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I's strength lies in its predictive prowess. Fed enough data, the conventional thinking goes, a machine learning algorithm can predict just about anything -- for example, which word will appear next in a sentence. Given that potential, it's not surprising that enterprising investment firms have looked to leverage AI to inform their decision-making. There's certainly plenty of data that one might use to train an AI-powered due diligence or investment recommendation tool, including sources like LinkedIn, PitchBook, Crunchbase, Owler and other third-party data marketplaces. With it, AI-driven financial research platforms claim to be able to predict the ability of a startup to attract investments, and there might be some truth to this.


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Hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs), since they've proven fruitful test beds for safely trying out dangerous driving scenarios. Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, since testing and gathering nuanced I-almost-crashed data usually isn't the most easy or desirable to recreate. To that end, scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) created "VISTA 2.0," a data-driven simulation engine where vehicles can learn to drive in the real world and recover from near-crash scenarios. What's more, all of the code is being open-sourced to the public. "Today, only companies have software like the type of simulation environments and capabilities of VISTA 2.0, and this software is proprietary. With this release, the research community will have access to a powerful new tool for accelerating the research and development of adaptive robust control for autonomous driving," says MIT Professor and CSAIL Director Daniela Rus, senior author on a paper about the research.


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The buzz around Artificial Intelligence (AI) has been growing steadily for years. Still, it has exploded in recent months as tech giants and startups alike have raced to develop new AI applications and capabilities. In Artificial Intelligence, a machine is given the ability to learn and work on its own, making decisions based on the data it is given. Although AI has many different definitions, in general, it can be summarized as a process of making a computer system "smart"--able to comprehend difficult tasks and execute complex commands. One of the primary reasons for AI's tremendously growing popularity is its ability to automate tasks that are time-consuming or exhausting for humans to do.


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Clearly, we need to do something about how we talk about open source and openness in general. It's been clear since at least 2006 when I rightly got smacked down for calling out Google and Yahoo! for holding back on open source. As Tim O'Reilly wrote at the time, in a cloud era of open source, "one of the motivations to share--the necessity of giving a copy of the source in order to let someone run your program--is truly gone." In fact, he went on, "Not only is it no longer required, in the case of the largest applications, it's no longer possible." That impossibility of sharing has roiled the definition of open source during the past decade, and it's now affecting the way we think about artificial intelligence (AI), as Mike Loukides recently noted.


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You can use artificial intelligence (AI) to automate complex repetitive tasks much faster than a human. AI technology can sort complex, repetitive input logically. That's why AI is used for facial recognition and self-driving cars. But this ability also paved the way for AI cybersecurity. This is especially helpful in assessing threats in complex organizations.


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#artificialintelligence

Artificial intelligence (AI) has been a much-debated topic over the past decades due to its rapid development. The hype around artificial intelligence (AI) involves a key part of computer science and can be understood as computers that display human-like behaviour based on data collected and stored historically, enabling them to recognise and use patterns in their responses. At its inception, in the 1950's, AI was very basic and consisted of computers reacting to commands. However, this encountered hurdles in its further development due to the lack of storage and wider processing capabilities. Further developments in AI took place in the 1980s, expanding the algorithmic toolkit and computers by learning through experience. For example, computers obtained the ability to play and win games against humans, such as world champions in chess.


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The long-term potential of AI to change key aspects of the way we live and to support the operation of businesses, governments, and other organizations is hard to grasp. But even today, existing and proven AI applications can potentially create value for economies and societies around the world. Indeed, AI has contributed to improvements in quality of life for all segments of society through innovations such as predictive healthcare, adaptive education, and optimized crisis response.1 The National Health Service in the United Kingdom, for instance, set up a National COVID-19 Chest Imaging Database containing a shared library of chest X-rays, CT scans, and MRI images to support the testing and development of AI technologies to treat COVID-19 and a variety of other health conditions.2 Businesses have seen increased productivity and operational efficiency through the use of autonomous robotics in manufacturing, AI-optimized supply chains, and intelligent cargo routing with autonomous vehicles, among other initiatives.


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Software engineer Blake Lemoine worked with Google's Ethical AI team on Language Model for Dialog Applications (LaMDA), examining the large language model for bias on topics such as sexual orientation, gender, identity, ethnicity, and religion Over the course of several months, Lemoine, who identifies as a Christian mystic, hypothesized that LaMDA was a living being, based on his spiritual beliefs. Lemoine published transcripts of his conversations with LaMDA and blogs about AI ethics surrounding LaMDA. In June, Google put Lemoine on administrative leave; last week, he was fired. In a statement, Google said Lemoine's claims that LaMDA is sentient are "wholly unfounded." "It's regrettable that despite lengthy engagement on this topic, Blake still chose to persistently violate clear employment and data security policies that include the need to safeguard product information," Google said in a statement.


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Artificial intelligence is rapidly upending how people do business across industries, and yet skeptics still abound. But is there really a reason to fear AI? AI will change how we work and do business, and its impact is already being felt. Still, that doesn't mean it is something to fear. On the contrary, business managers and leaders who embrace AI and harness its potential now have everything to gain. According to IBM, at its most basic, AI is anything that "leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind."


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Patients are 20% less likely to die of sepsis because a new AI system catches symptoms hours earlier than traditional methods, new research shows. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," says Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins University, and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."