A funny thing happened in Hong Kong earlier this month. Well, funny unless you were there. The annual ApeFest, where collectors of Bored Ape NFTs (remember them?) took place in Hong Kong (for the uninitiated, NFTs, or non-fungible tokens, can be linked to products like digital artworks and traded for cryptocurrencies on the open market). Nouveau-riche investors who got rich off the back of the revolutionary technology and investment products came together to party. A number of them reported suffering from "eye burn, extreme pain and impaired vision after attending one of its events, which was lit by UV lights".

But an accuracy assessment from a lab goes only so far. It says nothing of how the AI will perform in the chaos of a real-world environment, and this is what the Google Health team wanted to find out. Over several months they observed nurses conducting eye scans and interviewed them about their experiences using the new system. When it worked well, the AI did speed things up. But it sometimes failed to give a result at all. Like most image recognition systems, the deep-learning model had been trained on high-quality scans; to ensure accuracy, it was designed to reject images that fell below a certain threshold of quality.

But an accuracy assessment from a lab goes only so far. It says nothing of how the AI will perform in the chaos of a real-world environment, and this is what the Google Health team wanted to find out. Over several months they observed nurses conducting eye scans and interviewed them about their experiences using the new system. When it worked well, the AI did speed things up. But it sometimes failed to give a result at all.

Although the future is exciting, it's also unknown. Dell Technologies' Realizing 2030 report found that 85 percent of jobs that will exist in 2030 haven't yet been thought up. Despite fears around the unknown, research from McKinsey reports that, on the whole, job growth will outpace job loss. Researchers found that while 15 percent of the global workforce -- 400 million workers -- could be displaced by automation by 2030, this will be offset by the jobs gained. In the same period, labor demand is predicted to grow by up to 33 percent of the global workforce -- the equivalent of 890 million new jobs.

Yes, it is indeed one of the most burning paradoxes of modern times. Do you really have to fear the fact that artificial intelligence (AI) will snatch away your only way to livelihood? Well, both history and facts says a different story. The way I see it, here is humanity's best shot to dedicate all mundane and repetitive tasks to bots while we can engage ourselves into something more creative that would further the cause of the business. If you are a pizza delivery boy in London, you don't have to worry for a second about your job loss when you see, the self-driving robots doing the delivery.

Whether you are working as Data Scientist or looking to build a career in a Data Science, the pipeline of your work include Extracting dataset, loading dataset, Data Cleansing and munging, finding summary statistics, then do some Exploratory Data analysis (EDA), and after all these things build a model using machine learning. Anscombe Quartet dataset demonstration is one example that shows us, depending only on summary statistics can be troublesome and how badly it can affect our machine learning model. Here for this post, we are going to use Anscombe-quartet data set which is stored as an excel file and we can read it using the pd.read_excel(). It's a group of four subsets that appear to be similar when using typical summary statistics, but when you plot all the groups using the Matplotlib package, you'll see a different story. Each dataset consists of eleven (x,y) pairs as follows: We have labelled four pairs as (X, Y),(X.1,

A link has been posted to your Facebook feed. Chatbots now work well for ordering a pizza, but managing a complex travel itinerary is a different story. Ask any technology expert about the future of artificial intelligence (AI) in travel and they'll breathlessly tell you we're on the verge of a revolution. They'll describe a world in the not-too-distant future where smart applications can find and book a bargain airfare, manage your trip and troubleshoot any problems that might come up with greater speed and efficiency than any human travel agent. But ask any traveler to describe their experience with AI, and you might hear a different story: One of struggling to be understood by technology that claims to be smart. These early days of travel bots that specialize in customer service, chat, messaging and search are a cautionary tale.