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  • 🤖 Insider Insights: 8 Key AI Trends Unveiled by Tech CEOs for 2024

🤖 Insider Insights: 8 Key AI Trends Unveiled by Tech CEOs for 2024

PLUS: Introducing Ferret

Happy 2024, AI Enthusiasts!

Embrace the Unfolding Possibilities of a New Year with AI!

As we step into this fresh chapter, let's celebrate the strides and innovations that have shaped the AI landscape. The past year was a testament to the incredible growth and potential of artificial intelligence, marked by groundbreaking advancements in models, applications, and ethical considerations.

Now, standing at the threshold of a new year, let's embark on this journey with optimism and determination. Together, let's delve deeper into the realms of innovation, explore the uncharted territories, and craft solutions that elevate humanity.

Let's embrace collaboration, diversity, and inclusivity in our pursuit of AI excellence. As we navigate the unknown, let's prioritize ethical AI, ensuring that progress is not just impactful but also responsible and mindful of its societal implications.

May this new year be a canvas for ingenious ideas, transformative discoveries, and meaningful connections within the AI community. Here's to a year brimming with opportunities, learning, and the collective pursuit of a brighter, AI-enabled future.

Happy New Year from our AI newsletter family to yours! Let's make 2024 a year of innovation, empathy, and progress

Cheers,
Hyrise AI Team

In 2023, generative AI experienced a monumental surge with the launch of groundbreaking models like GPT-4, GPT-4V, Google Bard, PaLM 2, and Google Gemini. This proliferation signals an intense competition among developers, aiming to revolutionize and streamline everyday tasks through automation.

Apple introduced Ferret, an open-source multimodal generative AI model developed in partnership with Cornell University.

In today’s extended new year issue:

  • 🤖 Insider Insights: 8 Key AI Trends Unveiled by Tech CEOs for 2024

  • 🦾 Introducing Ferret: Apple's Open-Source AI Model Merging Vision and Language

  • 🛠️ 3 New AI tools

  • 💻 Custom prompts ChatGPT and DALL-E 3

  • 🤖 3 Quick AI updates

Read time: 10 minutes.

LATEST HIGHLIGHTS

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Image source: Unsplash

To recap:

  1. Rise of AI-Powered Malware: There's a growing concern about the emergence of generative AI-driven malware that could autonomously strategize attacks, a particular worry regarding threats from nation-state adversaries. This evolution might lead to more sophisticated and adaptive attack patterns.

  2. Passkey Adoption for Enhanced Security: With the proliferation of AI in cybercrime (like WormGPT), there's an imminent need for stronger authentication methods. Passkeys are predicted to surpass passwords, providing more secure authentication, especially amid rising cyber threats.

  3. Ensuring Safety in AI Models: Prioritizing safety and privacy in AI models is crucial. Having mechanisms to identify and report safety concerns is likened to addressing security vulnerabilities, ensuring continuous improvement and risk mitigation.

  4. Impact of Large Language Models (LLMs) on Cloud Security: LLMs, a fusion of Generative AI and language models, will revolutionize cybersecurity by enhancing detection capabilities and refining security operations, allowing professionals to focus on strategic analysis.

  5. Evolution of Data Security Strategies: The advent of Generative AI might reshape data security approaches. Organizations might shift from deleting unnecessary data to retaining information for training AI, altering how risk is perceived and managed in data security.

  6. Trust Concerns in AI Decision-Making: Similar to the early days of cloud services, the rapid integration of AI raises concerns about security, transparency, and ethical implications. The potential lack of controls could lead to security breaches and erode trust in AI decision-making.

  7. Enhanced Developer Efficiency and Risks: AI tools like Co-Pilot and ChatGPT will streamline development processes but introduce potential vulnerabilities. Balancing efficiency with security is crucial, urging leaders to consider AI in security policies and incident responses.

  8. Mainstream Integration of Video Generation: Advancements in video generative models will lead to a significant portion of online video content incorporating AI-generated elements. Moreover, more sophisticated AI frameworks will emerge, powering novel interfaces and products.

In summary, the forecast for 2024 anticipates a transformative period driven by AI, balancing security concerns with opportunities to automate workflows and unlock value for both enterprises and individuals.

The details:

  •  AI-Powered Malware Evolution: There's a concern about the evolution of generative AI-fueled malware that autonomously adapts attack strategies based on the target environment. This shift from human-coded attacks to self-learning probes could pose significant cybersecurity threats, especially from nation-state adversaries.

  • Passkey Adoption and Passwordless Future: Passkeys are predicted to surpass passwords as a more secure authentication method. Despite ongoing transitions, mass adoption of passkeys is expected due to the escalating sophistication of cyberattacks leveraging generative AI, like WormGPT, prompting businesses and consumers to seek more reliable authentication methods.

  • Large Language Models (LLMs) and Cloud Security: The amalgamation of Generative AI and LLMs will redefine cybersecurity by bolstering detection capabilities and refining security protocols in cloud environments. LLMs are anticipated to enhance log analysis, detect zero-day attacks, and streamline security operations, enabling professionals to focus on strategic analysis and innovation in security frameworks.Here is the key takeaway: Generative AI (GAI) is playing a significant role in revolutionizing the electric vehicle (EV) industry. It is helping to accelerate the development of efficient batteries, improve EV range and charging speed, and provide predictive maintenance insights. This technology has the potential to address critical challenges in the EV industry and fuel innovation, making electric vehicles more practical and appealing to consumers.

Jan. 2020, Chengdu, China - Two staffs was looking down the crowd in Taikoo Li shopping center through the window of the Apple Store. Although people’s enthusiasm for shopping seemed not to have been deterred by the Corona virus from Wuhan because of the coming lunar New Year, many of them was wearing surgical masks. So did the Apple staffs.

Image source: Unsplash

In Summary: Apple introduced Ferret, an open-source multimodal generative AI model developed in partnership with Cornell University. Launched on GitHub in October 2023 alongside a research paper, Ferret merges computer vision and natural language processing. It uniquely interacts with visual content by identifying objects, linking textual concepts to visual elements, and engaging in nuanced conversations about images. Apple's focus on conversational AI, led by AI chief John Giannandrea, includes an internal chatbot nicknamed "Apple GPT." Although tightly restricted internally, this AI is utilized for prototyping and query responses. Apple's significant investments in conversational AI, including projected spending of over $4 billion on AI servers in 2024, underscore its commitment to advancing this field. Ferret's innovation lies in its ability to recognize semantic objects within specified image regions, facilitating detailed conversations about these areas. Leveraging a dual-encoder architecture and a curated dataset called GRIT, Ferret excels in tasks requiring region-based understanding and reduces issues like object hallucination. Apple's decision to open-source Ferret fosters collaboration, innovation, and transparency in AI research, paving the way for enhanced conversational AI systems and potential integrations into Apple products like visual search in Spotlight.

Key points:

    1. Unique Multimodal Functionality: Ferret, a collaborative project between Apple and Cornell University, blends computer vision and natural language processing. It stands out by identifying objects, associating text with visual elements, and engaging in detailed conversations about images, allowing for nuanced interactions with visual content.

    2. Apple's Conversational AI Focus: Led by AI chief John Giannandrea, Apple is committed to advancing conversational AI. They've developed an internal chatbot, referred to as "Apple GPT," utilized for prototyping and responding to queries. The company is investing significantly in AI infrastructure, projected to spend over $4 billion on AI servers in 2024.

    3. Ferret's Innovation and Capabilities: Ferret's groundbreaking feature lies in its ability to recognize semantic objects within specified image regions, enabling in-depth discussions about these areas. Leveraging a dual-encoder architecture and a curated dataset called GRIT, Ferret excels in tasks requiring region-based understanding and reduces issues like object hallucination.

    4. Open-Source Initiative: Apple's decision to release Ferret as open-source marks a shift from its traditionally closed-off approach to AI research. This move fosters collaboration, innovation, and transparency in AI development, potentially leading to enhanced conversational AI systems and integration into Apple products like visual search in Spotlight.

  • Our thoughts: Apple's release of Ferret as an open-source multimodal AI model is a significant move that showcases the company's commitment to advancing AI research while embracing collaboration and transparency. The merging of computer vision and natural language processing in Ferret is a noteworthy innovation, enabling detailed interactions with visual content and potentially paving the way for more sophisticated AI applications.

    The investment in conversational AI, as evidenced by the internal chatbot development and substantial spending on AI servers, demonstrates Apple's dedication to pushing the boundaries of AI capabilities. This emphasis aligns with the industry's growing focus on enhancing AI systems for more nuanced and contextual interactions.

    Ferret's technical advancements, particularly its ability to recognize semantic objects within image regions and address issues like object hallucination, signify a step forward in multimodal AI models. The utilization of a curated dataset and dual-encoder architecture showcases a meticulous approach to improving AI capabilities.

    The decision to open-source Ferret is a strategic move that promotes collaboration and innovation within the AI research community. This step fosters the potential for rapid advancements beyond Apple's internal development, allowing for diverse applications and extensions of the model.

    Overall, Apple's release of Ferret reflects a forward-thinking approach to AI research, emphasizing technical innovation, collaboration, and the potential for broader contributions to the field beyond the confines of the company.

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AI DOJO

Custom ChatGPT and DALL-E 3
 

ChatGPT

Provide Clear Prompts: Craft clear and concise prompts to elicit specific responses. Clear questions or prompts lead to more focused and relevant answers.

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QUICK BYTES

Generative AI, since the emergence of ChatGPT, has rapidly evolved and found extensive application beyond chatbots like Google Bard, Claude, and ChatGPT. Gartner predicts a significant surge in Gen AI usage, especially in Identity and Access Management (IAM), where 90% of professionals anticipate its positive impact. IAM faces challenges due to evolving threats like MFA bypass techniques and insider risks, indicating the need for enhanced security measures.

Gen AI promises to redefine IAM in five significant ways:

1. Intelligent Access Policy Management: By analyzing real-time data, Gen AI can adapt access policies dynamically, improving administrators' ability to manage complex rules and groups effectively.

2. Curbing Insider Threats: Leveraging AI, IAM providers can enhance behavioral detection, deploy decoys, and improve security against insider threats and zombie credentials.

3. Streamlining Application Access Rights: Automating account credentials and access rights, Gen AI simplifies the onboarding and offboarding processes, providing a foundation for administrators to fine-tune settings.

4. Personalized Access Recommendations: Analyzing user behavior and responsibilities, Gen AI tailors access permissions for individual users, providing dynamic recommendations aligned with evolving roles.

5. Reducing False Positives: Integrating Gen AI into IAM enhances fraud detection capabilities, significantly reducing false alerts, and improving the efficiency of fraud detection.

While Gen AI offers promising solutions, cautious implementation is crucial due to potential biases in machine learning algorithms and privacy concerns. Human supervision remains essential to maintain the integrity and reliability of IAM processes amidst Gen AI integration.

Several Chinese startups, including Deeproute.ai, WeRide.ai, Pony.ai, and Momenta, once lauded for their ambitious pursuit of self-driving vehicles, secured substantial funding to build fleets of robotaxis. Initially, these companies thrived on the promise of widespread commercialization. However, the reality of delayed commercialization and the increasing cost pressures compelled a shift in focus towards more immediate monetization.

Unlike well-funded American counterparts like Waymo and Cruise, the Chinese robotaxi firms faced challenges in sustaining their expensive operations. To achieve profitability, they sought alternative revenue sources amid regulatory restrictions and escalating geopolitical tensions, diverting attention from full autonomy to more commercially viable smart-driving solutions.

The hurdles to profitability for robotaxis are multifaceted. Safety concerns, regulatory limitations, and the high operational costs of driverless taxis, compounded by the necessity of human supervision in current deployments, hinder their viability. Subsidized rides initially attract customers, but sustaining these services without subsidies poses a challenge.

While some executives remain optimistic, stating potential savings from eliminating human operators, trust from regulators and the public remains a significant barrier, highlighted by incidents like Cruise's suspension. Amidst these challenges, companies pivot to selling advanced driver assistance systems (ADAS) to automakers as a more immediate revenue stream. However, the revenue potential from ADAS sales appears limited compared to the scale of a successful robotaxi business.

Companies also explore partnerships with OEMs and government contracts for survival. However, challenges in establishing partnerships with OEMs and navigating government bureaucracy pose additional hurdles. Plans for IPOs in the U.S. face scrutiny from Chinese regulators due to concerns about cross-border data transfers.

As funding dwindles and losses mount, these Chinese robotaxi firms face a crucial period to validate their new monetization strategies. The next year could be pivotal for their self-driving aspirations.

GitHub introduced Copilot Chat, a programming-oriented chatbot akin to ChatGPT, initially for businesses using Copilot for Business. It later arrived in beta for individual Copilot users, and now it's officially available for all users. Integrated into Microsoft's Visual Studio and Visual Studio Code, it's accessible to Copilot paid subscribers and free for certain verified users, like teachers, students, and select open source project maintainers.

Powered by GPT-4, OpenAI's specialized AI model for development contexts, Copilot Chat allows developers to seek guidance, explanations, vulnerability checks, or code generation through natural language prompts. However, its use of publicly available data, including copyrighted or restricted content, has sparked lawsuits alleging licensing and IP violations.

Despite concerns about opt-outs for training data, GitHub's recommendation for owners to make repositories private to prevent inclusion in future training sets has drawn skepticism. Copilot's reliance on AI also raises issues of code accuracy and security, as AI-generated code might introduce bugs or deprecated elements, impacting overall security.

While GitHub stresses GPT-4's improvements and implemented filters for detecting insecure code patterns, they emphasize the importance of human review in using AI-generated code for security reasons. GitHub acknowledges the need to balance AI's assistance with human oversight for more secure software development.

While Copilot boasts substantial user numbers, its profitability remains a challenge due to high operational costs related to running AI models. Competitors like Amazon's CodeWhisperer have expanded their offerings with free tiers, professional and enterprise plans, and optimized suggestions for specific programming contexts.

Besides Amazon, other competitors like Magic, Tabnine, Codegen, Laredo, Meta's Code Llama, Hugging Face's StarCoder, and ServiceNow also vie for space in the AI-driven coding assistance market. As GitHub navigates Copilot's profitability challenges, the landscape remains competitive, with various players offering diverse features to developers.

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