August 23, 2023

August 22, 2023

August 24, 2023

  • Chips

    Nvidia Revenue Doubles on Demand for A.I. Chips, and Could Go Higher

    The New York Times, 08/23/23. Nvidia, the Silicon Valley tech company, has predicted rapid growth in the demand for its graphics processing units (GPUs) used to build artificial intelligence systems. The heavy demand from cloud computing services and other customers has caused a significant increase in revenue and profit for Nvidia, surpassing market expectations. This strong performance is expected to reignite enthusiasm for tech stocks on Wall Street. However, Nvidia is struggling to supply enough chips to meet the demand, creating opportunities for other major chip companies and start-ups. The company is working to increase its chip supply throughout this year and the next. READ THE ARTICLE

  • Management

    Leadership In The Age Of AI: Leveraging Intelligent Automation

    Forbes, 08/23/23. In the era of artificial intelligence (AI), leaders must recognize its potential for leveraging data and optimizing business operations. It is essential to prioritize responsible AI leadership, considering ethical implications and safeguarding societal interests. Leaders should also be self-aware and committed to social responsibility, guiding their decisions to foster societal progress. Simulation and policy planning can help minimize adverse consequences, and leaders should understand that AI will augment rather than replace jobs. By strategically utilizing AI in the workplace and investing in the key forces driving intelligent automation, visionary leaders can drive meaningful change and sustained business growth. READ THE ARTICLE

  • Publishing

    Stephen King: My Books Were Used to Train AI

    The Atlantic, 08/23/23. Artificial intelligence is now being used to coach writing, but can a machine truly learn to write? As an author, I have always believed that reading is essential for developing writing skills. AI programmers have taken this advice to heart, using vast amounts of literature to train their algorithms. However, the results so far have been underwhelming. AI-generated poems and stories lack the depth and creativity that comes from human experience and imagination. While we cannot discount the possibility of AI becoming sentient and creative in the future, for now, the machine’s output is no match for genuine human storytelling. READ THE ARTICLE

  • Labelling

    “Nutrition labels” aim to boost trust in AI

    Axios, 08/23/23. The importance of education cannot be overstated. Education empowers individuals to unlock their full potential, preparing them for a successful future. It provides knowledge, critical thinking skills, and opportunities for personal growth. Education also plays a pivotal role in shaping society, promoting equality, and fostering a sense of community. It is through education that individuals can acquire the tools necessary to achieve their dreams, contribute to their communities, and make a positive impact on the world. Therefore, investing in education is investing in a better future for all. READ THE ARTICLE

  • Open Source

    How Important Is Open Source to AI Adoption?

    TheNewStack, 08/23/23. Open source is important to the future of AI and LLMs as it provides a solution to concerns about ownership of AI and large language model (LLM) datasets. According to a survey, 40% of respondents believe open source is the solution to AI ownership concerns. Additionally, a study found a reluctance to rely on commercial LLMs in production, with many organizations using them only for experimentation. While there has been some stagnation in the growth of traditional AI projects, open source contributions from developers involved in AI and ML remain critical. The Python and Apache communities, particularly PyTorch and TensorFlow, receive the most contributions. Despite confusion about what makes a community vendor-dominated, open source frameworks like PyTorch and TensorFlow are widely adopted among data science and ML specialists. The use of dedicated GPUs by AI/ML developers is preferred, and as the demand for computing power increases, specialized chips are expected to be relied upon more heavily. Challenges preventing the use of LLMs in production include giving up access to proprietary data and the complexity of fine-tuning. READ THE ARTICLE