Artificial Intelligence used to only exist in movies, in science fiction, but over the past decade AI has been replacing jobs, according to CNBC 30 percent of jobs risk being taken over by AI. Recently, one of the biggest tech guys of the industry Elon Musk stated that "AI will make jobs pointless." This has hence led us to wonder what jobs will be replaced by AI in the near future? This has also led us to worry whether our own jobs will be replaced. Therefore, I have compiled a list of jobs that will be replaced by AI in the near future, and also a list of jobs that probably won't be replaced.
The most well-funded US artificial intelligence startup is Nuro, with just over $1B in disclosed equity funding, including a $940M Series B from SoftBank in February 2019. The California-based startup is developing autonomous vehicles, with a focus on last-mile delivery. Nuro is followed by New York's UiPath ($1B in disclosed equity funding) and Illinois' Avant ($655M). There are 9 unicorn startups on our map: robotic process automation vendor UiPath ($7.1B valuation), autonomous vehicles software provider Argo AI ($7B), agtech startup Indigo Agriculture ($3.5B), Nuro ($2.7B), alternative lending startup Avant ($1.9B), AI-powered predictive sales platform InsideSales.com The startup with the least funding on the list is Rhode Island's The Innovation Scout, a SaaS platform that connects enterprises with startups, accelerators, and more.
Shares of AMD, which will report earnings next week, rose 1% to a record high. Nvidia shares were also up 1%. Intel's stock was up 8.6% at $68.75, a level it has not seen since the peak of the dotcom boom in 2000, propelling the broader Nasdaq and the Philadelphia SE Semiconductor Index to record highs. Other major chipmakers such as Taiwan Semiconductor Manufacturing Co Ltd (TSMC) and Texas Instruments have also given upbeat forecasts this month, cementing hopes of a rebound in the market that fell nearly 12% in 2019, according to research firm Gartner. However, Intel has struggled with delays in its 10nm chip technology, losing its lead to rival TSMC in the race to supply to the "new data economy", which includes 5G, autonomous vehicles and artificial intelligence.
With 2020 predictions looming, there's sure to be a fresh wave of hype around the edge and 5G. Now's an ideal time to solidify and update your understanding, and explore how they'll complement each other. If you're processing payments, taking online orders, detecting fraud, in the financial services industry, or exploring machine learning, these two technologies can help keep you competitive in the coming months. Edge computing is all about processing information from devices closer to where it's being created, rather than shuttling it back and forth from the cloud. Together with 5G, computing at the edge paves the way for applications that wouldn't have been possible before.
AI/Edge Vastai Technologies is using Arteris IP's FlexNoC Interconnect IP and AI Package for its Artificial Intelligence Chips for artificial intelligence and computer vision systems-on-chip (SoCs). Startup Vastai Technologies was founded in December 2018, designs ASICs and software platforms for computer vision and AI applications, such as smart city, smart surveillance, smart education, according to a press release. Smart city connections will be dominated by video surveillance and smart utility metering, says ABI Research in a report, predicting that by 2026, 87% of the smart city market will be those two device types. Low-latency 5G connections and embedded AI in video surveillance systems are some of the enabling technologies. Internet of Things The smart building market will generate over $2 billion in revenue by 2026 for software and services, says ABI Research, thanks to some new emerging applications.
The vehicles still drive with a safety driver and a software operator. Optimus Ride, an MIT spinoff, has started operating its autonomous vehicles at Paradise Valley Estates in Fairfield, California. The shuttles, which have been carrying passengers for a couple of months now, follow deployments at the Seaport District in Boston, the Halley Rise mixed-use district in Reston, Virginia, and the Brooklyn Navy Yard in New York, a 300-acre industrial park. At the moment, the vehicles still drive with two people from the company on board, a safety driver and a software operator, but the goal of the company is to be fully driverless later this year. We caught up with the company recently -- check out the video below.
This week, Alphabet CEO Sundar Pichai and IBM CEO Ginni Rometty called for AI to get its own regulation system. Alphabet CEO Pichai stated that it was "too important not to", going on to expand by explaining that sectors within AI technology, such as autonomous cars and healthtech, needed their own sets of rules. IBM CEO Rometty joined the discussion with the idea of'precision regulation', stating that it is not the technology itself, but how it is used that should be regulated, using facial recognition as an example of technology that can harm people's privacy as well as having its benefits, such as catching criminals. Asheesh Mehra, co-founder and CEO at AntWorks, explains why regulating AI is important. Without it, the technology won't take the world by storm These announcements have come in spite of recent setbacks in the sphere; just last week it was revealed that the European Commission were considering a five year ban on facial recognition, and Google's last attempt to assemble an AI ethics board lasted under two weeks due to controversy over who was appointed.
Uber's self-driving cars will soon be jockeying for space on the streets of Washington, DC, with the ride-hailing company announcing it will begin collecting data to support the development of its fleet of autonomous vehicles. The vehicles will not be operating in autonomous mode, though. They will instead be operated by human drivers to start out, collecting mapping data and capturing driving scenarios which Uber's engineers will then reproduce in simulation. That said, the company hopes to eventually allow its self-driving cars in Washington to, well, self-drive. "Our hope is that this first round of manually driven data collection will lay the foundation for testing our vehicles in self-driving mode in Washington, DC," the company's Advanced Technologies Group said in a Medium post.
Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximators to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to overfitting due to the presence of many parameters within the network to learn. Can we have another type of neural network that can learn complex non-linear relationship but with fewer parameters and hence prone to overfitting?. Convolution Neural Network (CNN) is another type of neural network that can be used to enable machines to visualize things and perform tasks such as image classification, image recognition, object detection, instance segmentation etc…are some of the most common areas where CNN's are used. In this article, we will explore the workings of the Convolution Neural Network in-depth.