The article discusses the continuing debate around return to office mandates post-pandemic. It states that despite investments in office spaces and some companies requiring RTO, employee resistance remains high. A contrarian perspective presented is that there may be an undersupply rather than oversupply of offices in years ahead. As pandemic fears subside, the demand for collaborative workspaces could increase, especially from younger workers.
Additionally, the article argues that the office versus remote binary debate is overly simplistic. Companies need to consider that the future is likely …
“And it isn’t just work. If AI turns poor employees into good ones, it might do the same for other fields. I suspect that it will have a major effect on entrepreneurship, giving every entrepreneur a personalized cofounder to fill in the gaps in their skills. This suggests a potential for an expansion of entrepreneurship, especially given that GPT-4 is widely available for free around the world, helping potential founders far from the usual technology hubs.” —Everyone is above average - by Ethan Mollick
The key insights from this article are that AI is being increasingly integrated into HR processes to streamline operations and enhance productivity. However, AI is viewed as a complementary tool rather than a substitute for human professionals. While AI can handle many tasks, human intelligence is still necessary for decision-making and providing a human touch. AI has advantages in talent acquisition, employee engagement, predictive analytics for HR decisions, and performance management. However, AI has limitations in emotional intelligence, empathy, and contextual understanding. read more >
“Fabrizio Dell’Acqua shows why relying too much on AI can backfire. In an experiment, he found that recruiters who used high-quality AI became lazy, careless, and less skilled in their own judgment. They missed out on some brilliant applicants and made worse decisions than recruiters who used low-quality AI or no AI at all. When the AI is very good, humans have no reason to work hard and pay attention. They let the AI take over, instead of using it as a tool.” —Centaurs and Cyborgs on the Jagged Frontier
The key insights from this article are that the future of work will involve a heavy dose of AI, and companies need to cultivate an AI-ready workforce. AI can be viewed as an ally that augments human potential. Companies should leverage AI to identify emerging skills gaps, build talent transformation programs, and personalize employee engagement. Agility in talent strategies is crucial, and companies should establish AI-driven talent acquisition programs and upskilling initiatives. AI ethics should be embedded in operations to eliminate bias and counteract job displacement. read more >
The key insights from this article are that artificial intelligence (AI) is playing a significant role in enterprise digital transformation by revolutionizing operations, improving customer experiences, and unlocking growth opportunities. AI-powered automation can streamline processes and improve efficiency, while data-driven insights enable informed decision-making. AI also enables personalized customer experiences and enhances security and risk management. However, there are challenges to consider, such as data quality and privacy, skill gaps, change management, and integration complexity. … read more >
The article discusses a study on the reliability and robustness of code generated by large language models (LLMs) for Java coding questions. The study evaluated four code-capable LLMs, including GPT-3.5 and GPT-4 from OpenAI, and found that they exhibited high rates of API misuse. The study also highlighted the importance of assessing code reliability beyond semantic correctness and emphasized the need for static analysis to ensure full coverage. Llama 2, an open model, performed the best with a failure rate of less than one percent. read more >
The key insights from this article are that OpenAI has released fine-tuning for the ChatGPT 3.5 Turbo model, which allows for customization to specific tasks and improves durability and reliability of the output. Fine-tuning also enables the shortening of prompts, saving time and cutting costs. The article provides a step-by-step guide on how to fine-tune the ChatGPT 3.5 Turbo model, including formatting data into JSON, gathering examples, uploading examples, creating a fine-tuning job, and using the fine-tuned model. The cost of fine-tuning is divided into training cost and usage cost. read more >
The key insights from these article sections are: - OpenAI has made changes to large language models (LLMs) like GPT3 to make them more responsive to human interaction, allowing them to be used as powerful, general-purpose information-processing tools. - These changes enable AI models like GPT3.5 and GPT4 to be used in a programming manner, using new data instead of training, making AI more of a general-purpose tool. - Generative AI, like GPT4, is probabilistic and predicts the probability of words and phrases based on input, making it flexible but also unpredictable. - Training LLMs involves … read more >
“The answer is to extend and customize a model to make it smart about your own business. While hosted models like ChatGPT have gotten most of the attention, there is a long and growing list of LLMs that enterprises can download, customize, and use behind the firewall — including open-source models like StarCoder from Hugging Face and StableLM from Stability AI."—How to minimize data risk for generative AI and LLMs in the enterprise | VentureBeat
