Few technological advancements have been as divisive as artificial intelligence (AI), especially in matters where human lives could hang in the balance. These AI solutions make complex treatment recommendations for cancer patients based on their individual characteristics and risk factors. Roughly 60 percent of the WHO's "essential" medicines are unavailable in these countries, due partially to the cost and partially due to a lack of medical professionals in the area. Developing AI in the medical industry, further than it's already gone, is going to help us resolve the forthcoming doctor shortage, mitigate the ever-rising costs of healthcare, and bring adequate medical treatment to areas of the world that are currently without it.

Some banks customers are already using virtual assistants powered by artificial intelligence to ask questions like "What's my account balance?" without having to look it up themselves online or on a mobile device. While strides have been made in natural language processing and machine learning, many chatbots still must hand off to a person when the conversation gets too complicated. But some banks are looking to virtual assistants to be all-in-one bankers: equal parts teller, contact center representative, investment adviser and mortgage broker.

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The biggest change and most technology-forward move yet was just announced as the new AI Chatbot programming in the Starbucks app will be released soon. The new system, called My Starbucks Barista, will allow a customer to type or say their order through messaging, to which the AI chatbot will respond with follow-up questions in order to determine the completed order. The program was able to recognize what was being asked and ask follow-up questions to complete the order. Just like Starbucks isn't cutting off your ability to order food in person still, you shouldn't cut off a customer's ability to contact you in person, but if you can get your customers on board with AI, it will increase your efficiency and accuracy.

Artificial Intelligence (AI) is powering the ad industry's transformation into the ad tech industry. Based on a particular context, we understand that intelligent algorithms take actions (or make decisions) to maximise a defined objective. All of these inputs influence the AI algorithm's decision-making process, so one of the biggest challenges that ad tech companies face when trying to implement AI into their core technology (along with of having the right team of professionals) is to select and add new variables each time to create new patterns, and to train algorithms correctly to generate the best outcomes. The future belongs to them, so choosing the right partner will make a significant difference for the success of your campaigns, helping your brand make better decisions and support to maximise advertising revenue.

Along with autonomous cars, virtual reality, and drones, artificial intelligence (AI) has been one of the key areas of tech that is generating huge amounts of excitement. Basically AI develops an understanding of users' regular spending patterns, and is able to adapt quickly and learn from any changes. In a similar way to understanding spending habits, AI can be applied to understand an individual's investing history and preferences. By building an understanding of a user's transactional history, AI can be used to understand the relationship between transactions and other external factors, such as the time of year or month, purchases made when an account balance reaches a certain level, or when purchases happen in reaction to others.

Zipline has hired locals to operate its drones and run the distribution centers, which stock blood products and medical supplies. Yet "two-way services are what's actually needed for clinical care," says Amukele, who heads clinical pathology labs at Johns Hopkins' Bayview Medical Center. Delivering emergency supplies and picking up diagnostic test samples require aircraft with different capabilities, says Jeff Street, a drone engineer and pilot. Street and Amukele have shown that unmanned aircraft can safely ship a range of clinical specimens and recently set a new distance record for medical drone transport -- a three-hour flight carrying human blood samples across 161 miles of Arizona desert.

Desperate, the doctors called a distribution center near Kigali, where clinic workers and a flight crew loaded a series of small, unmanned aircraft with the needed supplies and launched them into the sky. The Tanzanian government wants to make as many as 2,000 daily deliveries from four distribution centers serving an area roughly the size of Texas and Louisiana. Each can carry 3 pounds of cargo (one unit of blood weighs roughly 1.2 pounds), and the batteries can make a round trip of 100 miles. Zipline makes a habit of recruiting and training local engineers, health workers, and flight operators.

It explores the study and construction of algorithms that can learn from and make predictions on data sets that are provided by building a model from sample data sets provided during a "training" period. In a supervised training period, a human feeds the data set to the computer along with the correct answer. The algorithms must build a model identifying how the correct answer is indeed the correct answer. An unsupervised training period is when the data set is provided to the computer which, in turn, discovers both the correct answer and how to figure out the correct answer.

The test was to see if the neural networks, once trained, could take a new story from the database and predict how many Quora upvotes it got – a sign that the AI was understanding what makes a story popular and what doesn't. In the team's experiments, the neural networks proved better at judging stories than traditional machine-learning techniques, with the network judging the story as a whole, registering an 18 percent improvement. Having said that, it's another example of the way advanced AI techniques like neural networks – which are capable of accepting and processing vast amounts of data in a layered way – can potentially make systems that are more human-like over time. "The ability to predict narrative quality impacts on both story creation and story understanding," says Disney Research vice president Markus Gross, who wasn't directly involved in the research.