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What Quantum Computing Will Mean for the Future Artificial Intelligence

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Today's artificial intelligence (AI) systems are only as good as the data they're trained on. The AI industry is currently taking advantage of large datasets to train AI models and make them more useful. However, as these datasets are becoming limited, researchers are exploring other ways to improve AI algorithms. One such way is quantum computing. It is a new frontier of computer science that will enable better AI algorithms shortly.


AI Will Speak the Animal Language! Translate Your Words to Your Dogs!

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A piece of good news for all animal lovers-- you can get a chance to talk to your lovable pets in the nearby future. The impossible thought can be possible with the integration of cutting-edge technologies such as artificial intelligence or AI. The development of AI models or the machine learning animal translator can speak the animal language to communicate with various animals. Researchers are looking forward to leveraging a machine learning animal translator for humans to understand animal language efficiently and effectively. The focus is to introduce these AI models to the entire animal kingdom to decode animal language and have a better understanding of the animals.


Introducing BANMo: From Cat Pictures to Deformable 3D Models

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I explain Artificial Intelligence terms and news to non-experts. Here's BANMo, a NeRF-inspired approach shared at the CVPR 2022 event I attended a few weeks ago. BANMo takes pictures to create deformable 3D models. If you are in VFX, game development, or creating 3D scenes, this new AI model is for you. I wouldn't be surprised to see this model or similar approaches in your creation pipeline very shortly, allowing you to spend much less time, money, and effort on making 3D models.


10 top artificial intelligence (AI) solutions in 2022

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Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Among the many drivers of the tech ecosystem's rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of "advanced analysis and logic-based techniques" to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries. There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today.


Unlocking the hidden value of dark data

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IT leaders seeking to derive business value from the data their companies collect face myriad challenges. Perhaps the least understood is the lost opportunity of not making good on data that is created, and often stored, but seldom otherwise interacted with. This so-called "dark data," named after the dark matter of physics, is information routinely collected in the course of doing business: It's generated by employees, customers, and business processes. It's generated as log files by machines, applications, and security systems. It's documents that must be saved for compliance purposes, and sensitive data that should never be saved, but still is.


Five Rules for Fixing AI in Business

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I've never been a gambler. Outcomes are determined more on luck than skill and that just makes me queasy. Sometimes, I feel like companies view #AI the same way, like they are hedging their bets and maybe even expecting defeat. Today I want you to weigh in. Why do you think that, according to a study by @BCG (Boston Consulting Group), only one in 10 companies have found success with AI? Seems like we should have better odds than that.


Five Rules for Fixing Artificial Intelligence in Business

#artificialintelligence

Artificial intelligence (AI) provides thorough data analysis, automates business processes, and engages with customers and employees. The adoption of AI has been particularly widespread in the business world. From workflow management to trend predictions, AI has numerous use cases. Sometimes, I feel like companies view AI the same way, like they are hedging their bets and maybe even expecting defeat. Today I want you to weigh in.


AI: The Tool, Not the Movie

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"The development of full artificial intelligence (AI) could spell the end of the human race. It would take off on its own and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded." Now, I love Stephen Hawking and his way of thinking. Here is a person who seems able to look around corners to predict the future. And I just don't buy this statement.


Foundation Models: AI's Next Frontier

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Modern-day artificial intelligence (AI) centers on learning from data -- and the more data there is, the better it learns. That's why, until now, AI research and application has been largely focused on training bigger AI models on more data by using highly efficient computational resources. But while significant progress has been made in this area, many application areas -- such as healthcare and the manufacturing industry -- have limited data available, which has limited its applicability in these areas. Foundation models could be the solution to this. The term "foundation models" refers to a general purpose behind an AI model.


New AI technology integrates multiple data types to predict cancer outcomes

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While it's long been understood that predicting outcomes in patients with cancer requires considering many factors, such as patient history, genes and disease pathology, clinicians struggle with integrating this information to make decisions about patient care. A new study from researchers from the Mahmood Lab at Brigham and Women's Hospital reveals a proof-of-concept model that uses artificial intelligence (AI) to combine multiple types of data from different sources to predict patient outcomes for 14 different types of cancer. Results are published in Cancer Cell. Experts depend on several sources of data, like genomic sequencing, pathology, and patient history, to diagnose and prognosticate different types of cancer. While existing technology enables them to use this information to predict outcomes, manually integrating data from different sources is challenging and experts often find themselves making subjective assessments.