Without noticing it, artificial intelligence (AI) already fits in many forms into our daily lives and supports our decision-making. At times, it's discussed a bit like blockchain, which we're being promised will solve things like world hunger and human trafficking. However, artificial intelligence is already prevalent practically everywhere in technology, from cars to Google searches and as this technology is specifically designed for singular tasks, we humans cannot compete with that level of insistent focus. This relates specifically to what is called Artificial Narrow Intelligence or ANI designed to perform one sole task meticulously. The two next levels of AI development, specifically Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) are still out of our reach, for now.

In a departure from using AI and machine learning tools for tasks such as automating customer service, some companies are applying the technologies to grow better corn crops and exterminate bugs and vermin. Artificial intelligence (AI) is rising in prominence with the proliferation of chatbots, virtual assistants and other conversational tools that companies are using to improve customer service, productivity and operational efficiency. But AI is also helping to automate and streamline tasks in data-intensive industries traditionally ruled by rigorous science and good old-fashioned human analysis. Seed retailers, for example, are using AI products to churn through terabytes of precision agricultural data to create the best corn crops, while pest control companies are using AI-based image-recognition technology to identify and treat various types of bugs and vermin. Such markedly different scenarios underscore how AI has evolved from science fiction to practical solutions that can potentially help companies get a leg up on their competition.

Touchkin is a platform that uses AI and machine learning to provide personalised care solutions. For Jo Aggarwal and Ramakant Vempati, one of the biggest concerns they had while working abroad was the health of their family living back in India. The husband and wife duo were working in high-flying corporate jobs with organsations like Pearson Learning Solutions and Goldman Sachs International in the UK. Living away from family, the couple realised that remote care giving was a huge global challenge, not just in India, but abroad as well, especially when it came to understanding the emotional and mental well-being of the families living in their home countries. So, in 2012, the two of them moved back to India to take care of their family.

Google Assistant can now give you step-by-step instructions in the kitchen. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. Google Assistant can now give you step-by-step instructions in the kitchen.

Amazon revealed the Echo Show Tuesday, and with coverage of the Alexa gadget came rumors about Apple's upcoming Siri speaker. Apple employees have been testing the Siri speaker in their homes for several months, sources familiar with the matter told Bloomberg. So far, it's unknown whether Apple's upcoming Siri speaker will come with a built-in display, like Amazon's Echo Show. Marketing chief Phil Schiller said last week in an interview he thinks voice assistant devices are beneficial, but that doesn't mean you'd never want a screen. KGI Securities analyst Ming-Chi Kuo previously said there's a more than 50 percent chance Apple could announce its Siri speaker at the Worldwide Developers Conference this June.

Earlier this year, artificial intelligence scientist Sebastian Thrun and colleagues at Stanford University demonstrated that a "deep learning" algorithm was capable of diagnosing potentially cancerous skin lesions as accurately as a board-certified dermatologist. The cancer finding, reported in Nature, was part of a stream of reports this year offering an early glimpse into what could be a new era of "diagnosis by software," in which artificial intelligence aids doctors--or even competes with them. Experts say medical images, like photographs, x-rays, and MRIs, are a nearly perfect match for the strengths of deep-learning software, which has in the past few years led to breakthroughs in recognizing faces and objects in pictures. Companies are already in pursuit. Verily, Alphabet's life sciences arm, joined forces with Nikon last December to develop algorithms to detect causes of blindness in diabetics.

Depression is a simple-sounding condition with complex origins that aren't fully understood. Now, machine learning may enable scientists to unpick some of its mysteries in order to provide better treatment. For patients to be diagnosed with Major Depressive Disorder, which is thought to be the result of a blend of genetic, environmental, and psychological factors, they have to display several of a long list of symptoms, such as fatigue or lack of concentration. Once diagnosed, they may receive cognitive behavioral therapy or medication to help ease their condition. But not every treatment works for every patient, as symptoms can vary widely.

Many people wish they could predict what will happen next in the world. Many predictions are assigned to the waste bin of time very quickly. With hindsight, unforeseen factors come into play that changed their'models'. It is because there were so many factors involved to predict. The ability of models to analyse and interpret means technology was not able to process, analyse and predict with a high degree of success.

Facebook's deep-learning artificial intelligence systems have learned to recognize your friends in your photos, and Google's AI has learned to anticipate what you'll be searching for. But there's no need to feel left out, even if your company's computers haven't learned much lately. A growing number of tech giants and startups have begun offering machine learning as a cloud service. That means other companies and startups do not need to develop their own specialized hardware or software to apply deep learning--the high-powered version du jour of machine learning--to their specific business needs. "Deep-learning algorithms dominate other machine-learning methods when data sets are large," says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego, who has examined cloud AI services from companies such as Amazon and IBM.

I am spending some cycles on my algorithmic rotoscope work -- which is basically a stationary exercise bicycle for my learning about what is and what is not Machine Learning. I am using it to help me understand and tell stories about Machine Learning by creating images using Machine Learning that I can use in my Machine Learning storytelling. Picture a bunch of Machine Learning gears all working together to help make sense of what I'm doing, and WTF I am talking about? As I'm writing a story on how image style transfer Machine Learning could be put to use by libraries, museums, and collection curators, I'm reminded of what a con machine learning will be in the future, and how it will be a vehicle for the extraction of value and outright theft. My image style transfer work is just one tiny slice of this pie.