For many years artificial intelligence wasn’t producing many breakthroughs. However, in recent times AI has finally produced major success stories. AI has already changed the world and its impact won’t slow down anytime soon. AI is set to dramatically transform the global economy and to disrupt lifestyles around the world. Here’s a short list of ten major changes that are expected to come soon because of the AI revolution.
1. Increased Economic Productivity
AI machines are better at certain skills than humans and can get certain tasks done faster. This means that the productivity of labor, business and entire countries is expected to increase with the AI revolution. Given the slow growth rates of major countries in recent years, this is good news.
2. Increased Unemployment and Declining Pay
Because AI is set to automate certain skills, companies are expected to increasingly automate many positions leaving many workers with less work, less pay or even full unemployment.
3. More Time for what Humans are Good At
AI is expected to give humans more time for what we are good at; social leadership and creative skills are not expected to be automated. AI should free up our time for these types of skills.
4. Higher Levels of Inequality
There are significant gaps between different nations, companies and industries when it comes to AI readiness. Companies are expected to make a lot more money while hiring and paying workers less. All of these factors are expected to increase economic inequality between individuals and between countries.
5. Increased Predictive Maintenance
AI is good at making predictions. This means that tasks like medical diagnostics will increasingly be done by AI, while doctors will have more time for human interactions with patients.
6. Increased AI Related Jobs
More jobs than ever before will be using AI. For many occupations, only certain tasks will be automated. So, if you’re trying to prepare for the coming years of career life, you should start working together with AI.
7. More Partially Automated Occupations
According to Mckinsey & Co. research, about 30 percent of activities in 60 percent of all occupations could be automated; in contrast, in 5 percent of occupations nearly all activities are automatable. This means that working hours might be fewer with remaining hours being spent collaborating with AI.
8. Increased Security
AI drones will increasingly help us deal with emergency situations by delivering care packages or emergency responses to those in need.
9. Better Healthcare
AI is already better than human doctors at diagnosing certain illnesses; this means that AI should set up humanity for a healthier future.
10. Routine Jobs and Data Maximization Jobs are expected to be Automated
AI is good at routine tasks with stable and predictable environments. It is also good at using data for maximizing results. Jobs involving these tasks are expected to be automated soon.
Hearing is one of the five senses which technology has successfully aided. Hearing aids are common and successful technologies. Eyeglasses designed to help people to see well have existed since the Middle Ages. Recently, an artificial tongue has been developed by the University of Glasgow. So far, our sense of smell has been harder to enhance. However, a tech start-up called Cyrano Sciences is commercializing an electronic nose designed not just to assist human noses but also to assist sniffer dogs who are used for security purposes.
A New Zealand wine dealer has partnered with an artificial intelligence company called Spacetime in order to match wine drinkers with wines they would probably love. American companies IBM and McCormick & Company are trying to use artificial intelligence to learn about people’s taste preferences. There are many philosophical questions about how these new technologies might best be used. One concern is that enhanced tasting experiences might addict people to the foods they already like and prevent them from trying new foods that they don’t currently like; oftentimes people learn to like foods that they used to not like through exposure.
A group of Chinese researchers at Tsinghua University recently created a chip that combines conventional artificial intelligence (AI) with an AI inspired by the human brain. The team claims this breakthrough could lead to a more naturalized AI. The researchers demonstrated the capabilities of the new chip in a video of a self-driving bicycle that stands-up, balances itself, avoids obstacles, tracks objects and reacts to voice commands. This breakthrough is happening in the context of a US-China technology competition and trade war. Last May, the US put Chinese telecommunications giant Huawei on a trade blacklist to prevent US companies from selling components that might be used to threaten US national security. China is behind in traditional chip making, but AI chip making presents a huge opportunity for China to advance in the technology race. For China, this puts them one step closer to realizing its goal of being the world leader in AI by 2030.
On Monday the Congressional Internet Caucus Academy held a panel titled “In the Era of Streaming, Who’s the Bigger Music Mogul: Jay Z or Congress?” Part of the panel was about the Music Modernization Act, a complex, proposed law that sets up a new compensation mechanism for artists in the streaming environment. Prior to streaming, the system for paying artists was well established; ASCAP and BMI pay songwriters for the use of their work through an national auditing system of venues and broadcast stations while record companies pay artists a royalty on sales. If you are singer/songwriter you get pad from both sides. Straightforward.
Streaming doesn’t fit into either system, hence, the need for the Music Modernization Act.
Kevin Erickson, the director of the Future of Music Coalition (FMC), was brought into Congress to explain the complexities of the act. He brought puppets because he was told the representatives wanted an explanation dumbed down to a five year old level – PowerPoint was too sophisticated.
His presentation explained what you can read in the first paragraph using Sally, the songwriter puppet, who wants to take her music to market with the help of the publishing puppet and Ricky, the artist puppet, who works with a record label puppet. Ricky makes recordings and the record label puppet helps Ricky market his physical recording. He goes on to explain that for streaming, the Music Modernization Act establishes a new non-profit called the Mechanical Licensing Collective (M.L.C.). It will be set up to collect royalties from digital music streaming services such Spotify and Apple Music. The publishers will get paid by the M.L.C. and the publisher will then pay the artist. It really gets confusing when Kermit the Frog sings “It’s Not Easy Being Green.” Who gets the royalty – the puppet, the actor holding the puppet or the songwriter? MLC will sort it all out.
Baidu, a Chinese Tech Giant released a conversational AI machine that works in both Chinese and English. The new machine ERNIE 2.0 claims to outperform Google’s BERT and XLNet in several language understanding benchmarks and in nine different Chinese natural language tasks. ERNIE 2.0 relies on a transformer encoder and the BookCorpus data set for training. The new technology has been relatively successful at analyzing sentiment in movie reviews and inferring meaning from sentences. ERNIE 2.0 is based on an open-sourced language understanding model. This new machine relies on something which Baidu refers to as continual pre-training. It’s a two step process. The first is unsupervised pre-training tasks using a lot of data and the second is multi-task learning.
A new technological age is upon us. Artificial intelligence is being applied across many different industries very quickly. One of the uses in the healthcare field applies AI to diagnose illnesses. It is now being used to distinguish between benign and malignant cells in potential breast cancer tumors. These classifications can help patients avoid unnecessary treatments. AI uses machine learning, the science of getting machines to act without being explicitly programmed, in this case, to help make a diagnosis. It is becoming respected tool for breast cancer tumor classification by putting together and processing huge data sets to help doctors make more accurate, faster diagnoses and to suggest the most appropriate therapies for their patients.