Artificial intelligence (AI) and robotics have become the technology industry's hottest topics with major companies and start-ups cashing-in on their bright promises. On the crest of the AI wave is the advent of conversational computing, or chatbots, as well as more complex virtual assistants that respond to voice commands on smartphones or tablets, such as Amazon's Alexa or Apple's Siri. However, many companies experimenting in this area have yet to get it right – a stark reminder of the risks of overhyping technologies. What many don't realise is that AI has already been around for some time, albeit in different guises. To understand how, it's important to define this technology.

The market research and insight industry will soon undergo a public test of its potency. In two weeks it will predict what it believes will be the outcome of the general election. Already under fire for failing to predict the outcomes of both the UK referendum and the US presidential election, you'd forgive top polling organisations for being nervous. The general election, however, is not the biggest challenge facing the market research industry. Insiders say market research agencies are slowly waking up to a much greater threat.

Artificial intelligence is where the competition is in IT, with Microsoft and Google both parading powerful, always-available AI tools for the enterprise at their respective developer conferences, Build and I/O, in May. It's not just about work: AI software can now play chess, go, and some retro video games better than any human -- and even drive a car better than many of us. These superhuman performances, albeit in narrow fields, are all possible thanks to the application of decades of AI research -- research that is increasingly, as at Build and I/O, making it out of the lab and into the real world. Alexa and Samsung Electronics' Bixby may offer less-than-superhuman performance, but they also require vastly less power than a supercomputer to run. Businesses can dabble on the edges of these, for example developing Alexa "skills" that allow Amazon Echo owners to interact with a company without having to dial its call center, or jump right in, using the various cloud-based speech recognition and text-to-speech "-as-a-service" offerings to develop full-fledged automated call centers of their own.

This could all change--or rather has to change, because humans need emotional connection. If AI-to-human interactions are to gain traction, we will need to develop AI sensitivity, somehow. In the meantime, a new job is up for grabs in Silicon Valley: robot poetry coach. Engineers are recruiting gifted authors and Hollywood screenwriters with sharp wits and a soft heart to make interactive conversational programs, known as bots, more human. They have the difficult job of teaching tact to Microsoft's Cortana and humor to Apple's Siri.

The developmental trajectory of future apps will be heavily influenced by AI and machine learning, says a new report by F5 Networks, released ahead of the annual EMEA F5 Agility conference in Barcelona, Spain. The Future of Apps study, commissioned to The Foresight Factory, says developments in artificial intelligence and machine learning are likely to include more personalised, predictive services in areas such as cognitive health and finance. EMEA is already poised for the next wave of advances in AI, says the report. Nearly a third of surveyed respondents across Europe and South Africa say they use voice commands on their mobile devices. Already, some 10,000 third-party voice enabled apps are available for use with Amazon's Alexa at the end of Q1 2017.

However, AI represents an opportunity to introduce intelligent, scalable engagement and more personalised experiences to help customers accomplish tasks or solve problems while also improving overall satisfaction. Whether they're based in messaging platforms or hardware devices, virtual concierges are bots designed to provide personalised services. We're already seeing the following list of AI applications implemented today: Today's customers live in a multi-screen, omnichannel world. Whether it's integrating back-end CRM, enhancing commerce, personalising experiences, introducing new touch points, predicting behaviors, trends and expectations, successful AI implementations require a new blueprint.

The biggest hardware and software arrival since the iPad in 2010 has been Amazon's Echo voice-controlled intelligent speaker, powered by its Alexa software assistant. But just because you're not seeing amazing new consumer tech products on Amazon, in the app stores, or at the Apple Store or Best Buy, that doesn't mean the tech revolution is stuck or stopped. They are: Artificial intelligence / machine learning, augmented reality, virtual reality, robotics and drones, smart homes, self-driving cars, and digital health / wearables. Google has changed its entire corporate mission to be "AI first" and, with Google Home and Google Assistant, to perform tasks via voice commands and eventually hold real, unstructured conversations.

Addressing thousands of developers at the annual Google I/O conference on Wednesday, Google CEO Sundar Pichai outlined the company's new strategy to transition from mobile first to artificial intelligence and machine learning. The goal is to equip the company's line of digital assistant products and services to anticipate the needs of users, and comprehend sights and sounds in ways never before possible on a massive scale. Google's deep learning and computer vision capabilities have advanced dramatically, according to Pichai, and now impact everything from cloud computing to Gmail, search and mobile devices. "We spoke last year about this important shift in computing from mobile first to AI first," Pichai recalled. "Similarly, in the AI-first world, we're rethinking all our products and applying AI and machine learning to solve human problems."

This is my last weekly column for The Verge and Recode -- the last weekly column I plan to write anywhere. I've been doing these almost every week since 1991, starting at The Wall Street Journal, and during that time, I've been fortunate enough to get to know the makers of the tech revolution, and to ruminate -- and sometimes to fulminate -- about their creations. Now, as I prepare to retire at the end of that very long and world-changing stretch, it seems appropriate to ponder the sweep of consumer technology in that period, and what we can expect next. Let me start by revising the oft-quoted first line of my first Personal Technology column in the Journal on October 17th, 1991: "Personal computers are just too hard to use, and it's not your fault." It was true then, and for many, many years thereafter. Not only were the interfaces confusing, but most tech products demanded frequent tweaking and fixing of a type that required more technical skill than most people had, or cared to acquire.

On Wednesday, at the 2017 Google I/O developer conference in Mountain View, CA, Google CEO Sundar Pichai said that the company is rethinking all of its products with a renewed focus on machine learning and artificial intelligence (AI). One recent example of the company's use of machine learning is in Google Home, the company's smart speaker powered by Google Assistant, which uses deep learning to allow multiple users to share a single Google Home unit. Pichai also announced that the machine learning-driven Smart Reply feature is coming to Gmail on iOS and Android as well. One of the big announcements at I/O was Google Lens, a set of vision-based computing capabilities that seeks to understand what a user is looking at with their smartphone's camera, and help them take action based on that information. For example, a user can take a picture of a flower, and Lens will tell the user the kind of flower it is, Pichai said.