Wrap the table component in React.memo or useMemo — The table would still need to call useContext to access the side dra

From the heartbreaking loneliness epidemic during the pandemic, to the rapid growth of everyday social media influencers, to the rise of white nationalist and unwavering Trump supporters, I’ve realized how many people want something to belong to and to believe in.

Artificial Intelligence has had a crazy couple of year and 2020 has been like no other. With a pandemic, a global recession along with incredible gains in AI, there’s simply so much to keep an eye on.

I studied my MPhil in Machine Learning in 2015 and back then, AI was in relative infancy but it was actually the perfect time to see the foundation of the industry that was to form.

Note: Since function components run the entire function body on re-render, whenever the cell or open state updates (and causes a re-render), sideDrawerRef always has the latest value in .current.

This doesn’t mean you should go ahead and use this pattern for everything you build, though. It’s best used when you need to access or update another component’s state at specific times, but your component doesn’t depend or render based on that state. React’s core concepts of lifting state up and one-way data flow are enough to cover most app architectures anyway.

Memoize the react-data-grid component used to render the table — This would have introduced more verbosity to our code. We also found it prevented necessary re-renders, requiring us to spend more time fixing or restructuring our code entirely just to implement the side drawer.

Agreeably, not much has changed, but the number of challenges that AI has smashed is startling. In what follows, I’ll briefly go over a few topics that have really set the tone for this year, but are also important topics for the coming years.

Deep Learning has been around for what feels like a while but in reality, it’s only the past 5 years that it exploded with interest because of the gains that Deep Mind made. Pre-2015, yes it was interesting, but there wasn’t that element of explosive interest.

This doesn’t mean you should go ahead and use this pattern for everything you build, though. It’s best used when you need to access or update another component’s state at specific times, but your component doesn’t depend or render based on that state. React’s core concepts of lifting state up and one-way data flow are enough to cover most app architectures anyway.

We asked designers and friends of the UX Collective: what was your biggest lesson from 2020, that we can use as we go into 2021? Share your lessons with us: [email protected]

Pre 2015, the theory was pretty sophisticated but the active day-to-day implementation of AI or Machine Learning was quite old. Highly sophisticated algorithms looked a bit more like Kalman Filters, Support Vector Machines, and maybe even a Random Forest.

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wards black women where race and gender both play roles in bias. The term was coined by queer black feminist Moya Bailey, who created the term to address misogyny directed toward black women in American visual and popular culture. Trudy of Gradient Lair, a womanist blog about black women and art, media, social media, socio-politics and culture, has also been credited in developing the lexical definition of the term.

We asked designers and friends of the UX Collective: what was your biggest lesson from 2020, that we can use as we go into 2021? Share your lessons with us: [email protected]

Deep Learning is interesting because it doesn’t really have a pure statistical foundation, but rather, a layered functional form. A Deep Neural Network is built in a lattice like structure, where through an optimisation process, the weights (located in each adjoining lattice connection) will be optimised for the input to match the output. By adding more layers, you increase the expressivity here, but also more complicated connections (like LSTM or Gates) and also complicated net forms (e.g. Encoders) can produce amazing results in their specific domains.

Deep Learning essentially emulates the concepts reflected in your brain. Given some input data (and the black box of your brain), a neural network will break down the input and be able to make sense of it. With enough training data, it’ll be able to infer something from it.