This study demonstrates the extent to which prominent debates about the future of AI are best understood as subjective, philosophical disagreements over the history and future of technological change rather than as objective, material disagreements over the technologies themselves. It focuses on the deep disagreements over whether artificial general intelligence (AGI) will prove transformative for human society; a question that is analytically prior to that of whether this transformative effect will help or harm humanity. The study begins by distinguishing two fundamental camps in this debate. The first of these can be identified as "transformationalists," who argue that continued AI development will inevitably have a profound effect on society. Opposed to them are "skeptics," a more eclectic group united by their disbelief that AI can or will live up to such high expectations. Each camp admits further "strong" and "weak" variants depending on their tolerance for epistemic risk. These stylized contrasts help to identify a set of fundamental questions that shape the camps' respective interpretations of the future of AI. Three questions in particular are focused on: the possibility of non-biological intelligence, the appropriate time frame of technological predictions, and the assumed trajectory of technological development. In highlighting these specific points of non-technical disagreement, this study demonstrates the wide range of different arguments used to justify either the transformationalist or skeptical position. At the same time, it highlights the strong argumentative burden of the transformationalist position, the way that belief in this position creates competitive pressures to achieve first-mover advantage, and the need to widen the concept of "expertise" in debates surrounding the future development of AI.

While language technologies have advanced significantly, current approaches fail to address the complex sociocultural dimensions of linguistic preservation. AI Thinking proposes a meaning-centered framework that would transform technological development from creating tools FOR communities to co-creating solutions WITH them. This approach recognizes that meaningful solutions emerge through the interplay of cultural understanding, community agency, and technological innovation. The proposal articulates a holistic methodology and a five-layer technological ecosystem where communities maintain control over their linguistic and cultural knowledge representation. This systematic integration of community needs, cultural preservation, and advanced capabilities could revolutionize how we approach linguistic diversity preservation in the digital age.

In an exclusive interview with TIME, San Francisco Federal Reserve president and chief executive Mary Daly said that the explosion of artificial intelligence (AI) could improve the labor market in the long-term and make workers more productive, even as workers fear the rising technology will change or eliminate their jobs. "Jobs are being created, as well as jobs being replaced," Daly said of AI. "If we can get people to upskill or reskill to take the jobs that are being created, we'll have a very successful and growing economy. But that's the burden on us--to make sure that everyone can participate in this changing technological development." TIME sat down with Daly at the Aspen Ideas Festival on June 28 to discuss the nation's monetary policy, a potential softening in the labor market, the role of AI in the workforce, and more. This interview has been condensed and edited for clarity. TIME: Tell me a bit about your role as a Federal Reserve Bank president.

When legal regulations get ahead of technological developments, the progress of society may be constrained. Conversely, when technological developments run ahead of legal regulations, unregulated new technologies may harm society, defying technological development's fundamental purpose. This is exactly what has happened in the world in the past decade, as technological developments have far outpaced legal regulations. Worse, traditional legal frameworks focus on the relationship between people, whereas we must develop a legal framework to regulate relations between people and intelligent machines in the current era. Integrating AI technologies into human society imposes unique legal challenges without any precedence.

Artificial intelligence is about to have its "iPhone moment" and could revolutionize everything, according to Bank of America. In a Tuesday note to clients, BofA strategists listed four reasons why AI is about to change the landscape: democratization of data, unprecedented mass adoption, "warp-speed" technological development, and abundant commercial uses. "We are at a defining moment - like the internet in the '90s - where Artificial Intelligence (AI) is moving towards mass adoption, with large language models like ChatGPT finally enabling us to fully capitalize on the data revolution," they said. Up until recently, AI could read and write but couldn't understand content, BofA said. Tools like ChatGPT have changed that, however, and its ability to understand natural language has opened the door to huge upside.

At its most basic, AI refers to the development of computer systems that can perform tasks that would typically require human intelligence, such as recognizing speech, making decisions, and solving problems. There are many different types of AI, including machine learning, deep learning, and natural language processing, all of which are used to create more advanced and sophisticated AI systems. One of the most well-known applications of AI is virtual personal assistants, such as Apple's Siri and Amazon's Alexa. These systems use natural language processing to understand and respond to voice commands, allowing users to control their smart homes, make phone calls, and access information with just their voice. Another key area of AI development is self-driving cars, which use a combination of computer vision, machine learning, and other technologies to navigate roads and avoid obstacles.

Because of the considerable heterogeneity and complexity of the technological landscape, building accurate models to forecast is a challenging endeavor. Due to their high prevalence in many complex systems, S-curves are a popular forecasting approach in previous work. However, their forecasting performance has not been directly compared to other technology forecasting approaches. Additionally, recent developments in time series forecasting that claim to improve forecasting accuracy are yet to be applied to technological development data. This work addresses both research gaps by comparing the forecasting performance of S-curves to a baseline and by developing an autencoder approach that employs recent advances in machine learning and time series forecasting. S-curves forecasts largely exhibit a mean average percentage error (MAPE) comparable to a simple ARIMA baseline. However, for a minority of emerging technologies, the MAPE increases by two magnitudes. Our autoencoder approach improves the MAPE by 13.5% on average over the second-best result. It forecasts established technologies with the same accuracy as the other approaches. However, it is especially strong at forecasting emerging technologies with a mean MAPE 18% lower than the next best result. Our results imply that a simple ARIMA model is preferable over the S-curve for technology forecasting. Practitioners looking for more accurate forecasts should opt for the presented autoencoder approach.

Spanning from Silicon Valley to Upper Manhattan and everywhere in between, it seems this country is overflowing with tech-optimists. As you know -- a techno-optimist is someone who is generally optimistic about the current state of technology and its potential future. These people believe that technological developments will do more good for humanity than harm -- and that our technological future is very bright. These Techno-Optimists may believe that technology has the power to solve major crises, like global climate change, or believe that machine learning and AI will enable us to reach incredible new heights in humanity. They also tend to envision a technologically rich future, with science fiction-inspired gadgets and capabilities in the hands of average people.

While AI is an important technological development, you can also have some fun with it. So, here are ten fun AI tools you should check out. Job automation, algorithmic bias, and technological development are the first thoughts that spring to mind when we think of Artificial Intelligence. But at the same time, AI can be used in many fun and exciting ways. Here, we discuss ten fun AI tools that you must try out.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! When we think about AI and voice recognition, we typically think of one of two suboptimal scenarios. The first is your Amazon Alexa sitting at home, possibly eavesdropping on your everyday conversations and feeding advertising algorithms so you buy the right kind of lawn mower. The second scenario is clunky transcription software, auto-subtitling our videos and TV shows, often to inaccurate (and humorous) effect. In reality, though, there are some deeply exciting developments happening in the AI voice recognition space right now.