Here are some of the ways that AI is revolutionizing the marketing industry:

Customer Segmentation

One of the significant advantages of AI is its ability to analyze vast amounts of data and identify patterns. With AI, marketers can segment customers based on their behavior, demographics, and interests. This allows them to tailor their marketing messages to specific customer groups and increase engagement. For instance, an AI-powered marketing campaign can analyze a customer's purchase history, social media behavior, and web browsing history to provide personalized recommendations, increasing the likelihood of a conversion.

Chatbots

Chatbots have become a ubiquitous feature on many websites, and they are powered by AI. These chatbots use natural language processing (NLP) to understand and respond to customer queries. They can provide instant responses to customers, saving time and resources. Additionally, chatbots can analyze customer queries and provide insights into what customers are looking for. This can help businesses to optimize their marketing messages and provide better customer experiences.

Predictive Analytics

Predictive analytics is a data-driven approach that uses AI to identify patterns and predict future outcomes. In marketing, predictive analytics can help businesses to anticipate customer behavior, such as purchasing decisions and optimize their marketing campaigns accordingly. By analyzing past customer behavior, AI algorithms can identify trends and patterns, making it easier to target customers with personalized offers and recommendations.

Personalized Marketing

AI is transforming the way marketers approach personalization. Instead of using static segmentation, AI algorithms can analyze customer behavior in real time, providing real-time personalization. For instance, an e-commerce website can analyze a customer's browsing history and offer personalized product recommendations based on their preferences. This can significantly increase the chances of conversion, as customers are more likely to buy products that they are interested in.

Image and Video Recognition

AI is also revolutionizing image and video recognition in marketing. With AI-powered image recognition, marketers can analyze images and videos to identify objects and people, allowing them to target ads more effectively. For instance, an AI algorithm can analyze a customer's social media profile picture and determine their age, gender, and interests, allowing marketers to target them with personalized ads.

In conclusion, AI is revolutionizing the marketing industry by providing businesses with the ability to analyze vast amounts of data and personalize customer experiences. From customer segmentation to personalized marketing, AI is changing the way marketers approach their work. While some may fear that AI will replace human jobs, the truth is that AI is a tool that can help businesses to be more efficient, effective, and customer-focused. By leveraging AI in their marketing efforts, businesses can gain a competitive advantage and stay ahead of the curve.

Enhancing Fraud Detection and Prevention with Visa Advanced Authorization (VAA)

Visa is one of the largest payment companies in the world, handling billions of transactions every year. As such, it is a prime target for cyberattacks from hackers looking to steal sensitive financial information. To counter these threats, Visa has turned to artificial intelligence (AI) and machine learning (ML) to bolster its security defenses.

AI and ML offer several advantages over traditional cybersecurity methods. They can detect and respond to threats in real time, identify patterns in data that humans may miss, and adapt to changing threat landscapes. Visa has incorporated these technologies into its fraud detection and prevention systems, which help identify and block fraudulent transactions before they can cause harm.

Proactive Risk Assessment with Visa's Risk Manager Platform

One example of how Visa is using AI to counter cyberattacks is through its Visa Advanced Authorization (VAA) system. VAA uses ML algorithms to analyze transaction data and identify patterns of fraudulent activity. The system learns from historical data and uses that knowledge to detect and prevent future fraud attempts. This approach has been highly effective, with VAA reportedly blocking $25 billion in fraudulent transactions in 2020 alone.

Visa is also using AI to enhance its risk assessment capabilities. The company's Risk Manager platform uses ML algorithms to analyze transaction data and identify potential fraud risks. The system can detect unusual behavior patterns, such as a sudden increase in transaction volume or an unexpected change in location, and flag them for further investigation. This allows Visa to proactively address potential risks before they turn into full-fledged cyberattacks.

Using AI for Threat Intelligence with CyberSource Threat Intelligence

Another area where Visa is using AI to counter cyberattacks is in threat intelligence. The company's CyberSource Threat Intelligence service uses ML algorithms to analyze global threat data and identify potential security threats. This information is then shared with Visa's clients, helping them stay ahead of emerging threats and minimize their risk of a cyberattack.

Real-Time Detection and Disruption of Cyberattacks with Visa Payment Fraud Disruption (PFD) Platform

Visa has also developed a tool called the Visa Payment Fraud Disruption (PFD) platform, which uses AI to detect and disrupt cyberattacks targeting Visa clients. The PFD platform analyzes transaction data in real time and identifies any unusual activity that could indicate a cyberattack. The system then alerts Visa's cybersecurity team, who can take immediate action to prevent the attack from causing harm.

In addition to these measures, Visa is also investing in the development of AI and ML technologies to further enhance its cybersecurity capabilities. The company has partnered with leading AI firms and academic institutions to develop new tools and techniques to detect and prevent cyberattacks more effectively.

Overall, Visa's use of AI and ML in its cybersecurity systems has proven highly effective in countering cyberattacks. By leveraging these technologies, Visa is able to detect and respond to threats in real time, identify patterns in data that humans may miss, and adapt to changing threat landscapes. As cyberattacks continue to evolve and become more sophisticated, Visa will likely continue to invest in AI and ML to stay ahead of the curve and protect its customers' sensitive financial information.

The size of the language models in the LLaMA collection ranges from 7 billion to 65 billion parameters. In contrast, the GPT-3 model from OpenAI, which served as the basis for ChatGPT, has 175 billion parameters. 

Meta can potentially release its LLaMA model and its weights available as open source, since it has trained models through the openly available datasets like Common Crawl, Wkipedia, and C4. Thus, marking a breakthrough in a field where Big Tech competitors in the AI race have traditionally kept their most potent AI technology to themselves.   

In regards to the same, Project member Guillaume’s tweet read "Unlike Chinchilla, PaLM, or GPT-3, we only use datasets publicly available, making our work compatible with open-sourcing and reproducible, while most existing models rely on data which is either not publicly available or undocumented." 

Meta refers to its LLaMA models as "foundational models," which indicates that the company intends for the models to serve as the basis for future, more sophisticated AI models built off the technology, the same way OpenAI constructed ChatGPT on the base of GPT-3. The company anticipates using LLaMA to further applications like "question answering, natural language understanding or reading comprehension, understanding capabilities and limitations of present language models" and to aid in natural language research. 

While the top-of-the-line LLaMA model (LLaMA-65B, with 65 billion parameters) competes head-to-head with comparable products from rival AI labs DeepMind, Google, and OpenAI, arguably the most intriguing development comes from the LLaMA-13B model, which, as previously mentioned, can reportedly outperform GPT-3 while running on a single GPU when measured across eight common "common sense reasoning" benchmarks like BoolQ, PIQA LLaMA-13B opens the door for ChatGPT-like performance on consumer-level hardware in the near future, unlike the data center requirements for GPT-3 derivatives. 

In AI, parameter size is significant. A parameter is a variable that a machine-learning model employs in order to generate hypotheses or categorize data as input. The size of a language model's parameter set significantly affects how well it performs, with larger models typically able to handle more challenging tasks and generate output that is more coherent. However, more parameters take up more room and use more computing resources to function. A model is significantly more efficient if it can provide the same outcomes as another model with fewer parameters. 

"I'm now thinking that we will be running language models with a sizable portion of the capabilities of ChatGPT on our own (top of the range) mobile phones and laptops within a year or two," according to Simon Willison, an independent AI researcher in an Mastodon thread analyzing and monitoring the impact of Meta’s new AI models. 

Currently, a simplified version of LLaMA is being made available on GitHub. The whole code and weights (the "learned" training data in a neural network) can be obtained by filling out a form provided by Meta. A wider release of the model and weights has not yet been announced by Meta.  

The aforementioned findings were made by researchers from the Department of Energy’s Pacific Northwest National Laboratory based on an abstract simulation of the digital conflict between threat actors and defenders in a network and trained four different DRL neural networks in order to expand rewards based on minimizing compromises and network disruption. 

The simulated attackers transitions from the initial access and reconnaissance phase to other attack stages until they arrived at their objective, i.e. the impact and exfiltration phase. Apparently, these strategies were based on the classification of the MITRE ATT&CK architecture. 

Samrat Chatterjee, a data scientist who presented the team's work at the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington, DC, on February 14, claims that the successful installation and training of the AI system on the simplified attack surfaces illustrates the defensive responses to cyberattacks that, in current times, could be conducted by an AI model. 

"You don't want to move into more complex architectures if you cannot even show the promise of these techniques[…]We wanted to first demonstrate that we can actually train a DRL successfully and show some good testing outcomes before moving forward," says Chatterjee. 

AI Emerging as a New Trend in Cybersecurity 

Machine learning (ML) and AI tactics have emerged as innovative trends to administer cybersecurity in a variety of fields. This development in cybersecurity has started from the early integration of ML in email security in the early 2010s to utilizing ChatGPT and numerous AI bots that we see today to analyze code or conduct forensic analysis. The majority of security products now incorporate a few features that are powered by machine learning algorithms that have been trained on massive datasets. 

Yet, developing an AI system that is capable of proactive protection is still more of an ideal than a realistic approach. The PNNL research suggests that an AI defender could be made possible in the future, despite the many obstacles that still need to be addressed by researchers. 

"Evaluating multiple DRL algorithms trained under diverse adversarial settings is an important step toward practical autonomous cyber defense solutions[…] Our experiments suggest that model-free DRL algorithms can be effectively trained under multistage attack profiles with different skill and persistence levels, yielding favorable defense outcomes in contested settings," according to a statement published by the PNNL researchers. 

How the System Uses MITRE ATT&CK 

The initial objective of the research team was to develop a custom simulation environment based on an open-source toolkit, Open AI Gym. Through this environment, the researchers created attacker entities with a range of skill and persistence levels that could employ a selection of seven tactics and fifteen techniques from the MITRE ATT&CK framework. 

The attacker agents' objectives are to go through the seven attack chain steps—from initial access to execution, from persistence to command and control, and from collection to impact—in the order listed. 

According to Chatterjee of PNNL, it can be challenging for the attacker to modify their strategies in response to the environment's current state and the defender's existing behavior. 

"The adversary has to navigate their way from an initial recon state all the way to some exfiltration or impact state[…] We're not trying to create a kind of model to stop an adversary before they get inside the environment — we assume that the system is already compromised," says Chatterjee. 

Not Ready for Prime Time 

In the experiments, it was revealed that a particular reinforcement learning technique called a Deep Q Network successfully solved the defensive problem by catching 97% of the intruders in the test data set. Yet the research is just the beginning. Yet, security professionals should not look for an AI assistant to assist them with incident response and forensics anytime soon.  

One of the many issues that are required to be resolved is getting RL and deep neural networks to explain the causes that affected their decision, an area of research called explainable reinforcement learning (XRL).  

Moreover, the rapid emergence of AI technology and finding the most effective tactics to train the neutral network are both a challenge that needs to be addressed, according to Chatterjee.  

One of the less discussed consequences in regard to ChatGPT is its privacy risk. Google only yesterday launched Bard, its own conversational AI, and others will undoubtedly follow. Technology firms engaged in AI development have certainly entered a race. 

The issue would be its technology, which is entirely based on users’ personal data. 

300 Billion Words, How Many Are Yours? 

ChatGPT is apparently based on a massive language model, which backs up an enormous amount of data to operate and enhance its functions. Implying, the model gets more adept at seeing patterns, foreseeing what will happen next, and producing credible text as more data is used to train it. 

OpenAI, the developer of ChatGPT, sourced the Chatbot model with some 3 million words systematically taken from the internet –  via books, articles, websites, and posts – which also undeniably involves online users’ personal information, gathered without their consent. 

Every blog post, product review, or comment written on an article, which exists or ever existed in the online world has a good chance that the data or information involved it is was consumed by ChatGPT. 

What is the Issue? 

The gathered data used in order to train ChatGPT is problematic for numerous reasons. 

First, the collected data is unconsented, since none of the online users were ever asked if OpenAI could use their seemingly personal information. Thus, this would be a clear violation of privacy, especially when the data is sensitive and can be used to locate us, identify our loved ones, or identify ourselves. 

The usage of data can compromise what we refer to as contextual integrity even when the data is publicly available. This is a cornerstone idea in discussions about privacy law. Information on people must not be made public outside of the context in which it was first created. 

Moreover, OpenAI does not include any procedure for users to monitor whether the company has their personal information in-store, or to request it to be taken down. The European General Data Protection Regulation (GDPR), which guarantees this right, is still being discussed as to whether ChatGPT complies with its criteria. 

This “right to be forgotten” is specifically essential when it comes to situations involving information that is inaccurate or misleading, which seems to be a regular occurrence in ChatGPT. 

Furthermore, the scraped data that ChatGPT was trained on may be confidential or protected by copyright. For instance, the tool replicated the opening few chapters of Joseph Heller's copyrighted book Catch-22. 

Finally, OpenAI did not pay for the internet data it downloaded. Its creators—individuals, website owners, and businesses—were not being compensated. This is especially remarkable in light of the recent US$29 billion valuation of OpenAI, which is more than double its value in 2021. 

OpenAI has also recently announced ChatGPT Plus, which is a paid subscription plan that will provide users ongoing access to the tool, swift response times, and priority access to its new feature. By 2024, it is anticipated that this approach would help generate $1 billion in revenue. 

None of this would have been possible without the usage of ‘our’ data, acquired and utilized without our consent. 

Time to Consider the Issue? 

According to some professionals and experts, ChatGPT is a “tipping point for AI” - The realisation of technological advancement that can revolutionize the way we work, learn, write, and even think. 

Despite its potential advantages, we must keep in mind that OpenAI is a private, for-profit organization whose objectives and business demands may not always coincide with those of the larger community requirements. 

The privacy hazards associated with ChatGPT should serve as a caution. And as users of an increasing number of AI technologies, we need to exercise extreme caution when deciding what data to provide such tools with.  

The organization announces the product launch alongside the new AI-enhanced features for its Edge browser, promising users that the two will offer a fresh experience for acquiring information online. 

Microsoft, in a blog post, claims the new version as a technical breakthrough with its next-generation OpenAI model. “We’re excited to announce the new Bing is running on a new, next-generation OpenAI large language model that is more powerful than ChatGPT and customized specifically for search. It takes key learnings and advancements from ChatGPT and GPT-3.5 – and it is even faster, more accurate, and more capable,” the blog post states.  

In regards to the product launch, Microsoft CEO Satya Nadella says “race starts today, and we’re going to move and move fast […] “Most importantly, we want to have a lot of fun innovating again in search, because it’s high time.” at a special event at Microsoft headquarters in Redmond, Washington. 

According to Nadella, he believed it was ready to transform how people interact with other applications and do online searches. "This technology will reshape pretty much every software category that we know," he said. 

With the latest advancements, Bing will now respond to search queries in a more detailed manner, rather than just links and websites. 

Additionally, Bing users can now interact with bots to efficiently customize their queries. On the right side of a search results page, more contextual responses will be added. 

The announcement comes a day after Google unveiled information regarding Bard, its own brand-new chatbot. 

With both companies striving to launch their products to the market, Microsoft's investment, according to analyst Dan Ives of Wedbush Securities, will "massively increase" the company's capacity to compete, he said in a note to investors following the news. 

"This is just the first step on the AI front ... as [the] AI arms race takes place among Big Tech," he added. Microsoft has been spending billions on artificial intelligence and was an early supporter of San Francisco-based OpenAI. 

It declared last month that it will be extending its partnership with OpenAI through a "multiyear, multibillion-dollar investment." 

Bing will employ OpenAI technology, according to Microsoft, which is even more sophisticated than the ChatGPT technology announced last year. Additionally, the powers will be added to its Edge web browser.   

The study demonstrates how these AI systems can be programmed to reproduce precisely copyrighted artwork and medical images. It is a result that might help artists who are suing AI companies for copyright violations.  

Research: Extracting Training Data from Diffusion Models 

Researchers from Google, DeepMind, UC Berkeley, ETH Zürich, and Princeton obtained their findings by repeatedly prompting Google’s Imagen with image captions, like the user’s name. Following this, they analyzed if any of the images they produced matched the original photos stored in the model's database. The team was successful in extracting more than 100 copies of photos from the AI's training set. 

These image-generating AI models are apparently produced over vast data sets, that consist of images with captions that have been taken from the internet. The most recent technology works by taking images in the data sets and altering pixels individually until the original image is nothing more than a jumble of random pixels. The AI model then reverses the procedure to create a new image from the pixelated mess. 

According to Ryan Webster, a Ph.D. student from the University of Caen Normandy, who has studied privacy in other image generation models but is not involved in the research, the study is the first to demonstrate that these AI models remember photos from their training sets. This could also serve as an implication for startups wanting to use AI models in health care since it indicates that these systems risk leaking users’ private and sensitive data. 

Eric Wallace, a Ph.D. scholar who was involved in the study group, raises concerns over the privacy issue and says they hope to raise alarm regarding the potential privacy concerns with these AI models before they are extensively implemented in delicate industries like medicine. 

“A lot of people are tempted to try to apply these types of generative approaches to sensitive data, and our work is definitely a cautionary tale that that’s probably a bad idea unless there’s some kind of extreme safeguards taken to prevent [privacy infringements],” Wallace says. 

Another major conflict between AI businesses and artists is caused by the extent to which these AI models memorize and regurgitate photos from their databases. Two lawsuits have been filed against AI by Getty Images and a group of artists who claim the company illicitly scraped and processed their copyrighted content. 

The researchers' findings will ultimately aid artists to claim that AI companies have violated their copyright. The companies may have to pay artists whose work was used to train Stable Diffusion if they can demonstrate that the model stole their work without their consent. 

According to Sameer Singh, an associate professor of computer science at the University of California, Irvine, these findings hold paramount importance. “It is important for general public awareness and to initiate discussions around the security and privacy of these large models,” he adds.  

How Can ChatGPT be Used Maliciously? 

Recently, researchers from Check Point Software discovered that ChatGPT could be utilized to create phishing emails. When combined with Codex, a natural language-to-code system by OpenAI, ChatGPT can develop and disseminate malicious code. 

According to Sergey Shykevich, threat intelligence group manager at Check Point Software, “Our researchers built a full malware infection chain starting from a phishing email to an Excel document that has malicious VBA [Visual Basic for Application] code. We can compile the whole malware to an executable file and run it in a machine.” 

He adds that ChatGPT primarily produces “much better and more convincing phishing and impersonation emails than real phishing emails we see in the wild now.” 

In regards to the same, Lorrie Faith Cranor, director and Bosch Distinguished Professor of the CyLab Security and Privacy Institute and FORE Systems Professor of computer science and of engineering and public policy at Carnegie Mellon University says, “I haven’t tried using ChatGPT to generate code, but I’ve seen some examples from others who have. It generates code that is not all that sophisticated, but some of it is actually runnable code[…]There are other AI tools out there for generating code, and they are all getting better every day. ChatGPT is probably better right now at generating text for humans, and may be particularly well suited for generating things like realistic spoofed emails.” 

Moreover, the researchers have also discovered hackers that create malicious tools like info-stealers and dark web markets using ChatGPT. 

What AI Tools are More Worrisome? 

Cranor says “I think to use these [AI] tools successfully today requires some technical knowledge, but I expect over time it will become easier to take the output from these tools and launch an attack[…]So while it is not clear that what the tools can do today is much more worrisome than human-developed tools that are widely distributed online, it won’t be long before these tools are developing more sophisticated attacks, with the ability to quickly generate large numbers of variants.” 

Furthermore, complications could as well arise from the inability to detect whether the code was created by utilizing ChatGPT. “There is no good way to pinpoint that a specific software, malware, or even phishing email was written by ChatGPT because there is no signature,” says Shykevich. 

What Could be the Solution? 

One of the methods OpenAI is opting for is to “watermark” the output of GPT models, which could later be used to determine whether they are created by AI or humans. 

In order to safeguard companies and individuals from these AI-generated threats, Shykevich advises using appropriate cybersecurity measures. While the current safeguards are still in effect, it is critical to keep upgrading and bolstering their application. 

“Researchers are also working on ways to use AI to discover code vulnerabilities and detect attacks[…]Hopefully, advances on the defensive side will be able to keep up with advances on the attacker side, but that remains to be seen,” says Cranor. 

While ChatGPT and other AI-backed systems have the potential to fundamentally alter how individuals interact with technology, they also carry some risk, particularly when used in dangerous ways. 

“ChatGPT is a great technology and has the potential to democratize AI,” adds Shykevich. “AI was kind of a buzzy feature that only computer science or algorithmic specialists understood. Now, people who aren’t tech-savvy are starting to understand what AI is and trying to adopt it in their day-to-day. But the biggest question, is how would you use it—and for what purposes?”