Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is artificial intelligence. You know, HAL 9000 and Marvin the Paranoid Android? Thanks to books and movies, each generation has formed its own fantasy of a world ruled (or at least served) by robots. We've been conditioned to expect flying cars that steer clear of traffic and robotic maids whipping up our weekday dinner. But if the age of AI is here, why don't our lives look more like the Jetson's?

Conversational AI is a huge technology advancement – as momentous as the unveiling of the Internet in 1983 or when Steve Jobs launched the iPhone in January 2007. But within the last year or two, Conversational AI has evolved into a cornerstone of innovation. Gone are the days of single-use chatbots that execute pre-scripted, single-path programs or recite your service manual to customers. With Conversational AI, we are talking about complex, Machine Learning (ML)-powered, intelligent Digital Assistants that can drive unmatched customer and employee interactions based on the current context, past history, even predicting the flow of conversations and delivering next best actions – based on naturally expressive voice or text. Where the engagement with a digital assistant is intelligent enough that you think of it as a "cobot" – a co-pilot in your journey as a customer, employee, vendor or partner.

Technology is a tool that helps us live out our God-given callings. This is one of the most important things for us to learn as we engage the topic of technology and artificial intelligence. Because we often see the tremendous power that technology has over our lives, we are tempted to treat technology as more than a tool, as something with a value similar to our own if it is powerful enough or does enough work on its own. Technology will be misused and abused by broken people just like you and me. Nowhere in Scripture is a tool or a technology condemned for being evil.

Hinge is going to give people in the US a $100 Visa online gift card to go on date, a decision that'll presumably encourage them to schedule plans while also helping the company market itself and grow. The dating app previously partnered with bars to give its daters discounts, but this is the first time the company has given people what essentially amounts to cash. The company has $25,000, or enough money for 250 daters, set aside for the promotion. To qualify, Hinge users have to pause their accounts from 4PM ET on Friday, March 6th until 4PM ET on Saturday, March 7th. After reactivating, people have to click on their date's profile and select that they "met," which tells the app that they met in-person and prompts it to ask whether this is the type of person this dater would want to see again.

Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).

This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

Most people talk about AI in very broad terms. The reality, however, is the artificial intelligence we have now is very different to the AI we see in the movies, i.e. AI where robots and machines are conscious, can deal with a myriad of situations, and have emotion. Specifically, the AI we have today is known as Narrow AI or Weak AI. The AI we want to get to in the future is General AI, also known as Strong AI.

The emergence of Artificial Intelligence (AI) and Machine Learning (ML) has transformed the role of digital technology in business operations. These cognitive technologies have boosted the value addition of computing and digital processing power, from pre-programmed process automation to turning data into actionable insights. AI is allowing businesses to be ultra-responsive in real-time, achieve system transparency and gain valuable insights into customers, markets and processes. The applications of AI are not only vast in themselves, but the technology can also be integrated with machines and human users through multiple interfaces. For instance, Audio AI allows humans to interact with machines using natural language processing algorithms.

Recommender systems (RS) are increasingly present in our daily lives, especially since the advent of Big Data, which allows for storing all kinds of information about users' preferences. Personalized RS are successfully applied in platforms such as Netflix, Amazon or YouTube. However, they are missing in gastronomic platforms such as TripAdvisor, where moreover we can find millions of images tagged with users' tastes. This paper explores the potential of using those images as sources of information for modeling users' tastes and proposes an image-based classification system to obtain personalized recommendations, using a convolutional autoencoder as feature extractor. The proposed architecture will be applied to TripAdvisor data, using users' reviews that can be defined as a triad composed by a user, a restaurant, and an image of it taken by the user. Since the dataset is highly unbalanced, the use of data augmentation on the minority class is also considered in the experimentation. Results on data from three cities of different sizes (Santiago de Compostela, Barcelona and New York) demonstrate the effectiveness of using a convolutional autoencoder as feature extractor, instead of the standard deep features computed with convolutional neural networks.

Artificial intelligence (AI) systems, powered by massive data and sophisticated algorithms -- including but not limited to -- deep neural networks and statistical machine learning (ML)(support vector machines, clustering, random forest, etc.), are having profound and transformative impact on our daily lives as they make their way into everything from finance to healthcare, from retail to transportation. Netflix movie recommender, Amazon's product prediction, Facebook's uncanny ability to show what you may like, Google's assistant, DeepMind's AlphaGo, Stanford's AI beating human doctors. Machine learning is eating software. However, one of the common features of these powerful algorithms is that they utilize sophisticated mathematics to do their job -- to classify and segment an image, to arrive at the key decisions, to make a product recommendation, to model a complex phenomenon, or to extract and visualize a hidden pattern from a deluge of data. All of these mathematical processes are, quite simply, beyond the scope of a single human (or a team) to perform manually (even on a computer) or inside their head.