A frustration for physicians and primary care providers alike is trying to balance listening to a patient and properly documenting the medical visit. This documentation is essential to making sure all the data is captured properly and that the patient was heard and treated correctly. Ravi Atreya and Pedro Teixeira, who met when they were in a joint M.D./Ph.D. program at Vanderbilt University, set out to solve the problem and eventually founded PredictionHealth, which uses artificial intelligence (AI) to help health providers complete documentation. On this episode of the Tennessee Voices podcast, the pair talked about their passion for medicine, computer science and data and how AI can be "game changing." The purpose behind their startup was helping create effective communication channels and reducing the frustration doctors and nurses may face at times with technology.
Artificial intelligence (AI) is motivating the automation of processes and services, being recently used as a way to interact directly with customers in frontline services (Belanche et al., 2020a). AI constitutes a major source of innovation (Huang and Rust, 2018), with a potential for disruption particularly high in services (Bock et al., 2020). As a result, there is an increasing interest in implementing automated forms of interaction in services (Paluch et al., 2020; Flavián et al., 2021), and this trend is not different in the tourism, leisure and hospitality industry. The use of AI and autonomous robots to perform different tasks in this context is continuously increasing (Ivanov and Webster, 2019; Tussyadiah, 2020; Belanche et al., 2020b), which is reshaping the service and affecting experiences and relationships with customers. In addition, service automation may have a great impact on customer choices (Van Doorn et al., 2017) and behaviors (Grewal et al., 2017).
Who goes to a demonstration with a high-powered assault weapon? He planned on bringing his high-powered assault weapon to that demonstration with every intention of using it. He claims it was self-defense -- it was not. He was out for blood, his goal was to shoot and kill as many as possible and claim it was self-defense. Absolutely relieved that the jury in this case based their deliberations and ultimate not guilty verdict on the facts and didn't cower to the obvious intended intimidation of BLM, Antifa, the left-leaning liberal loonies, and last but not least, the lying fake media.
As exciting as all this might seem, this decision seems to be more of an aberration than the rule. Before it was finally granted a patent in South Africa, the DABUS application had been rejected by patent offices in the US, Europe and the UK. The European Patent Office (EPO), justifying its decision to reject the patent application, pointed out that the law designates a natural person as the inventor of a work in order to preserve her moral right over the invention as well as to secure for her the economic rights made available by the patent. In order to be entitled to these benefits, an inventor needs to have actually "performed the creative act of invention". While artificial intelligence algorithms today are capable of perform complex computational functions that are often way beyond the capability of humans, the EPO pointed out that in all these instances, the programs are doing little more than just following the broad instructions of the humans who designed them.
About one year ago, British newspaper The Guardian ran an article titled A robot wrote this entire article. Are you scared yet, human?, written by an Artificial Intelligence (AI)-enabled robot called GPT-3 (Generative Pre-trained Transformer 3). It is an autoregressive language model that uses deep learning to produce human-like text. GPT-3 was fed a short introduction and was instructed to write an op-ed of around 500 words in simple language, focusing on why humans have nothing to fear from AI. In response, it produced eight different essays. The Guardian picked the best parts of each and ran the edited piece.
Modern security information and event management and intrusion detection systems leverage ML to correlate network features, identify patterns in data and highlight anomalies corresponding to attacks. Security researchers spend many hours understanding these attacks and trying to classify them into known kinds like port sweep, password guess, teardrop, etc. However, due to the constantly changing attack landscape and the emergence of advanced persistent threats (APTs), hackers are continuously finding new ways to attack systems. A static list of classification of attacks will not be able to adapt to new and novel tactics adopted by adversaries. Also, due to the constant flow of alarms generated by multiple sources in the network, it becomes difficult to distinguish and prioritize particular types of attacks--the classic alarm flooding problem.
In the first instalment of this new video series, Stephen José Hanson talks to Michael I Jordan, about AI as an engineering discipline, what people call AI, so-called autonomous cars, and more. To provide some background to this discussion, in 2018, Jordan published an essay on Medium entitled Artificial intelligence -- the revolution hasn't happened yet, in which he argues that we need to tone down the hype surrounding AI and develop the field as a human-centric engineering discipline. He adds further commentary on this topic in an interview published this year in IEEE spectrum, (Stop calling everything AI). Hanson wrote a rebuttal to the Medium article, AI: Nope, the revolution is here and this time it is the real thing, and the pair discuss the theme in more detail in this video discussion below. There is also a full transcript of the discussion below. This interchange was recorded on June 15th 2021. HANSON: Hi Michael, good to see you! So let's get into this. Let me just state what I think you said and you tell me where I'm wrong, if I am. So it appears to me that you're basically talking about that AI should arise from an engineering discipline that with start from well-defined science like chemistry and chemical engineering and this would allow the insights from the science to migrate their way into an engineering domain which had principles of design and control and risk management and many other good statistical quality control ideas that basically made AI the valuable and useful and have some utility and something actually went to calculate about the AI I actually being useful as opposed to the number hidden units it has…. JORDAN: Just to slow you down a little bit there, I mean historically I think the good points of reference or things like the development of chemical engineering or electrical engineering were that there was an existing science and understanding and there was an appetite to build real-world systems that have huge implications for human life. So chemical factories didn't exist initially, but when they started to exist, I don't think it was that the science was all worked out and they kind of applied it and it just happened.
Eight years after Kennedy's initial so-called moonshot challenge, two American astronauts took the famous "one giant leap for mankind"--walking on the Moon for the first time. However, the bigger impact of that moonshot was the resultant building of a massive military and industrial innovation complex that propelled the US to the top of the industrial economy. The industrial economy, in simple terms, grew by selling excess production from one place to another by connected pathways. By 1975, the industrial economy was at its peak, with more than 1 billion places connected by rail, road and airways dominated by multinational companies born in the US, selling technology developed during the moonshot. The other spinoff of this moonshot was ARPANET, kicked off in 1966, which eventually became the internet. The internet gave rise to the knowledge economy driven by connecting people via the world wide web.
A key part of any solution should involve revolutionizing data collection systems. We often don't have the data we need to build unbiased AI, whether it is the lack of uniformity in electronic health records, state-by-state variation in criminal sentence reporting, or missing data in the 2020 Census. The solution will require increased funding to agencies that gather data in underrepresented communities, outreach to community organizations to gain local buy-in, and democratization of data so that citizen scientists can highlight disparities and identify ongoing gaps.
I have been asked this question a few times so I have decided to write this post about the current services of Livepeer and the future implementations Livepeer could bring to the decentralized world. This may be helpful for new comers that want to learn about Livepeer and have an idea of where Livepeer could take us in the future. But first we need to cover some important topics of where we are right now. First, let's look at the difference between Ethereum Mining and Video Mining. Livepeer has many possible services it could provide.