Space travel, exploration, and observation involve some of the most complex and dangerous scientific and technical operations ever carried out. This means that it tends to throw up the kinds of problems that artificial intelligence (AI) is proving itself to be outstandingly helpful with.
Because of this, astronauts, scientists, and others whose job it is to chart and explore the final frontier are increasingly turning to machine learning (ML) to tackle the everyday and extraordinary challenges they face.
AI is revolutionizing space exploration, from autonomous spaceflight to planetary exploration and charting the cosmos. ML algorithms help astronauts and scientists navigate and study space, avoid hazards, and classify features of celestial bodies.
Quantum computing promises to be a revolutionary tool, making short work of equations that classical computers would struggle to ever complete. Yet the workhorse of the quantum device, known as a qubit, is a delicate object prone to collapsing.
Keeping enough qubits in their ideal state long enough for computations has so far proved a challenge.
In a new experiment, scientists were able to keep a qubit in that state for twice as long as normal. Along the way, they demonstrated the practicality of quantum error correction (QEC), a process that keeps quantum information intact for longer by introducing room for redundancy and error removal.
A quantum computational solution for engineering materials. Researchers at Argonne explore the possibility of solving the electronic structures of complex molecules using a quantum computer. If you know the atoms that compose a particular molecule or solid material, the interactions between those atoms can be determined computationally, by solving quantum mechanical equations — at least, if the molecule is small and simple. However, solving these equations, critical for fields from materials engineering to drug design, requires a prohibitively long computational time for complex molecules and materials.
Models are scientific models, theories, hypotheses, formulas, equations, naïve models based on personal experiences, superstitions (!), and traditional computer programs. In a Reductionist paradigm, these Models are created by humans, ostensibly by scientists, and are then used, ostensibly by engineers, to solve real-world problems. Model creation and Model use both require that these humans Understand the problem domain, the problem at hand, the previously known shared Models available, and how to design and use Models. A Ph.D. degree could be seen as a formal license to create new Models[2]. Mathematics can be seen as a discipline for Model manipulation.
But now — by avoiding the use of human made Models and switching to Holistic Methods — data scientists, programmers, and others do not themselves have to Understand the problems they are given. They are no longer asked to provide a computer program or to otherwise solve a problem in a traditional Reductionist or scientific way. Holistic Systems like DNNs can provide solutions to many problems by first learning about the domain from data and solved examples, and then, in production, to match new situations to this gathered experience. These matches are guesses, but with sufficient learning the results can be highly reliable.
We will initially use computer-based Holistic Methods to solve individual and specific problems, such as self-driving cars. Over time, increasing numbers of Artificial Understanders will be able to provide immediate answers — guesses — to wider and wider ranges of problems. We can expect to see cellphone apps with such good command of language that it feels like talking to a competent co-worker. Voice will become the preferred way to interact with our personal AIs.
Memes made with AI video generator algorithms are suddenly everywhere. Their sudden proliferation may herald an imminent explosion in the technology’s capability.
This video will cover the philosophy of artificial intelligence, the branch of philosophy that explores what artificial intelligence specifically is, and other philosophical questions surrounding it like; Can a machine act intelligently? Is the human brain essentially a computer? Can a machine be alive like a human is? Can it have a mind and consciousness? Can we build A.I. and align it with our values and ethics? If so, what ethical systems do we choose?
We’re going to be covering all those equations and possible answers to them in what will hopefully be an easy-to-understand, 101-style manner.
== Subscribe for more videos like this on YouTube and turn on the notification bell to get more videos: https://tinyurl.com/thinkingdeeply ==
0:00 Introduction. 0:45 What is Artificial Intelligence? 1:13 Rene Descartes. 2:11 Alan Turing & the ‘Turing Test’ 3:42 A.I.M.A. & A.I. 4:45 Intelligent Agents. 5:40 Newell’s Definition. 6:26 Weak A.I. vs Strong A.I. 7:31 Narrow A.I. vs General A.I. vs Super Intelligence. 10:00 Computationalism. 10:44 Approaches to A.I. 13:32 Can a Machine Have Consciousness? 14:23 The ‘Chinese Room’ 16:30 Critical Responses. 17:18 The ‘Hard Problem of Consciousness’ 18:47 Philosophical Zombies. 21:20 New Questions in the Philosophy of A.I. 21:34 Singularitarianism. 24:40 A.I. Alignment. 26:45 The Orthogonality Thesis. 27:36 The Ethics of A.I. 30:56 Conclusion.
Descartes, 1,637, R., in Haldane, E. and Ross, G.R.T., translators, 1911, The Philosophical Works of Descartes, Volume 1, Cambridge, UK: Cambridge University Press.
Russell, S. & Norvig, P., 2009, Artificial Intelligence: A Modern Approach 3rd edition, Saddle River, NJ: Prentice Hall.
The team was able to produce blur-free, high-resolution images of the universe by incorporating this AI algorithm.
Before reaching ground-based telescopes, cosmic light interacts with the Earth’s atmosphere. That’s why, the majority of advanced ground-based telescopes are located at high altitudes on Earth, where the atmosphere is thinner. The Earth’s changing atmosphere often obscures the view of the universe.
The atmosphere obstructs certain wavelengths as well as distorts the light coming from great distances. This interference may interfere with the accurate construction of space images, which is critical for unraveling the mysteries of the universe. The produced blurry images may obscure the shapes of astronomical objects and cause measurement errors.
Are you ready for the future of #ai? In this video, we will showcase the top 10 AI-tools to watch out for in 2023. From advanced machine learning algorithms to cutting-edge deep learning #technologies, these tools will revolutionize the way we work, learn, and interact with the world. Join us as we explore the #innovative capabilities of these AI-tools and discover how they can boost your productivity, streamline your operations, and enhance your decision-making process. Don’t miss out on this exciting glimpse into the future of artificial intelligence!
This special edition show is sponsored by Numerai, please visit them here with our sponsor link (we would really appreciate it) http://numer.ai/mlst.
Prof. Karl Friston recently proposed a vision of artificial intelligence that goes beyond machines and algorithms, and embraces humans and nature as part of a cyber-physical ecosystem of intelligence. This vision is based on the principle of active inference, which states that intelligent systems can learn from their observations and act on their environment to reduce uncertainty and achieve their goals. This leads to a formal account of collective intelligence that rests on shared narratives and goals.
To realize this vision, Friston suggests developing a shared hyper-spatial modelling language and transaction protocol, as well as novel methods for measuring and optimizing collective intelligence. This could harness the power of artificial intelligence for the common good, without compromising human dignity or autonomy. It also challenges us to rethink our relationship with technology, nature, and each other, and invites us to join a global community of sense-makers who are curious about the world and eager to improve it.
TOC: Intro [00:00:00] Numerai (Sponsor segment) [00:07:10] Designing Ecosystems of Intelligence from First Principles (Friston et al) [00:09:48] Information / Infosphere and human agency [00:18:30] Intelligence [00:31:38] Reductionism [00:39:36] Universalism [00:44:46] Emergence [00:54:23] Markov blankets [01:02:11] Whole part relationships / structure learning [01:22:33] Enactivism [01:29:23] Knowledge and Language [01:43:53] ChatGPT [01:50:56] Ethics (is-ought) [02:07:55] Can people be evil? [02:35:06] Ethics in Al, subjectiveness [02:39:05] Final thoughts [02:57:00]
The use of time-lapse monitoring in IVF does not result in more pregnancies or shorten the time it takes to get pregnant. This new method, which promises to “identify the most viable embryos,” is more expensive than the classic approach. Research from Amsterdam UMC, published today in The Lancet, shows that time-lapse monitoring does not improve clinical results.
Patients undergoing an IVF treatment often have several usable embryos. The laboratory then makes a choice as to which embryo will be transferred into the uterus. Crucial to this decision is the cell division pattern in the first three to five days of embryo development. In order to observe this, embryos must be removed from the incubator daily to be checked under a microscope. In time-lapse incubators, however, built-in cameras record the development of each embryo. This way embryos no longer need to be removed from the stable environment of the incubator and a computer algorithm calculates which embryo has shown the most optimal growth pattern.
More and more IVF centers, across the world, use time-lapse monitoring for the evaluation and selection of embryos. Prospective parents are often promised that time-lapse monitoring will increase their chance of becoming pregnant. Despite frequent use of this relatively expensive method, there are hardly any large clinical studies evaluating the added value of time-lapse monitoring for IVF treatments.
