Scott Scholz

During Google I/O 2023, Google unveiled three features aimed at detecting AI-generated fake images in search results. The new tools, as reported by Bloomberg, include identifying the origins of an image, adding metadata to Google-generated AI images, and labeling other AI-generated images in search results. The proliferation of AI image synthesis models has made it increasingly easy to create realistic fake images, posing risks to misinformation, political propaganda, and the integrity of the historical record.

To combat these challenges, Google plans to introduce these features to its image search product in the upcoming months. Google acknowledges that misinformation is encountered regularly by a significant percentage of people, and therefore, they aim to develop user-friendly tools to help identify and evaluate visual content. The first feature, “About this image,” will provide users with additional information about an image’s history, including when it was indexed, its first appearance, and its presence on other online platforms such as news, social, or fact-checking sites.

By offering this context, users can make more informed judgments about an image’s reliability and determine if it requires further scrutiny. For instance, users may discover that an image depicting a fabricated Moon landing was flagged by news outlets as an AI-generated creation through the “About this image” feature. The second feature focuses on AI tools used in image creation. Google plans to label all images generated by its AI tools with special metadata that clearly indicates their AI origins.

Additionally, Google is collaborating with other platforms like Midjourney and Shutterstock, encouraging them to embed similar labels in their AI-generated images. These labels will be recognized by Google Image Search, displaying them to users within search results. While this approach is not foolproof as metadata can be altered or removed, it represents a significant effort to address the issue of deepfakes online.

As more images become AI-generated or AI-augmented over time, the boundary between “real” and “fake” may become increasingly blurred, influenced by evolving cultural norms. Ultimately, our trust in the source of information, regardless of its creation method, will continue to be crucial. However, solutions like those provided by Google can serve as valuable tools to assist users in evaluating source credibility as technology advances.

The free web and mobile chatbot offers many benefits, including links and sources for its answers.

Perplexity AI, created by a team of experts from OpenAI, Meta, Quora, and Databrick, aims to challenge the dominance of ChatGPT in the AI chatbot arena. Despite being the new kid on the block, Perplexity AI has raised $26 million in series A funding and offers a wide range of features, including a dedicated mobile app. While the business model remains uncertain, its free offering gives it a potential advantage over the subscription-based model of GPT-4.

Using machine learning and Natural Language Processing (NLP), Perplexity AI, like ChatGPT, provides responses to user queries. NLP enables computers to understand and process human language, with applications such as translation, chatbots, and voice assistants. While both chatbots offer detailed answers, Perplexity AI stands out with its mobile app, offering seamless access without the need for account signup. Additionally, Perplexity AI provides relevant links in its responses, including reputable sources like US News and World Reports, CIO, Great Value Colleges, and Computer Science Degree Hub, giving it an edge over GPT-4 in terms of up-to-date information.

In a comparison test between Perplexity AI and GPT-4, querying the top universities for artificial intelligence education, GPT-4 provided a list of ten universities while Perplexity AI offered a shorter list of five. However, Perplexity AI included links to relevant resources, showcasing its more recent information compared to GPT-4’s knowledge cutoff in 2021. Similarly, when asked to write an introduction email to Elon Musk for an interview on AI, both chatbots responded with detailed and polite emails. However, Perplexity AI once again excelled by providing additional resources, including links to the Tesla website and articles about Musk, although users should verify the accuracy and timeliness of these sources.

In conclusion, for those in search of a powerful and accessible AI chatbot with a competitive edge and no cost, Perplexity AI presents a promising option. With its impressive team and funding, dedicated mobile app, and the inclusion of relevant resources, Perplexity AI aims to rival the established ChatGPT in the AI landscape.

AI-driven Customer Relationship Management (CRM) systems encompass a range of AI-powered processes designed to efficiently manage large numbers of customers. These CRMs operate on four key principles:

1. Machine Learning: Machine Learning (ML) is an advanced technique widely utilized in commercial AI. ML involves training AI systems to adapt and perform effectively in dynamic conditions by closely monitoring patterns and trends over time, rather than relying solely on rigid and predefined instructions.
2. Predictive Analysis: Predictive analysis is crucial for enterprise planning and customer interactions. It empowers organizations to make data-driven decisions, accurately forecast sales, and efficiently allocate resources at all levels.
3. Automation: AI-based CRMs inherently incorporate artificial intelligence, making automation a fundamental aspect of their overall processes. Automation enhances the CRM’s capabilities to execute tasks with greater speed and precision compared to human counterparts. With advancements in AI, even complex workflows can be automated seamlessly and effortlessly.
4. Sentiment Analysis: One of the most valuable features of AI-powered CRMs is their ability to capture, analyze, and visualize how customers perceive products and services. Sentiment analysis enables customer service and sales agents to identify customer emotions, including satisfaction and frustration, across various communication channels such as phone, live chat, email, and social media.

In addition to these principles, AI-powered CRMs encompass various other components that enhance overall customer engagement.

Artificial Intelligence (AI) tools have burst on the scene with unprecedented business-altering capacity. Their buzz is becoming deafening and the promises and threats should be understood and capitalized upon because they will not be put back into the bottle. What’s seen cannot be unseen.

Two classifications of AI tools, graphics arts and Language Learning Models that can be prompted by language instructions, have been released to the masses that unleash a boost in creativity and cost savings for savvy business owners on a historic scale. Those who choose to ignore adaptation will like feel their impact negatively on the bottom line.

Our focal point in this post will be the AI language learning models, Google’s Bard and OpenAi/Microsoft’s ChatGPT, that are being deployed in marketing/advertising, research, legal, communications departments and more in businesses around the globe.

As opportune as these tools are, they need to be tightly controlled or they can create problems ranging from mildly embarrassing to legally and financially devastating.

Trust But Verify

To use an LLM (language learning model program), you type in a prompt. This can be in the form of a simple question, like a search, or a complex set of instructions. The program searches its database and can produce a detailed response almost before you blink twice. But it’s incumbent on the user to confirm the accuracy of the response. Consider a couple of examples:

ChatGPT

I recently made a request of ChaptGPT to provide a detailed description of a product and it’s characteristics and how it could benefit me. I already knew much of what the product could do, but was looking for more information to confirm spending some time with it.

In seconds, ChatGPT produced multiple paragraphs of convincing detail with a recommendation to give it a go. It was masterful, except for one thing. It had absolutely nothing to do with the product I thought I’d asked about!

Since you can question these tools and probe for more information, I suggested ChatGPT had given me misleading information. ChatGPT doubled-down and assured me all the information was completely accurate.

To be fair to ChatGPT, I shouldn’t have even put the query to the program because the product is a current product that the program did not have the ability to research. Nonetheless, it had the audacity to brazenly and convincingly assert it’s accuracy.

Google’s Bard

Bard has some advantages and challenges that ChatGPT does not. Advantages include access to the massive proprietary databases that Google has accumulated over the years. One of such sources that Bard scans when producing responses are the transcripts to YouTube videos.

To demonstrate the potential challenge this presents, the hosts of the All In Podcast put Bard through a light test. Hosts David Sacks and Jason Calacanis queried Bard about publications and positions Sacks had on a couple of topics. Bard miss-attributed several quotes to Sacks that did not accurately present his position on the issues. It effectively put words in Sacks’ mouth that were completely wrong. Sacks described Bard as ‘hallucinating.’ This is problematic for Google and potential Bard users and, although unlikely in this case, could create major legal issues for a user attempting to claim such statements as fact.

Why did Bard make this mistake? What Sacks and Calacanis realized is that the quotes Bard had made had come from a podcast they had done a few weeks earlier. Bard had read the transcript of the podcast and assumed the text to be the words of Sacks. This is an issue with YouTube transcripts. YouTube transcripts are a running narrative of the podcast that does not indicate when one speaker stops speaking and a second speaker carries on. If you read the transcripts while watching the podcast, you hear the change in voice. If you just read the transcript, it’s not always that simple or clear as to who is speaking. It was an issue for Bard in this case.

Much of what Bard had attributed to Sacks was actually the words of Calacanis, who held a completely different opinion on the subject they were discussing. The podcast hosts suggested that Google has further product refinement ahead, but that the promise and potential will be life changing.

So how should you utilize these tools? Or should you?

Use the tools, but be very specific in the instructions you give a language learning program. Don’t ask it open-ended questions for it to resort to its own devices to produce a response. Give it the content you want it to produce and ask it to do it better. Then confirm that what it produces is what you want.

Sonet Dynamics helps businesses leverage these tools in their marketing/advertising/sales/public relations and communications campaigns in ways that increase efficiencies and save businesses money. Call to discuss how we can help you.

According to the New York Post, Warren Buffett, the CEO of Berkshire Hathaway, has expressed concern about artificial intelligence (AI), comparing it to the creation of the atom bomb. Speaking at the company’s annual shareholder meeting on Saturday, Buffett stated that he becomes worried when something has the potential to do all kinds of things, as once it is created, it cannot be un-invented.

He acknowledged that the invention of the atom bomb was necessary during World War II, but questioned whether it is good for the world that the ability to create such a weapon has been unleashed for the next 200 years.

Although Buffett has been impressed by some of AI’s capabilities, including its ability to check all legal opinions “since the beginning of time,” he believes that artificial intelligence will change everything in the world, except for how men think and behave. His vice chairman, Charlie Munger, added that he is personally skeptical of the hype surrounding AI and that old-fashioned intelligence still works well.

Human intelligence is the result of millions of years of evolution, shaped by a complex interplay of genetics, environment, and culture. Human intelligence encompasses a wide range of cognitive abilities, including perception, memory, attention, language, creativity, and reasoning. Humans are also capable of social and emotional intelligence, which enables them to understand and interact with others, navigate complex social situations, and express empathy and compassion.

Despite the impressive advances in AI, there are still significant differences between the two types of intelligence. AI systems are specialized and focused on specific tasks, while human intelligence is general and adaptable to a wide range of situations. AI lacks the flexibility and creativity of human intelligence and is unable to understand context, emotions, and social cues in the same way as humans do, thereby giving those in Munger’s camp grounds for optimism.

Buffett’s and Munger’s comments follow a letter signed by 1,600 researchers, technology experts, and CEOs, including Elon Musk, warning of the potential dangers of AI and calling for a six-month pause to AI development. The letter stated that powerful AI systems should only be developed once their effects are positive, and their risks are manageable.