Blackholes are a breakdown in the equations of spacetime. This means both space and time no longer behave the way we would expect of them.
Today we explore the breakdown in time around blackholes and what it means to interact with the event horizon, or the place where time appears to stand still.
Further Reading/Consumption:
Do you know what the Earth’s atmosphere is made of? You’d probably remember it’s oxygen, and maybe nitrogen. And with a little help from Google you can easily reach a more precise answer: 78% nitrogen, 21% oxygen and 1% Argon gas. However, when it comes to the composition of exo-atmospheres—the atmospheres of planets outside our solar system—the answer is not known. This is a shame, as atmospheres can indicate the nature of planets, and whether they can host life.
As exoplanets are so far away, it has proven extremely difficult to probe their atmospheres. Research suggests that artificial intelligence (AI) may be our best bet to explore them—but only if we can show that these algorithms think in reliable, scientific ways, rather than cheating the system. Now our new paper, published in The Astrophysical Journal, has provided reassuring insight into their mysterious logic.
Astronomers typically exploit the transit method to investigate exoplanets, which involves measuring dips in light from a star as a planet passes in front of it. If an atmosphere is present on the planet, it can absorb a very tiny bit of light, too. By observing this event at different wavelengths—colors of light—the fingerprints of molecules can be seen in the absorbed starlight, forming recognizable patterns in what we call a spectrum. A typical signal produced by the atmosphere of a Jupiter-sized planet only reduces the stellar light by ~0.01% if the star is Sun-like. Earth-sized planets produce 10–100 times lower signals. It’s a bit like spotting the eye color of a cat from an aircraft.
AI doesn’t actually multitask very well because typical algorithms aren’t very versatile. But a new project from Google could change that.
A New York City law requires algorithms used in hiring to be “audited” for bias. It’s the first in the US—and part of a larger push toward regulation.
At its re: Invent 2021 conference today, Amazon announced Graviton3, the next generation of its custom ARM-based chip for AI inferencing applications. Soon to be available in Amazon Web Services (AWS) C7g instances, the company says that the processors are optimized for workloads including high-performance compute, batch processing, media encoding, scientific modeling, ad serving, and distributed analytics.
Alongside Graviton3, Amazon unveiled Trn1, a new instance for training deep learning models in the cloud — including models for apps like image recognition, natural language processing, fraud detection, and forecasting. It’s powered by Trainium, an Amazon-designed chip that the company last year claimed would offer the most teraflops of any machine learning instance in the cloud. (A teraflop translates to a chip being able to process 1 trillion calculations per second.)
As companies face pandemic headwinds including worker shortages and supply chain disruptions, they’re increasingly turning to AI for efficiency gains. According to a recent Algorithmia survey, 50% of enterprises plan to spend more on AI and machine learning in 2021, with 20% saying they will be “significantly” increasing their budgets for AI and ML. AI adoption is, in turn, driving cloud growth — a trend of which Amazon is acutely aware, hence the continued investments in technologies like Graviton3 and Trn1.
Our hunter-gatherer ancestors are huddled around a campfire when they suddenly hear the nearby bushes rustling. They have two options: investigate if the movement was caused by small prey such as a rabbit, or flee, assuming there was a predator such as a saber-tooth tiger. The former could lead to a nutritious meal, while the latter could ensure survival. What call do you think our ancestors would have made?
Evolution ensured the survival of those who fled the scene on the margin of safety rather than those who made the best decision by analyzing all possible scenarios. For thousands of years, humans have made snap decisions in fight-or-flight situations. In many ways, the human race learned to survive by jumping to conclusions.
Continue reading “Are You Guilty Of These 3 Cognitive Biases In Decision Making?” »
Summary: A new AI algorithm recognizes the complex range of emotions invoked when people listen to pieces of music.
Source: UPF Barcelona.
Music has been of great importance throughout human history, and emotions have always been the ultimate reason for all musical creations. When writing a song a composer tries to express a particular feeling, causing concert-goers to perhaps laugh, cry or even shiver.
With the increasing demand for data science approaches and cognitive technologies across all industries, organizations are learning how to successfully implement and manage newer, more intelligent tools and systems. What are the challenges that enterprises encounter when adopting AI and ML models for their organizations, and how can teams work to overcome these obstacles?
At an upcoming Data for AI event, Anil Kumar, Executive Director — Head of AI Industrialization at Verizon will be sharing in particular the ways that Verizon has leveraged AI to overcome some of their key challenges. This past January, the Machine Learning Lifecycle 2021 Conference featured Radha Sankaran, Executive Director of Algorithmic Customer Experiences at Verizon Wireless, where she shared some insight into the current state of AI usage and its challenges, techniques, and impacts. At the upcoming Data for AI virtual event, Anil Kumar, also from Verizon Wireless, will be speaking more on his experiences.
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How will future AI systems make the most ethical choices for all of us?
Artificial intelligence is already making decisions in the fields of business, health care, and manufacturing. But AI algorithms generally still get help from people applying checks and making the final call.
What would happen if AI systems had to make independent decisions and ones that could mean life or death for humans?
Continue reading “Worried about AI ethics? Worry about developers’ ethics first” »
The AI algorithm is more efficient in distinguishing false positives from the real stuff than human experts.
A new artificial intelligence algorithm has discovered over 300 previously unknown exoplanets in data gathered by a now-defunct exoplanet-hunting telescope.
The Kepler Space Telescope, NASA’s first dedicated exoplanet hunter, has observed hundreds of thousands of stars in the search for potentially habitable worlds outside our solar system. The calatog of potential planets it had compiled continues generating new discoveries even after the telescope’s demise. Human experts analyze the data for signs of exoplanets. But a new algorithm called ExoMiner can now mimic that procedure and scour the catalog faster and more efficiently.
