing texts, which makes their job easier.

The ChatGPT has safeguards in place to keep hackers from utilizing it for illegal activities, but they are far from infallible. The desire for a romantic rendezvous was turned down, as was the request to prepare a letter asking for financial assistance to leave Ukraine.

Security experts are concerned about the misuse of ChatGPT, which is now powering Bing's new, troublesome chatbot. They see the potential for chatbots to help in phishing, malware, and hacking assaults.

When it comes to phishing attacks, the entry barrier is already low, but ChatGPT could make it simple for people to proficiently create dozens of targeted scam emails — as long as they craft good prompts, according to Justin Fier, director for Cyber Intelligence & Analytics at Darktrace, a cybersecurity firm.

Most tech businesses refer to Section 230 of the Communications Decency Act of 1996 when addressing illegal or criminal content posted on their websites by third party users. According to the law, owners of websites where users can submit content, such as Facebook or Twitter, are not accountable for what is said there. Governments should be in charge of developing and enforcing legislation, according to 95% of IT respondents in the Blackberry study.

The open-source ChatGPT API models, which do not have the same content limitations as the online user interface, are being used by certain hackers, according to Shykevich.ChatGPT is notorious for being boldly incorrect, which might be an issue for a cybercriminal seeking to create an email meant to imitate someone else, experts told Insider. This could make cybercrime more difficult. Moreover, ChatGPT still uses barriers to stop illegal conduct, even if the correct script can frequently get around these barriers.

Cryptocurrencies Industry is Impacted by AI and ML

Artificial intelligence (AI) and Machine Learning is a fast expanding technology with the power to completely alter how we operate and live. Blockchain technology, a decentralized digital ledger system, is also thought to form the foundation of other upcoming technologies. These two methods can work together to develop strong new solutions across a range of sectors.

A number of indicators are used often by cryptocurrency traders. Nevertheless, given the prevalence of unorganized data in the digital world, manually creating trustworthy signals might be unfeasible. Massive amounts of information must be accurate, relevant, and clean prior to being assessed for investment insights.

In order to find investments and buy/sell signals as the number of investment alternatives increases, manual inquiry, extraction, and analysis procedures are no longer useful. AI has become a common tool in the financial sector, and it is much more powerful when integrated with blockchain.

Disadvantages of adopting blockchain with AI and ML:

1. Security: Employing blockchain with AI and ML might expose businesses to security issues. Blockchain-based solutions need a high level of trust since they exchange sensitive data, which is susceptible to malicious assaults.

2. Privacy: The integration of AI and blockchain technology has the risk of jeopardizing users' privacy because data recorded on the blockchain is indelible and accessible to all network users.

3. Scalability: When users upload more data to a blockchain, the size of the blockchain grows rapidly, creating scalability problems that can hamper performance and slow down processing rates.

4. Interoperability: Since different blockchains use dissimilar protocols, it is challenging to develop solutions that work well for all of them. As a result, they have trouble communicating with one another.

Blockchain technology, AI & ML successfully balance out each other's shortcomings, enabling reciprocal benefits, technological improvements, and robust enterprise support. AI in the blockchain sector can produce smart contracts and blockchain oracles that are more reliable, effective, and secure. These remedies have the power to lower expenses, boost efficiency, and open up fresh business prospects. One may anticipate more as technology develops further.

Microsoft Quietly Revealed a New Kind of AI


In the tangible future, humans will be interfacing their flesh with chips. Therefore, perhaps we should not have been shocked when Microsoft's researchers appeared to have hastened a desperate future. 

It was interestingly innocent and so very scientific. The headline of the researcher’s article read “Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers.” 

What do you think this may possibly mean? Is there a newer, faster method for a machine to record spoken words? 

The abstract by the researchers got off to a good start. It employs several words, expressions, and acronyms that many layman's language models would find unfamiliar. 

It explains why VALL-E is the name of the neural codec language model. This name must be intended to soothe you. What could be terrifying about a technology that resembles the adorable little robot from a sentimental movie? 

Well, this perhaps: "VALL-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt." 

The ChatGPT revolution: Microsoft Seems to Have Big Plans for This AI Chatbot 

The researchers often wanted to develop learning capabilities, while they have to settle for just waiting for them to show up. And what emerges from the researchers’ last sentence is quite surprising. 

Microsoft's big brains (AI, for an instance) can now create longer words and maybe lengthy speeches that were not actually said by you but sound remarkably like you with just three seconds of what one is saying. 

Through this, researchers wanted to shed light on how VALL-E utilizes an audio library assembled by Meta, one of the most reputable and recognized businesses in the world. It has a memory of 7,000 people conversing for 60,000 hours and is known as LibriLight. 

Also: Use AI-powered Personalization to Block Unwanted Calls And Texts 

This as well seems another level of sophistication. Taking the example of Peacock’s “The Capture,” in which deepfakes pose as a natural tool for the government. Perhaps, one should not really be worried since Microsoft is such a nice, inoffensive company these days. 

However, the idea that someone, anyone, can easily be conned into believing that a person is saying something he actually did not (perhaps, would never) itself is alarming. Especially when the researchers claim their capabilities to replicate the “emotions and acoustic behavior” of someone’s initial three-second speech as well. 

While this will be comforting when the researchers claim to have spotted this potential for distress. They offer: "Since VALL-E could synthesize speech that maintains speaker identity, it may carry potential risks in misuse of the model, such as spoofing voice identification or impersonating a specific speaker." 

One may as well stress enough to find a solution to these issues. An answer to this, according to the researchers is ‘Building a detection system.’ But this also leaves a few individuals wondering: “Why must we do this, at all?” Well, quite often in technology, the answer remains “Because we can.”  

How Can AI Understand Your Business Needs and Stop Threats?


AI in threat detection

In the current complicated cybersecurity scenario, threat detection is just a needle in the haystack. 

We have seen malicious actors exploiting everything they can get their hands on, from AI tools, to open-source code to multi-factor authentication (MFA), the security measures should also adapt from time to time across a company's entire digital landscape. 

AI threat detection, simply put is an AI that understands your needs- is essential that can businesses in defending themselves. According to Toby Lewis, threat analysis head at Darktrace, the tech uses algorithmic structures that make a baseline of a company's "normal." 

After that, it identifies threats, whether it's new or known, and in the end, makes "intelligent micro-decisions" about possible malicious activities. He believes that cyber-attacks have become common, rapid, and advanced. 

In today's scenario, cybersecurity teams can't be everywhere all the time when organizations are faced with cyber threats. 

Securing the digital landscapes 

It is understandable that complexity and operational risks go hand in hand as it is not easy to control and secure the "sprawling digital landscapes" of the new organizations. 

Attackers are hunting down data in the SaaS and cloud applications, the search also goes to the distributed infrastructure of endpoints- from IoT sensors to remotely-used computers to mobile phones. The addition of new digital assets and integration of partners and suppliers have also exposed organizations to greater risks. 

Not only have cyber threats become more frequent, but there is also a concern of how easily malicious cyber tools can be availed nowadays. These tools have contributed to the number of low-sophistication attacks, troubling chief information security officers (CISOs) and security teams. 

Cybercrime becoming a commodity

Cybercrime has become an "as-a-service" commodity, providing threat actors packaged tools and programs that are easy to install in a business. 

Another concern is the recently released ChatGP by OpenAI. It is an AI-powered content creation software that can be used for writing codes for malware and other malicious activities. 

Threat actors today keep on improving their ROI (return on investments), which means their techniques are constantly evolving, and security defenders are having problems predicting the threats. 

AI heavy lifting

AI threat detection comes in handy in this area. AI heavy lifting is important to defend organizations against cyber threats. AI is always active, its continuous learning capability helps the technology to scale and cover the vast volume of digital assets, data, and devices under an organization, regardless of their location. 

AI models focus on existing signature-based approaches, but signatures of known attacks become easily outdated as threat actors constantly change their techniques. To rely on past data is not helpful when an organization is faced with a newer and different threat. 

“Organizations are far too complex for any team of security and IT professionals to have eyes on all data flows and assets. Ultimately, the sophistication and speed of AI “outstrips human capacity," said Lewis. 

Detecting real-time attacks

Darktrace uses a self-learning AI that is continuously learning an organization, from moment to moment, detecting subtle patterns that reveal deviations from the norm. This "makes it possible to identify attacks in real-time, before attackers can do harm," said Lewis. 

Darktrace has dealt with Hafnium attacks that compromised Microsoft Exchange. In March 2022, Darktrace identified and stopped various attempts to compromise the Zobo ManageEngine vulnerability, two weeks prior to the discussion of the attack publicly. It later attributed the attack to APT41- a Chinese threat actor. 

War of algorithms- using AI to fight AI 

Darktrace researchers have tested offensive AI prototypes against its technology. Lewis calls it "a war of algorithms" or fighting AI with AI. 

Threat actors will certainly exploit AI for malicious purposes, therefore, it is crucial that security firms use AI to combat AI-based attacks.