Enthusiasts predicted the plan would relieve the pressure on hard-pressed GPs. Critics saw it as a sign of creeping privatisation and a data-protection disaster in waiting. Reactions to news last month that Amazon's voice-controlled digital assistant Alexa was to begin using NHS website information to answer health queries were many and varied. US-based healthcare tech analysts say the deal is just the latest of a series of recent moves that together reveal an audacious, long-term strategy on the part of Amazon. From its entry into the lucrative prescription drugs market and development of AI tools to analyse patient records, to Alexa apps that manage diabetes and data-driven experiments on how to cut medical bills, the $900bn global giant's determination to make the digital disruption of healthcare a central part of its future business model is becoming increasingly clear.

As oceans are altered by rising temperatures, acidification and other consequences of anthropogenic activity, understanding the behavioral patterns and responses of marine animals is required for effective stewardship. Researchers have made great strides in investigating marine megafauna behavior related to long-distance migrations (Block et al., 2011; Rasmussen et al., 2007; Sequeira et al., 2018) and foraging strategies (Sims et al., 2008; Weise et al., 2010). However, the behavior of more numerous, higher total-biomass, lower trophic-level animals such as zooplankton is much less well understood. Early attempts to investigate in situ behavior of zooplankton such as jellyfish relied on scuba divers following animals with hand-held video cameras (Colin and Costello, 2002; Costello et al., 1998) and later with remotely operated vehicles (ROVs; Kaartvedt et al., 2015; Purcell, 2009; Rife and Rock, 2003). Acoustic methods have also been used to describe large-scale movement patterns of jellyfish (Båmstedt et al., 2003; Kaartvedt et al., 2007; Klevjer et al., 2009), although these methods can be resolution-limited.

One industry that's always been at the cutting edge of technology is telecommunications. If we consider some of the breakthroughs made in human achievements, the most significant involve communication technology – from wired telegraph relays crossing vast continents to the invention of the telephone, to the world-crossing internet and the ubiquitous mobile phone. The need to communicate being at the heart of human behavior means that modern communications service providers (CSPs) operate in a highly competitive market; everyone needs to connect. Unless companies can find differentiation from one another, the technology that underpins the many services (like landlines, internet and mobile) is interchangeable, for businesses and consumers alike. After all, changing one's cellphone provider can be as simple as flicking a software toggle switch in a phone's settings to use SIM B, not SIM A. Retaining profitability is essential for CSPs as it is in every other vertical, of course, and with a saturated market that's based on technology, the challenges in the sector are very specific.

"We have a broad repertoire of products that consumers love to eat, to use, and to clean with," explained Alex Owens, VP and global head of data and analytics at Unilever. "My job is to get insights on people that use our products, to ensure that we can deliver the products that meet their needs, as well as get us the data that we have on consumers and that we collect with their consent, to power a real-time marketing capability," Owens said. Due to its size and reach, Unilever has a special partnership with Google, Owens said. The company gets top-level support and enjoys early access to many Google products. One that Unilever relies heavily on for its AI for marketing efforts is the Google Cloud Vision API, a product that, according to Google's webpage for it, provides easily modifiable pre-trained machine learning models that can analyze objects, images and text.

Fast-forward to today, and of course someone has figured it out. This special kind of artificial intelligence is called machine learning . If anything, Gates was too conservative in his estimates. Experts say the market opportunity is now far, far greater than 10 Microsofts. Other top business leaders are on board as well.

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so.

A way of identifying a condition that causes irregular heartbeat may have been discovered by artificial intelligence via computer modelling at the Mayo Clinic. Atrial fibrillation causes an irregular and often abnormally fast heart rate, and can sometimes be higher than 100 beats per minute, which can cause problems including dizziness, shortness of breath and tiredness. The research findings are published in The Lancet reveal the research group at the clinic developed an AI-enabled electrocardiograph (ECG) using a convolutional neural network to'detect he electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. The study included nearly 181,000 patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017. The team behind the study has said it is still early days and further research and testing was needed.

Both industries and governments alike have invested significantly in the AI field, with many AI-related startups established in the last 5 years. If another AI winter were to come about many people could lose their jobs, and many startups might have to shut down, as has happened before. Moreover, the economic difference between an approaching winter period or ongoing success is estimated to be at least tens of billions of dollars by 2025, according to McKinsey & Company. This paper does not aim to discuss whether progress in AI is to be desired or not. Instead, the purpose of the discussions and results presented herein is to to inform the reader of how likely progress in AI research is. For a detailed overview of both AI winters check out my first and second medium article on the topic. In this section, the central causes of the AI winters are extracted from the above discussion of previous winters.

Netflix recommends your next binge based on the show you watched last night. Amazon convinces you to buy three more items that align with your buying habits. And ride-share apps like Uber or Lyft analyze your location and connect you with a driver heading your direction. When AI makes everyday tasks easier, the consumer experience feels seamless. But for many business and marketing leaders AI is the buzzword du juor keeping them up at night.

Love it or hate it, Artificial Intelligence is definitely here to stay. It may not be the final piece in the digital jig saw – who knows what great new innovation will emerge next, that's the nature of our agile new society – but it might be the latest and most liberating to date. In fact, in many ways Artificial Intelligence (AI) is the digital component that makes sense of all the others. So what are the benefits of Artificial Intelligence? While 70% of transformations fail, organisations that are delivering the digital promise are reaping rewards.