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How to Choose a Major for Artificial Intelligence: Degree Research Guide


Artificial intelligence (AI) offers plenty of opportunities in the job market, as many AI companies try to solve real-world problems through this field of practice. AI's growth also comes with a wide range of options available to find the best majors for artificial intelligence. When it comes to what degree in artificial intelligence should you pursue, keep reading to learn how to choose a major for artificial intelligence and know the possible AI career paths that are open to you after graduating. A career in artificial intelligence provides tech professionals with competitive pay, job security, and continuous learning and development. The Bureau of Labor Statistics (BLS) reports that the average annual salary for computer and AI professionals is $126,830.

Dyson has been secretly building robots – TechCrunch


You probably know Dyson for their vacuums, or maybe that too-powerful dryer you've tentatively stuck your hands in while waiting for a flight. Maybe you've seen their hair dryers or fans. Dyson is a company that develops premium products that move air around. Recently, the firm expanded with a very strange face-mounted air purifier. While showcasing the product, Dyson took pains to highlight some of the research behind it. It offered a peek into their labs, providing some insight into a future of products that expand well beyond premium vacuum cleaners.

Dyson's been secretly working on robots that do household chores


Dyson has been getting into more and more offbeat products these days, like the Zone noise-canceling headphones that blow purified air at your face. Now, the company has revealed that it has an entire division that's secretly been developing robot prototypes that do household chores. The company didn't detail any of the models in particularly, but many look like regular robot arms adapted to do specialized home chores like cleaning and tidying. One appeared to be designed to vacuum out the seat cushions, mapping an armchair out in detail to do the job. "So this means I'll never, ever find crisps around the back of my sofa again?" the company's chief engineer, Jake Dyson, asked a researcher in a video (below).

Dyson reveals its big bet … robots

The Guardian

Dyson has signalled it is placing a "big bet" on producing robots capable of household chores by 2030, as it looks to move beyond the vacuum cleaners, fans and dryers that made its founder one of the wealthiest British businessmen. The company, founded by billionaire Sir James Dyson, on Wednesday published photographs of robot arms being used in household settings, including cleaning furniture, a claw picking up plates, and a hand-like machine picking up a teddy bear. While those may not sound like major achievements, robots still struggle with many actions that represent simple tasks for humans, such as grasping fragile objects or dealing with unfamiliar obstacles. Solving those and other problems could create new markets for the company. Dyson wants to build the UK's largest robotics research centre at its Hullavington Airfield site, close to its design centre in Malmesbury, Wiltshire.

Learning Spark: Lightning-Fast Data Analytics: Damji, Jules S., Wenig, Brooke, Das, Tathagata, Lee, Denny: 9781492050049: Books


Most developers who grapple with big data are data engineers, data scientists, or machine learning engineers. This book is aimed at those professionals who are looking to use Spark to scale their applications to handle massive amounts of data. In particular, data engineers will learn how to use Spark's Structured APIs to perform complex data exploration and analysis on both batch and streaming data; use Spark SQL for interactive queries; use Spark's built-in and external data sources to read, refine, and write data in different file formats as part of their extract, transform, and load (ETL) tasks; and build reliable data lakes with Spark and the open source Delta Lake table format. For data scientists and machine learning engineers, Spark's MLlib library offers many common algorithms to build distributed machine learning models. We will cover how to build pipelines with MLlib, best practices for distributed machine learning, how to use Spark to scale single-node models, and how to manage and deploy these models using the open source library MLflow.

What can we learn from a new documentary on Elon Musk?

The Guardian

You could be forgiven for believing that we've already achieved the era of autonomous vehicles. Tesla, the electric car manufacturer run by Elon Musk, refers to a version of its Autopilot software as "Full Self Driving". The company released a (misleadingly edited) video of an autonomous vehicle navigating city streets, its drivers' hands on their lap – a style replicated by enthusiasts. Musk has repeatedly assured in speeches and interviews that autonomous vehicles were one to two years away – or, as he put it in 2015, a "solved problem" because "we know what to do and we'll be there in a few years." But the existing Autopilot technology has not yet realized those promises and, as a new New York Times documentary illustrates, the gap in expectation and reality has led to several deadly crashes.

The Engineer - AI tool tracks plastic waste from space


Developed by Minderoo Foundation, the'Global Plastic Watch' tool uses advanced satellite data technology and machine learning to create a near-real-time, high resolution map of plastic pollution. The tool aims to help authorities better manage plastic leakage into the marine environment, and is said to provide the largest ever open source dataset of plastic waste across dozens of countries. Global Plastic Watch uses remote sensing satellite imagery from the European Space Agency and a novel machine learning model created in collaboration with digital product agency Earthrise Media. The tool can determine the size and scale of land-based plastic waste sites, which fuel the growing issue of plastic pollution in the world's rivers and oceans. By using the data, governments, industry and communities can evaluate and monitor the risk of land-based plastic waste sites as well as prioritise investment in solutions, Minderoo Foundation said.

Data Engineer


Well, from a technical point of view, we leverage the power of a global crowd to provide some of the world's biggest companies with the high-quality data they need to power their artificial intelligence. We're instrumental to the progression and development of artificial intelligence and we couldn't be prouder or more inspired to be involved in an industry that is changing the world. We bond over our shared love of software engineering, data science, and strong coffee. We like online gaming, running marathons, and team drinks. We celebrate authenticity and diversity and we're invested in what we do.

Will DeepMind's AlphaCode Replace Programmers? - KDnuggets


The Alphabet subsidiary DeepMind has done it again, and this time, they are testing the boundaries of AI in software development sectors. DeepMind's AlphaCode was tested against human performance on coding challenges and achieved rank among the top 54% of human coders on Codeforces. This is a remarkable achievement as it is one of its kind. There are other code generation machine learning models, such as OpenAI Codex, but none of them tried to compete with human programmers. A coding challenge is like solving puzzles. To solve these challenges, an individual must have an understanding of logic, math, and programming skills.