Since the dawn of the iPhone, many of the smarts in smartphones have come from elsewhere: the corporate computers known as the cloud. Mobile apps sent user data cloudward for useful tasks like transcribing speech or suggesting message replies. Now Apple and Google say smartphones are smart enough to do some crucial and sensitive machine learning tasks like those on their own. At Apple's WWDC event this month, the company said its virtual assistant Siri will transcribe speech without tapping the cloud in some languages on recent and future iPhones and iPads. During its own I/O developer event last month, Google said the latest version of its Android operating system has a feature dedicated to secure, on-device processing of sensitive data, called the Private Compute Core.
Apple has announced a new feature called Live Text, which will digitize the text in all your photos. This unlocks a slew of handy functions, from turning handwritten notes into emails and messages to searching your camera roll for receipts or recipes you've photographed. This is certainly not a new feature for smartphones, and we've seen companies like Samsung and Google offer similar tools in the past. But Apple's implementation does look typically smooth. With Live Text, for example, you can tap on the text in any photo in your camera roll or viewfinder and immediately take action from it.
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Podcasts are excellent for keeping up with the dope in your field and also familiarise with the processes/people behind the scenes. Following are five of the podcasts which I personally think are the best (in no particular order) by virtue of their content and quality of guests/hosts. This is hosted by Lukas Biewald who is heavy on startup creds -- he is the founder & CEO of Weights & Biases, a company that builds developer tools for ML. He also founded Figure Eight, an AI/ML company that was sold for $300 million. Lukas is known for prying out details on technology and engineering practices at organisations his guests are associated with.
Hello, today I'd like to explain briefly how we use artificial intelligence to count sunflower seeds in a photo taken with a mobile device. Agenda: 1. Business needs 2. Data preparation 3. Model structure 4. Used libs and tools 5. Results 6. Error analysis 7. Fails/Hypotheses 8. Conclusion 9. References Fortunately for me, I am working at Kernel. Where I am developing Computer Vision (CV) and other models to solve business problems and challenges. One of them is to count seeds on sunflower.
Artificial intelligence (AI) and machine learning breakthroughs have paved the way for today's mobile applications. Apps that use AI can now recognize speech, images, and gestures, as well as translate voices with high accuracy. With so many apps reaching the app stores, it's critical that they stand out from the crowd by satisfying escalating consumer expectations. As a result, the way we engage with our mobile devices is changing. Our smartphones and tablets are now powerful enough to run real-time learning and reaction software. This has paved the way for several interesting applications.
While Daft Punk may have sadly split, machine-created music may be about to skyrocket in popularity. Not only are artificial intelligence neural networks now capable of creating original melodies, but scientists are also developing robots capable of playing – and improvising – live music. So, will AI and androids soon top the charts? And could they even replace human musicians entirely? On this week's episode of the Science Focus Podcast, Prof Nick Bryan-Kinns, director of the Media and Arts Technology Centre at Queen Mary University of London, joins staff writer Thomas Ling to explain groundbreaking new music technology.
Many believe the company that enables real intelligence on end devices (such as mobile and IoT devices) will define the future of computing. Racing toward this goal, many companies, whether tech giants such as Google, Microsoft, Amazon, Apple and Facebook, or startups spent tens of billions of dollars each year on R&D. Assuming hardware is the major constraint for enabling real-time mobile intelligence, more companies dedicate their main efforts to developing specialized hardware accelerators for machine learning and inference. Billions of dollars have been spent to fuel this intelligent hardware race. This article challenges the view.