What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Technology is now evolving at such a rapid pace that annual predictions of trends can seem out-of-date before they even go live as a published blog post or article. As technology evolves, it enables even faster change and progress, causing an acceleration of the rate of change, until eventually, it will become exponential. Technology-based careers don't change at the same speed, but they do evolve, and the savvy IT professional recognizes that his or her role will not stay the same. And an IT worker of the 21st century will constantly be learning (out of necessity if not desire). What does this mean for you?
Silicon Valley headlines often report on the size of venture capital raised by a startup -- the bigger the funding, the bigger the story. But this is a poor way to understand the startup community. Startup success isn't determined by how much you raise; it's about how much you keep. Arena.im is a great example. It recently raised a seed round of $2.3 million -- a tiny amount by local standards.
Specialized replicated compute accelerators (RCA) are multiplied up by having multiple copies per ASICs, multiple ASICs per server, multiple servers per rack, and multiple racks per datacenter. Server controller can be an FPGA, microcontroller, or a Xeon processor. Power delivery and cooling system are customized based on ASIC needs. If required, there would be DRAMs on the PCB as well. Each ASIC interconnects its RCAs using a customized on-chip network.
In both the consumer and business worlds, technology is constantly and rapidly evolving. Unique and innovative new business, health and consumer technologies are emerging every day, but sometimes it takes a little time for the "next big thing" to get recognized and catch on. Google, for instance, launched the original iteration of G-Suite back in 2006--long before the cloud computing and real-time collaboration became the standard. As leaders in the tech field, the members of Forbes Technology Council are always on the lookout for emerging devices, programs and systems that could revolutionize their industry--even if the tech is still in its early phases. We asked a group of them to share the most impressive piece of tech from the last three years that most people aren't aware of yet.
The graph represents a network of 4,023 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 14 May 2020 at 04:33 UTC. The requested start date was Thursday, 14 May 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 1-hour, 46-minute period from Tuesday, 12 May 2020 at 04:35 UTC to Wednesday, 13 May 2020 at 06:22 UTC.
The graph represents a network of 2,273 Twitter users whose tweets in the requested range contained "TopCyberNews", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 17 February 2020 at 18:20 UTC. The requested start date was Monday, 17 February 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 4-day, 1-hour, 45-minute period from Monday, 10 February 2020 at 23:14 UTC to Saturday, 15 February 2020 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Many financial institutions are rapidly developing and adopting AI models. They're using the models to achieve new competitive advantages such as being able to make faster and more successful underwriting decisions. However, AI models introduce new risks. In a previous post, I describe why AI models increase risk exposure compared to the more traditional, rule-based models that have been in use for decades. In short, if AI models have been trained on biased data, lack explainability, or perform inadequately, they can expose organizations to as much as seven-figure losses or fines.
Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).