Thinking machines are still decades away from stealing our jobs, but in the meantime they could drastically improve our quality of life and even earn us some extra cash, according to a leading artificial intelligence expert. Toby Simpson, chief technology officer at Cambridge global learning and simulation firm Ososim, will discuss how AI will impact our future at the London Press Club's monthly gathering on Monday. Mr Simpson previously worked at AI company DeepMind, which was acquired by Google in 2014 and is now based in King's Cross. He specialises in biologically inspired artificial intelligence, which helps to make it more human-like and emotional in its interactions with people. At the event for industry professionals in Whitehall -- called Technology: How far can it go?

Tech giants like Facebook, Google, and Baidu know that people aren't filling their devices with apps anymore. Just 35 percent of smartphone users download a single app in an average month, and the average app loses 90 percent of its daily active users within 30 days of release. While it might be fun to slice fruit or slingshot cartoon birds while waiting for the bus, these apps can't offer the frictionless experience users crave. Consumers want a new, on-demand kind of app: one clad in a conversational interface, ready to serve, and capable of complex actions. Want to check your flight status, book an Uber for when you land, and schedule your meetings for that afternoon?

Artificial intelligence (AI) has gradually become an integral part of modern life, from Siri and Spotify's personalized features on our phones to automatic fraud alerts from our banks whenever a transaction appears suspicious. Defined simply, a computer with AI is able to respond to its environment by learning on its own--without humans providing specific instructions. A new report from Stanford University in Palo Alto, California, outlines how AI could become more integrated into people's lives by 2030, and recommends how best to regulate it and make sure its benefits are shared equally. Here are five examples--some from this report--of AI technology that could become a part of our lives by 2030. Smart traffic lights using artificial intelligence technology to learn and adapt to traffic patterns in real time could make intersections safer and more efficient.

Alicia Vikander in "Ex Machina," a sci-fi thriller about an eccentric inventor who designs artificial intelligence. Artificial intelligence (AI) is all about getting a machine to mimic a human in every way: thought, speech, movement. That's why one of the tests for AI is the Turing test: whether a robot can fool a human into thinking it is conversing with another of its own species. An integral part of accomplishing this is making the AI recognize human emotions. So one research lab is working on the next iteration of virtual assistants, those that can recognize and react to emotional cues.

It's hard to put your finger on why, but the voices our computers use to speak just sound wrong. Even with the best voice programming, like Amazon's Alexa or Apple's Siri, computers sound--well--robotic when they talk. But that could change soon. Neural networks are now tackling the problem of making computer speech sound more natural, filling sentences with nonverbal sounds like lip smacks, breath intakes, and irregular pauses. DeepMind, an Alphabet-owned world leader in artificial intelligence research, recently published a blog post about WaveNet, a convolutional neural network (like DeepDream) that can reduce the performance gap between computer and human speech by about 50%, researchers say.

Artificial intelligence (AI) is all about getting a machine to mimic a human in every way: thought, speech, movement. That's why one of the tests for AI is the Turing test: whether a robot can fool a human into thinking it is conversing with another of its own species. An integral part of accomplishing this is making the AI recognize human emotions. So one research lab is working on the next iteration of virtual assistants, those that can recognize and react to emotional cues. SRI International, the birthplace of Siri, is working on better chatbots and phone assistants that can detect agitation, confusion, and other emotional states, and respond accordingly.

It might surprise some of you to know that artificial intelligence (AI) is already in use and a routine part of our daily lives, but we leverage this technology when we use our smartphones or other devices to ask Apple's Siri, Microsoft's Cortana, Google Now, or Amazon's Alexa a question to get the facts or data we are looking for. Using your voice, you can say, "Where's the nearest gas station?" or "What's on my calendar today?", and the intelligent personal assistant (IPA) will respond by finding information and relaying it from your phone or sending commands to other apps. However, AI is not only being used in the context of a personal assistant. Self-driving cars are moving closer to reality, retailers are using anticipatory shipping in the hopes to send you items before you need them, and banks are using AI to detect fraud to monitor accounts and alert the owners when questionable transactions occur. So it is here, but what is AI? AI is an intelligent system's ability to improve predictions, accelerate problem solving, and automate administrative tasks, ushering in an era of automation.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.

With the Echo listening constantly for the wake word (e.g., "Alexa"), it is conscious of everything you are saying. Nothing is actually recorded and sent to the Amazon cloud until the wake word has been heard, then recording starts (including a clip that spans a few seconds before the wake word was spoken). This is set up so that Echo can be continually learning how you are using Alexa. Becoming familiar with the ways you are interacting with the device is the most effective way to improve the product over time.

This article was originally published on TechRepublic. Remember the famous scene in Stanley Kubrick's 1968 2001: A Space Odyssey, when Hal 9000--the intelligent-turned-malevolent computer--regresses to his "childhood" and sings "Daisy Bell" as he's decommissioned by astronaut Dave Bowman? Its inspiration was a real-life Bell Labs demonstration of speech synthesis on an IBM 704 mainframe in 1961, witnessed by Arthur C Clark, who later incorporated it into his 2001 novel and screenplay. Although Bell Labs' involvement in the field stretches back to the 1930s with Homer Dudley's keyboard-and-footpedal-driven Voder speech synthesis device, it's undoubtedly the classic Kubrick/Clarke movie that cemented the ideas of artificial intelligence (AI) and conversing with computers into the public mind. Depending on how old you are, we're now familiar with computerised voices, thanks to devices like Texas Instruments' popular 1978 Speak & Spell educational toy, Stephen Hawking's speech synthesiser (memorably sampled in the Pink Floyd song Keep Talking), GPS navigational systems in your car, and any number of public information and call handling systems. More recently, the combination of automatic speech recognition (ASR), natural-language understanding (NLU) and text-to-speech (TTS) has come to mainstream attention in virtual assistants such as Apple's Siri, Google Now, Microsoft's Cortana, and Amazon's Alexa. To get a handle on how speech technologies work, we clearly need to know something about the mechanics of human speech and the structure of language. When we speak, air from the lungs passes through the vocal tract to produce "voiced" or "unvoiced" sounds (depending on whether the vocal cords are vibrating or not) that may then be modulated by the tongue, teeth and lips.