Each time any of us uses a tool, such as Gmail, where theres a powerful agent to help correct our spellings, and suggest sentence endings, theres an AI machine in the background, steadily getting better and better at understanding language. Sentence structures are parsed, word choices understood, idioms recognised.

That exact capability could, in 2020, grant the ability to speak with other large animals. Really. Maybe even faster than brain-computer interfaces will take the stage.

Our AI-enhanced abilities to decode languages have reached a point where they could start to parse languages not spoken by anyone alive. Recently, researchers from MIT and Google applied these abilities to ancient scripts Linear B and Ugaritic (a precursor of Hebrew) with reasonable success (no luck so far with the older, and as-yet undeciphered Linear A).

First, word-to-word relations for a specific language are mapped, using vast databases of text. The system searches texts to see how often each word appears next to every other word. This pattern of appearances is a unique signature that defines the word in a multidimensional parameter space. Researchers estimate that languages all languages can be best described as having 600 independent dimensions of relationships, where each word-word relationship can be seen as a vector in this space. This vector acts as a powerful constraint on how the word can appear in any translation the machine comes up with.

These vectors obey some simple rules. For example: king man + woman = queen. Any sentence can be described as a set of vectors that in turn form a trajectory through the word space.

These relationships persist even when a language has multiple words for related concepts: the famed near-100 words Inuits have for snow will all be in similar dimensional spaces each time someone talks about snow, it will always be in a similar linguistic context.

Take a leap. Imagine that whale songs are communicating in a word-like structure. Then, what if the relationships that whales have for their ideas have dimensional relationships similar to those we see in human languages?

That means we should be able to map key elements of whale songs to dimensional spaces, and thus to comprehend what whales are talking about and perhaps to talk to and hear back from them. Remember: some whales have brain volumes three times larger than adult humans, larger cortical areas, and lower but comparable neuron counts. African elephants have three times as many neurons as humans, but in very different distributions than are seen in our own brains. It seems reasonable to assume that the other large mammals on earth, at the very least, have thinking and communicating and learning attributes we can connect with.

What are the key elements of whale songs and of elephant sounds? Phonemes? Blocks of repeated sounds? Tones? Nobody knows, yet, but at least the journey has begun. Projects such as the Earth Species Project aim to put the tools of our time particularly artificial intelligence, and all that we have learned in using computers to understand our own languages to the awesome task of hearing what animals have to say to each other, and to us.

There is something deeply comforting to think that AI language tools could do something so beautiful, going beyond completing our emails and putting ads in front of us, to knitting together all thinking species. That, we perhaps can all agree, is a better and perhaps nearer-term ideal to reach than brain-computer communications. The beauty of communicating with them will then be joined to the market ideal of talking to our pet dogs. (Cats may remain beyond reach.)

Mary Lou Jepsen is the founder and CEO of Openwater. John Ryan, her husband, is a former partner at Monitor Group

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Artificial intelligence is helping us talk to animals (yes, really) - Wired.co.uk

By Vivek Wadhwa

AI has the potential to be as transformative to the world as electricity, by helping us understand the patterns of information around us. But it is not close to living up to the hype. The super-intelligent machines and runaway AI that we fear are far from reality; what we have today is a rudimentary technology that requires lots of training. Whats more, the phrase artificial intelligence might be a misnomer because human intelligence and spirit amount to much more than what bits and bytes can encapsulate.

I encourage readers to go back to the ancient wisdoms of their faith to understand the role of the soul and the deeper self. This is what shapes our consciousness and makes us human, what we are always striving to evolve and perfect. Can this be uploaded to the cloud or duplicated with computer algorithms? I dont think so.

What about the predictions that AI will enable machines to have human-like feeling and emotions? This, too, is hype. Love, hate and compassion arent things that can be codified. Not to say that a machine interaction cant seem human we humans are gullible, after all. According to Amazon, more than 1 million people had asked their Alexa-powered devices to marry them in 2017 alone. I doubt those marriages, should Alexa agree, would last very long!

Todays AI systems do their best to replicate the functioning of the human brains neural networks, but their emulations are very limited. They use a technique called Deep Learning. After you tell a machine exactly what you want it to learn and provide it with clearly labelled examples, it analyses the patterns in those data and stores them for future application. The accuracy of its patterns depends on completeness of data. So the more examples you give it, the more useful it becomes.

Herein lies a problem, though an AI system is only as good as the data it receives. It is able to interpret them only within the narrow confines of the supplied context. It doesnt understand what it has analysed so it is unable to apply its analysis to other scenarios. And it cant distinguish causation from correlation.

AI shines in performing tasks that match patterns in order to obtain objective outcomes. Examples of what it does well include playing chess, driving a car on a street and identifying a cancer lesion in a mammogram. These systems can be incredibly helpful extensions of how humans work, and with more data, the systems will keep improving. Although an AI machine may best a human radiologist in spotting cancer, it will not, for many years to come, replicate the wisdom and perspective of the best human radiologists. And it wont be able to empathise with a patient in the way that a doctor does. This is where AI presents its greatest risk and what we really need to worry about use of AI in tasks that may have objective outcomes but incorporate what we would normally call judgement. Some such tasks exercise much influence over peoples lives. Granting a loan, admitting a student to a university, or deciding whether children should be separated from their birth parents due to suspicions of abuse falls into this category. Such judgements are highly susceptible to human biases but they are biases that only humans themselves have the ability to detect.

And AI throws up many ethical dilemmas around how we use technology. It is being used to create killing machines for the battlefield with drones which can recognise faces and attack people. China is using AI for mass surveillance, and wielding its analytical capabilities to assign each citizen a social credit based on their behaviour. In America, AI is mostly being built by white people and Asians. So, it amplifies their inbuilt biases and misreads African Americans. It can lead to outcomes that prefer males over females for jobs and give men higher loan amount than women. One of the biggest problems we are facing with Facebook and YouTube is that you are shown more and more of the same thing based on your past views, which creates filter bubbles and a hotbed of misinformation. Thats all thanks to AI.

Rather than worrying about super-intelligence, we need to focus on the ethical issues about how we should be using this technology. Should it be used to recognise the faces of students who are protesting against the Citizenship (Amendment) Act? Should India install cameras and systems like China has? These are the types of questions the country needs to be asking.The writer is a distinguished fellow and professor, Carnegie Mellon Universitys College of Engineering, Silicon Valley.

This story is part of the 'Tech that can change your life in the next decade' package.

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Quantum leap: Why we first need to focus on the ethical challenges of artificial intelligence - Economic Times

Artificial intelligence (AI) has transformed the face of computing, making its mark on everything from cybersecurity to modern medicine. There's no sign of a slowdown, with analysts predicting that by 2022 worldwide spending within the AI industry will soar to US$79.2 billion. There have been some incredible breakthroughs over the past decade, with some of the most significant highlighted below. 2010 Deep learning advances

While the foundations for deep learning sate to the 1980s, researchers George Dahl and Abdel-rahman Mohamed broke new ground in 2010 when they developed advanced deep learningspeech recognition tools. This paved the way for more deep learning advances focusing on anything from facial recognition to machine translation.

In 2011 a question-answering computer system developed by IBM's DeepQA project made headlines when it outplayed Brad Rutter and Ken Jennings, two of the most successful contestants to take part in the popular American game show Jeopardy!

Artificial intelligence took another stride forward in October 2011 when Apple launched Siri, it's signature personal assistant. From reciting the weather forecast to plotting a route on Google Maps, Siri is now used by hundreds of millions of people around the world.

In 2015 Google successfully pulled off "the world's first fully driverless ride on public roads" using its Waymomodel. The passenger was a blind American man called Steve Mahan, a close friend of principal engineer Nathaniel Fairfield.

Perceptions of artificial intelligence were challenged in 2018 when a set of original paintings created by machines using Generative Adversarial Network technology sold for more than US$400,000 at a Christies auction. The portrait was created using a two-part algorithm that analysed image data from 15,000 portraits dating from the 14th to 20th centuries.

Artificial intelligence won more headlines in 2019 when Google launched an AI system that can detect lung cancer with more accuracy than human radiologists. The system is powered by deep learning and uses an algorithm to analyse computed tomography (CT) scans and predict the risk of developing the disease.

Want to know more about the most significant scientific breakthroughs of 2019? Introducing the latest technology from robotics companyAndrew Alliance, 'Addressing the challenges of the reproducibility crisis with improved automation and protocol sharing' spotlights advanced laboratory automation and software infiltrating labs around the world.

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Science in the 2010s: Artificial Intelligence - Labmate Online

Last year, just as I was beginning to cover artificial intelligence, the AI world was getting a major wake-up call. There were some incredible advancements in AI research in 2018from reinforcement learning to generative adversarial networks (GANs) to better natural-language understanding. But the year also saw several high-profile illustrations of the harm these systems can cause when they are deployed too hastily.

A Tesla crashed on Autopilot, killing the driver, and a self-driving Uber crashed, killing a pedestrian. Commercial face recognition systems performed terribly in audits on dark-skinned people, but tech giants continued to peddle them anyway, to customers including law enforcement. At the beginning of this year, reflecting on these events, I wrote a resolution for the AI community: Stop treating AI like magic, and take responsibility for creating, applying, and regulating it ethically.

In some ways, my wish did come true. In 2019, there was more talk of AI ethics than ever before. Dozens of organizations produced AI ethics guidelines; companies rushed to establish responsible AI teams and parade them in front of the media. Its hard to attend an AI-related conference anymore without part of the programming being dedicated to an ethics-related message: How do we protect peoples privacy when AI needs so much data? How do we empower marginalized communities instead of exploiting them? How do we continue to trust media in the face of algorithmically created and distributed disinformation?

Sign up for The Algorithm artificial intelligence, demystified

But talk is just thatits not enough. For all the lip service paid to these issues, many organizations AI ethics guidelines remain vague and hard to implement. Few companies can show tangible changes to the way AI products and services get evaluated and approved. Were falling into a trap of ethics-washing, where genuine action gets replaced by superficial promises. In the most acute example, Google formed a nominal AI ethics board with no actual veto power over questionable projects, and with a couple of members whose inclusion provoked controversy. A backlash immediately led to its dissolution.

Meanwhile, the need for greater ethical responsibility has only grown more urgent. The same advancements made in GANs in 2018 have led to the proliferation of hyper-realistic deepfakes, which are now being used to target women and erode peoples belief in documentation and evidence. New findings have shed light on the massive climate impact of deep learning, but organizations have continued to train ever larger and more energy-guzzling models. Scholars and journalists have also revealed just how many humans are behind the algorithmic curtain. The AI industry is creating an entirely new class of hidden laborerscontent moderators, data labelers, transcriberswho toil away in often brutal conditions.

But not all is dark and gloomy: 2019 was the year of the greatest grassroots pushback against harmful AI from community groups, policymakers, and tech employees themselves. Several citiesincluding San Francisco and Oakland, California, and Somerville, Massachusettsbanned public use of face recognition, and proposed federal legislation could soon ban it from US public housing as well. Employees of tech giants like Microsoft, Google, and Salesforce also grew increasingly vocal against their companies use of AI for tracking migrants and for drone surveillance.

Within the AI community, researchers also doubled down on mitigating AI bias and reexamined the incentives that lead to the fields runaway energy consumption. Companies invested more resources in protecting user privacy and combating deepfakes and disinformation. Experts and policymakers worked in tandem to propose thoughtful new legislationmeant to rein in unintended consequences without dampening innovation. At the largest annual gathering in the field this year, I was both touched and surprised by how many of the keynotes, workshops, and posters focused on real-world problemsboth those created by AI and those it could help solve.

So here is my hope for 2020: that industry and academia sustain this momentum and make concrete bottom-up and top-down changes that realign AI development. While we still have time, we shouldnt lose sight of the dream animating the field. Decades ago, humans began the quest to build intelligent machines so they could one day help us solve some of our toughest challenges.

AI, in other words, is meant to help humanity prosper. Lets not forget.

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In 2020, lets stop AI ethics-washing and actually do something - MIT Technology Review

tech predictions 2020: scientists have already used it to explore our ancient origins, beer lovers have rigged taps with it to pour the perfect pint, and now humankind wants to use it to find everything out about everyone artificial intelligence is making rapid strides and theres talk of a new evolution that could fundamentally change life on our planet.

this month, LA-based studio ouchhh created a 3 billion-pixel digital monolith combining AI with data learnt from the pre-pottery neolithic period (read more)

in 2020, artificial intelligence will reach new heights. robotic scanners that serve the perfect pizza, seem pretty schoolboy in comparison to its future potential. the AI of tomorrow uses its political prowess instead of its culinary skills. it will decide who should be hired and who should be fired, who is guilty and who is innocent, deciding the fate of entire nations.

earlier this year, dominoes announced the launch of a new pizza-checkingrobot which uses a mix of AI, advanced machine learning and sensor technology to identify pizza type, even topping distribution and correct toppings (read more)

deepfakes refer to manipulated videos, or other digital representations produced by sophisticated artificial intelligence, that generate fabricated images and sounds that appear to be real. these falsified videos are becoming increasingly sophisticated and accessible, with the danger of making people believe something is real when it is not. its just in time for the 2020 US election where some fear it could be used to undermine the reputation of political candidates by making the candidate appear to say or do things that never actually occurred.

in june, a doctored video of mark zuckerberg was uploaded to instagram raising concerns over falsified content (read more)

gartner, an IT research and advisory company, reports that by 2024, the world health organization will identify online shopping as an addictive disorder. that might be in part because by then, as the same report suggests, AI which is able to identify emotions will influence more than half of the online advertisements you see. by 2020, it is predicted that 85% of customer interactions in retail will be managed by artificial intelligence. new technology could monitor customers reactions to brands, pricing and store layouts, helping retailers make decisions based on consumer responses. its kind of like market research but 24/7: if emotions read negative, it might be time to lower prices, and if shoppers appear confused, it might be time for a redesign.

just a couple of months ago, researchers at openAI developed a roboticarm that usesartificial intelligence to solve a rubiks cube one-handed (read more)

theres no hiding your emotions in the future. newly developed artificial emotional intelligence puts power in the hands of big businesses with an incentive to know exactly whats on your mind and when. it might not change the way we shop entirely, but the use of AI to detect consumer emotions will surely change the way we are sold to. imagine a hyperpersonalized shopping experience curated by humanoid sales assistants whose ability to understand what you want or need happens before youve even had time to articulate it.

in september, designboom reported on a new PSA in america that used artificial intelligence to create a composite portrait of hunger by scanning the faces of americans (read more)

the biggest concern of the future is if brands will be transparent and if so, how? consumers will demand an education on how their data is being collected and used. AI that can scan human beings for their emotional state is already being used to vet job seekers, test criminal suspects for signs of deception, and set insurance prices but just cause AI can read our emotionsshould it? research center AI now institute has called for new laws to restrict the use of emotion-detecting for fears that it is built on markedly shaky foundations. we just cant rely on AI doing its job properly when peoples lives are at stake. with AI around theres no room for human error, but theres still plenty of space for machine-made mistakes.

israel-based startup seedo is developing fully automated, commercial-scale cannabis farms for example (read more)

but its not all bad AI is set to drive sustainability in 2020 and beyond. companies will use it to measure environmental and social effects within their businesses, automatically optimizing operations for sustainability. that includes operating responsibly, reducing waste, making smarter transportation strategies.

kieron marchese I designboom

dec 27, 2019

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designboom TECH predictions 2020: AI and the third era of computing - Designboom