Tech giants and beyond are set to spend over $1tn on AI capex in coming years, with so far little to show for it. So, will this large spend ever pay off? MIT’s Daron Acemoglu and GS’ Jim Covello are skeptical, with Acemoglu seeing only limited US economic upside from AI over the next decade and Covello arguing that the technology isn’t designed to solve the complex problems that would justify the costs, which may not decline as many expect. But GS’ Joseph Briggs, Kash Rangan, and Eric Sheridan remain more optimistic about AI’s economic potential and its ability to ultimately generate returns beyond the current “picks and shovels” phase, even if AI’s “killer application” has yet to emerge. And even if it does, we explore whether the current chips shortage (with GS’ Toshiya Hari) and looming power shortage (with Cloverleaf Infrastructure’s Brian Janous) will constrain AI growth. But despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst.
ChatGPT has not been exposed yet to new problems and solutions. It lacks the critical thinking skills of a human and can only address problems it has previously encountered. This could explain why it is so much better at addressing older coding problems than newer ones.
“ChatGPT may generate incorrect code because it does not understand the meaning of algorithm problems, thus, simple error feedback information is not enough,”
This study explores the adverse effects of a Bitcoin mining facility on the health and environment of Granbury, Texas. Residents report significant disturbances due to noise pollution, resulting in sever vascular circulatory issues, migraines, sleep issues, and a decrease in local wildlife. Despite efforts to mitigate noise through sound barriers, the community continues to experience reduced quality of life. The rapid expansion of Bitcoin mining in Texas, driven by favorable political conditions, underscores the need for regulatory measures to balance economic benefits with community well-being.
Spectral sensitivity of eye is influenced by light intensity. And the light intensity determines the level of activity of cones cell and rod cell. This is the main characteristic of human vision. Sensitivity to individual colors, in other words, wavelengths of the light spectrum, is explained by the RGB (red-green-blue) theory. This theory assumed that there are three kinds of cones. It’s selectively sensitive to red (700-630 nm), green (560-500 nm), and blue (490-450 nm) light. And their mutual interaction allow to perceive all colors of the spectrum.
