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  • Workers are putting AI skills on resumes as demand heats up

Workers are putting AI skills on resumes as demand heats up

Also: How Blockchain Can Solve the Music Industry's AI Problem


As AI continues to shape industries and redefine job roles, the race to arm oneself with relevant skills is more fierce than ever. A recent surge in LinkedIn profiles touting AI skills illustrates this trend, but as with any gold rush, there's skepticism around genuine expertise versus mere buzzwords. Meanwhile, Stanford researchers push the envelope in blending AI and human learning to code, and the music industry grapples with the challenges and opportunities AI brings. In healthcare, there's a growing realization that while AI holds transformative potential, its successful implementation requires a measured and strategic approach. Stay with us as we explore these developments and more, ensuring you stay abreast of the latest in AI innovations and implications.


  • ๐Ÿ‘ฉ๐Ÿผโ€๐Ÿ’ผ Workers are putting AI skills on resumes as demand heats up

  • ๐Ÿง‘๐Ÿฝโ€๐Ÿ’ป New Tool Helps AI and Humans Learn To Code Better

  • ๐ŸŽน How Blockchain Can Solve the Music Industry's AI Problem

  • ๐Ÿฅ Beyond Hype: Getting the Most Out of Generative AI in Healthcare Today

The surge in demand for AI-related roles has caused a dramatic increase in workers listing AI skills on their LinkedIn profiles. Over the past five years, the number of LinkedIn members in the U.S. holding "Head of AI" positions has shot up by 264%. Before the launch of ChatGPT in 2022, only 7.7% of users claimed to have AI expertise; this rose to 13% in just seven months. Terms such as "Prompt Engineering" and "Generative Artificial Intelligence" have seen a surge in use on profiles. Singapore leads globally in AI skills growth, with self-reported skills up 20-fold since 2016. Notably, while the demand for AI skills has surged, so has the demand for soft skills like flexibility and social perceptiveness. However, there's some skepticism regarding the accuracy of these self-reported skills, and experts emphasize the significance of genuine skills over merely owning sophisticated tech.

Stanford researchers have introduced a new framework, Parsel, that enables both humans and large language models (LLMs) to improve their coding abilities. Conceived by Noah D. Goodman and Eric Zelikman, Parsel encourages the decomposition of challenging coding tasks into more manageable parts, mirroring human problem-solving behavior. Initially designed as an educational tool, the system lets students describe desired program behaviors in plain English, with Parsel converting this into the appropriate syntax. When applied to LLMs, Parsel drastically enhanced their performance, as they often struggle with algorithmic reasoning despite their proficiency in syntax generation. This innovative approach ensures more reliable multi-part code generation, aligning more closely with human programming logic. Beyond education, the team foresees Parsel broadening its impact in future coding advancements.

The music industry, which previously faced significant challenges from peer-to-peer file-sharing platforms like Napster, now confronts new complexities with the rise of AI in music creation. AI's potential to generate music brings up questions of copyright and licensing, as models may use human creations as a base. To address these concerns, blockchain's transparent and immutable qualities present a solution. By using tokenization, particularly non-fungible tokens (NFTs), ownership of music and rights distribution becomes clearer and more traceable. Smart contracts on the blockchain can handle royalty payments securely and transparently, ensuring artists receive due compensation, even from AI-generated content that uses their original works. Several musicians are already leveraging AI for creativity, and some even propose sharing profits from AI-generated models of their voices. By integrating these new technologies judiciously, the music industry aims to create a beneficial ecosystem for all stakeholders.

The healthcare industry is grappling with financial strains and rising operational costs, with over half of US hospitals ending 2022 in the negative. Despite these challenges, there's increasing enthusiasm around the potential of generative AI, particularly its role in healthcare applications. Cost-effective and powerful, AI tools, particularly large language models (LLMs), can help review medical records, diagnose, and suggest treatments. Surveys indicate that while 75% of health executives acknowledge the transformative potential of generative AI, only 6% have a clear strategy in place. The rapid advancements in generative AI come with hurdles, such as resource constraints, lack of expertise, and regulatory challenges. Successful healthcare systems are implementing pragmatic strategies: beginning with low-risk applications, choosing between buying, partnering, or building technology, reinvesting cost savings, and ensuring AI aligns with larger organizational goals. Starting with focused, small-scale projects can yield immediate benefits and lay the foundation for future expansive endeavors in the AI domain.

๐Ÿ› ๏ธ AI tools updates

Leveraging GPT-4 for content moderation offers a more efficient and streamlined approach compared to traditional methods. Traditionally, human moderators, burdened by the vast amount of online content and its accompanying emotional toll, grappled with the slow and potentially inconsistent application of evolving content policies. GPT-4 provides a solution by understanding and applying detailed policy guidelines rapidly, reducing policy update cycles from months to hours. By iteratively comparing GPT-4's judgments with human experts, policies can be refined for better consistency. Not only does this system promote more uniform content labeling, but it also relieves human moderators from continuous exposure to potentially harmful content. Although GPT-4's labeling quality is comparable to lightly-trained human moderators, well-trained moderators still outperform it. As technology progresses, enhancements like chain-of-thought reasoning and self-critique are being explored. Despite the advancements, human oversight remains essential to address biases and complex policy intricacies, ensuring a holistic and responsible content moderation approach.

Microsoft Azure ChatGPT enables organizations to operate ChatGPT within their own network, enhancing the workflow by offering functionalities like code correction. Released as open-source on GitHub with options for private Azure hosting, integration is relatively straightforward for existing Azure users. This enterprise solution mirrors the user experience of the public ChatGPT but in a more private and controlled setting, keeping network traffic restricted to the company's network and offering advanced security measures. Furthermore, Azure ChatGPT brings the advantages of data privacy, control over network traffic, and the ability to deliver enhanced business value by easily integrating with internal data sources and services like ServiceNow.

๐Ÿ’ต Venture Capital updates

In 2023, the enthusiasm for AIstartups seems to be waning among seed investors. A.I. seed startups, previously commanding high valuations, are facing skepticism as large tech players, like Adobe, introduce competitive products that overshadow newcomers. While investment values in A.I. startups have decreased significantly over the summer, there's also growing concern about their genuine uniqueness and competitive advantage, especially since many rely heavily on open-source models like those from OpenAI. As a result, investors are predicted to exercise more caution in the coming months, carefully examining startups' distinctiveness and potential for long-term success. The overarching question for A.I. startups remains their defensibility in a market where much of the technology is similar or open-source.

Deckmatch, an innovative startup, has secured โ‚ฌ1 million ($1.1 million) in funding to transform the preliminary filtering of pitch decks using AI, intending to streamline the process for venture capital (VC) firms. Traditionally, VC associates have been responsible for sifting through countless pitch decks to identify those aligning with a VC's investment thesis. Deckmatch plans to automate this process by converting unstructured data from pitch decks into structured data that can match VC preferences. Beyond just analyzing pitch decks, Deckmatch aims to aggregate additional data, making more informed decisions about potential investments. Currently in closed beta with 60 VCs, the company aspires to extend its services to industries like recruitment and procurement. Deckmatch's long-term vision is to cultivate a data-driven decision-making approach in VC and similar sectors, emphasizing the importance of personal relationships and creativity. The latest funding was led by Alliance VC, with participation from Skyfall Ventures and various angel investors.

๐Ÿซก Meme of the day

โญ๏ธ Generative AI image of the day