Inteligência Competitiva Tecnológica: Reconhecimento facial para vacas está virando realidade

Quando falamos em reconhecimento facial, a maioria de nós pensa em identificar pessoas para fins como desbloquear a tela do celular ou mesmo promover vigilância estatal em massa. Contudo, a Cargil e uma empresa irlandesa chamada Cainthus estão trabalhando em levar essa tecnologia para fazendas. A ideia é identificar vacas individualmente pelos seus rostos e acompanhar seus padrões de alimentação, bem como comportamento geral.

Com isso, o sistema desenvolvido pelas duas empresas poderá identificar com mais agilidade quando um animal está doente ou com algum outro tipo de problema que precisa de atenção. Dessa forma, fazendeiros poderiam dar atenção especial para um bovino identificado como em perigo e resolver problemas de forma mais econômica e menos estressante para os bichos.

Funcionamento

Para funcionar, o sistema precisaria que câmeras fossem instaladas em áreas de alimentação dos animais. Enquanto as vacas se alimentam, o sistema traçaria as características de seus rostos e monitoraria seu comportamento por toda parte de forma individualizada. As pessoas responsáveis por cuidar dos rebanhos receberiam atualizações em tempo real sobre os animais.

Nossa visão é transformar o modo como trazemos informações e análises para produtores de laticínios ao redor do mundo

“Nossa visão é transformar o modo como trazemos informações e análises para produtores de laticínios ao redor do mundo”, disse SriRaj Kantamneni, diretor geral dos negócios digitais da Cargil. “Dar subsídios para que nossos consumidores possam tomar decisões proativas e preditivas para melhorar a eficiência de suas fazendas, incrementar a saúde dos animais e seu bem-estar, reduzir a perda de rebanho e, consequentemente, aumentar a lucratividade”, completou.

É curioso considerar, contudo, que esta não é a primeira vez que se utiliza aplicações de inteligência artificial — como reconhecimento facial — na produção de alimentos via animal. Em 2017, a Scientific American reportou que pesquisadores usavam IA para “decodificar o cacarejado” de galinhas, também para monitorar a saúde dos bichos.

Fonte: tecmundo.com.br | @leowmuller, SOFTWARE, 

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Leia a nova edição da Revista Inteligência Competitiva, v. 7, n. 1 (2017), Janeiro – Março

Revista Inteligência Competitiva - e-ISSN:2236-210X

  1. 7, n. 1 (2017)

Sumário

Editorial

Editorial Janeiro – Março PDF
Alfredo Passos

Artigos

Comprometimento Organizacional: Estudo do Modelo Tridimensional em uma Cooperativa de Crédito na Capital Gaúcha PDF
Sheila Cristina Tavares de Oliveira Bassani, Flávia Camargo Bernardi, Mikael Dalberto, Maria Emilia Camargo, Uiliam Hahn Biegelmeyer 1-43

 

Qualidade sobre rodas: o nível de satisfação de consumidores sobre os serviços de alimentação em Food Trucks PDF
Pyetro Pergentino de Farias, Joelma Ferreira da Silva, Jammilly Mikaela Fagundes Brandão 44-71

 

Mercado de Transferências de Atletas de Futebol e o Processo de Globalização: Correlação entre os valores do Transfermarkt e do jogo eletrônico Football Manager PDF
Eric Matheus Rocha Lima, Ivan Wallan Tertuliano, André Luis Aroni, Afonso Antonio Machado, Carlos Norberto Fischer 72-90

 

Fusão entre ALL – América Latina Logística e Rumo Logística Operadora Multimodal: Uma Análise a partir da Visão Baseada em Recursos. PDF
Anderson Aquiles Viana Leite, Cristiane Teresinha Agnolin, Carlos Eduardo Carvalho 91-128

 

MAPEAMENTO DA PRODUÇÃO CIENTÍFICA BRASILEIRA SOBRE APRENDIZAGEM ORGANIZACIONAL: UM ESTUDO NA BASE SPELL PDF
Mayara Pires Zanotto, Juliano Uecker de Lima, Diego Luís Bertollo, Adrieli Pereira Radaeli, Fabiano Larentis, Eric Henry Charles Dorion 129-153

 

Perspectivas Teóricas do Mainstream da Administração Estratégica: Uma Meta-Síntese PDF
Jonathan Simões Freitas, Júlia Araújo Tiso Mudrik, Paulo Vítor Guerra, Lin Chih Cheng, Carlos Alberto Gonçalves 154-182

Estudo de Caso

IN SEARCH OF INNOVATION: LOOKING OUTSIDE THE COMPANY PDF (ENGLISH)
Celso dos Santos Malachias, Luiz Carlos Di Serio 183-214

Relato Técnico-Científico

Notas críticas acerca das Estruturas Organizacionais Competitivas PDF
Luciano augusto toledo, Guilherme de Farias Shiraishi, Conceiçao Aparecida Barbosa 215-231

 

EMPREENDEDORISMO DIGITAL: UM ESTUDO SOBRE O USO DA TECNOLOGIA COMO ALAVANCAGEM DE NEGÓCIOS EM UMA STARTUP EDUCACIONAL PDF
Alexandre Albuquerque Domingues, Kathryn Floyd-Wheeler 232-261

 

Balanceamento da remuneração estratégica da força de vendas como vantagem competitiva PDF
Viviana Beatriz Huespe Aquino Vieira, Claudio Antonio Rojo 262-273

 

Revista Inteligência Competitiva: CHAMADA PARA PUBLICAÇÃO

A Revista Inteligência Competitiva recebe artigos, relatos técnicos, de experiências, de pesquisas, entrevistas, resenhas e estudos de casos em regime de fluxo contínuo, ou seja, os materiais são avaliados à medida que são recebidos.

A submissão é realizada pelo link: aqui

Nós agradecemos muito a colaboração de todos e contamos com os autores que vêm contribuindo com pesquisas voltadas à Inteligência Competitiva.

Prof. Dr. Alfredo Passos
Editor Chefe

Estudo de Caso e Relato Técnico-Científico: Revista Inteligência Competitiva

Estudo de Caso

MODELO DE INTELIGÊNCIA COMPETITIVA: UMA PROPOSTA PARA MONITORAMENTO E PROSPECÇÃO DE CLIENTES NA COMPANHIA ENERGÉTICA DE MINAS GERAIS – CEMIG PDF
Frederico Giffoni de Carvalho Dutra 224-243
DIAGNÓSTICO E PROPOSTA DE ADEQUAÇÕES PARA MELHORIA NA ADMINISTRACAO DE UMA EMPRESA INDIVIDUAL DE CONFECÇÃO PDF
Leonardo de Carvalho, Claudio Antonio Rojo 244-258

Inteligência Competitiva Cenários: 9 motivos para você repensar a sua profissão

Em poucos anos a sua profissão pode mudar radicalmente, é provável que a empresa onde você trabalha nem mais exista e que você nem consiga mais se empregar com o que você sabe hoje.
Veja 9 informações surpreendentes que farão você repensar o seu futuro no trabalho.
Ahhh… é melhor ouvir sentado porque tem risco de você cair da cadeira.

Fonte: Mauro SeguraPublicado em 1 de mai de 2016.

Vídeos espetaculares do YouTube mostrando cenas cotidianas do início do século, onde algumas cenas foram retiradas montagem desse vídeo.
https://www.youtube.com/channel/UCZ2j…
https://www.youtube.com/channel/UCEfx…
https://www.youtube.com/watch?v=Uz4Am…
https://www.youtube.com/watch?v=684n8…
https://www.youtube.com/watch?v=RN7ft…
https://www.youtube.com/watch?v=ZPP0a…
https://www.youtube.com/watch?v=EWg2x…
https://www.youtube.com/watch?v=OebeM…
https://www.youtube.com/watch?v=iEGbv…
https://www.youtube.com/watch?v=IrNKD…
https://www.youtube.com/watch?v=fcfMj…

Inteligência Competitiva Tecnológica: Inteligência Artificial está chegando

A inteligência artificial e a computação cognitiva estão chegando em alta velocidade e vão invadir o nosso dia a dia. Conheça aqui o Dino, o brinquedo cognitivo que tem conversas inteligentes com crianças.

Dino é um Cognitoy da linha de brinquedos inteligentes da Elemental Path. Ele utiliza o Watson, tecnologia cognitiva capaz de interagir, entender linguagens, aprender novas habilidades e raciocinar como seres humanos.

Dino é o exemplo perfeito da era da inteligência artificial e da computação cognitiva em nossas mãos.

Hoje ele fala somente inglês e é voltado para crianças, imagine o Dino falando diversos idiomas e sendo expert em várias áreas do conhecimento humano.

A sociedade vai mudar radicalmente, o mercado de trabalho também, com o surgimento e a transformação de muitas profissões.

Fonte: Mauro SeguraPublicado em 17 de jul de 2016

Competitive Intelligence: Equipping people to stay ahead of technological change

WHEN education fails to keep pace with technology, the result is inequality. Without the skills to stay useful as innovations arrive, workers suffer—and if enough of them fall behind, society starts to fall apart. That fundamental insight seized reformers in the Industrial Revolution, heralding state-funded universal schooling. Later, automation in factories and offices called forth a surge in college graduates. The combination of education and innovation, spread over decades, led to a remarkable flowering of prosperity.

Today robotics and artificial intelligence call for another education revolution. This time, however, working lives are so lengthy and so fast-changing that simply cramming more schooling in at the start is not enough. People must also be able to acquire new skills throughout their careers.

Unfortunately, as our special report in this issue sets out, the lifelong learning that exists today mainly benefits high achievers—and is therefore more likely to exacerbate inequality than diminish it. If 21st-century economies are not to create a massive underclass, policymakers urgently need to work out how to help all their citizens learn while they earn. So far, their ambition has fallen pitifully short.

Machines or learning

The classic model of education—a burst at the start and top-ups through company training—is breaking down. One reason is the need for new, and constantly updated, skills. Manufacturing increasingly calls for brain work rather than metal-bashing (see Briefing). The share of the American workforce employed in routine office jobs declined from 25.5% to 21% between 1996 and 2015. The single, stable career has gone the way of the Rolodex.

Pushing people into ever-higher levels of formal education at the start of their lives is not the way to cope. Just 16% of Americans think that a four-year college degree prepares students very well for a good job. Although a vocational education promises that vital first hire, those with specialised training tend to withdraw from the labour force earlier than those with general education—perhaps because they are less adaptable.

At the same time on-the-job training is shrinking. In America and Britain it has fallen by roughly half in the past two decades. Self-employment is spreading, leaving more people to take responsibility for their own skills. Taking time out later in life to pursue a formal qualification is an option, but it costs money and most colleges are geared towards youngsters.

The market is innovating to enable workers to learn and earn in new ways. Providers from General Assembly to Pluralsight are building businesses on the promise of boosting and rebooting careers. Massive open online courses (MOOCs) have veered away from lectures on Plato or black holes in favour of courses that make their students more employable. At Udacity and Coursera self-improvers pay for cheap, short programmes that bestow “microcredentials” and “nanodegrees” in, say, self-driving cars or the Android operating system. By offering degrees online, universities are making it easier for professionals to burnish their skills. A single master’s programme from Georgia Tech could expand the annual output of computer-science master’s degrees in America by close to 10%.

Such efforts demonstrate how to interleave careers and learning. But left to its own devices, this nascent market will mainly serve those who already have advantages. It is easier to learn later in life if you enjoyed the classroom first time around: about 80% of the learners on Coursera already have degrees. Online learning requires some IT literacy, yet one in four adults in the OECD has no or limited experience of computers. Skills atrophy unless they are used, but many low-end jobs give workers little chance to practise them.

Shampoo technician wanted

If new ways of learning are to help those who need them most, policymakers should be aiming for something far more radical. Because education is a public good whose benefits spill over to all of society, governments have a vital role to play—not just by spending more, but also by spending wisely.

Lifelong learning starts at school. As a rule, education should not be narrowly vocational. The curriculum needs to teach children how to study and think. A focus on “metacognition” will make them better at picking up skills later in life.

But the biggest change is to make adult learning routinely accessible to all. One way is for citizens to receive vouchers that they can use to pay for training. Singapore has such “individual learning accounts”; it has given money to everyone over 25 to spend on any of 500 approved courses. So far each citizen has only a few hundred dollars, but it is early days.

Courses paid for by taxpayers risk being wasteful. But industry can help by steering people towards the skills it wants and by working with MOOCs and colleges to design courses that are relevant. Companies can also encourage their staff to learn. AT&T, a telecoms firm which wants to equip its workforce with digital skills, spends $30m a year on reimbursing employees’ tuition costs. Trade unions can play a useful role as organisers of lifelong learning, particularly for those—workers in small firms or the self-employed—for whom company-provided training is unlikely. A union-run training programme in Britain has support from political parties on the right and left.

To make all this training worthwhile, governments need to slash the licensing requirements and other barriers that make it hard for newcomers to enter occupations. Rather than asking for 300 hours’ practice to qualify to wash hair, for instance, the state of Tennessee should let hairdressers decide for themselves who is the best person to hire.

Not everyone will successfully navigate the shifting jobs market. Those most at risk of technological disruption are men in blue-collar jobs, many of whom reject taking less “masculine” roles in fast-growing areas such as health care. But to keep the numbers of those left behind to a minimum, all adults must have access to flexible, affordable training. The 19th and 20th centuries saw stunning advances in education. That should be the scale of the ambition today.

Competitive Intelligence: What It Takes to Stay Ahead of the Competition

Matt Palmquist

Bottom Line: For companies, sustaining a consistently high level of performance requires unique capabilities that may differ sharply from the strategies they used to succeed in the first place.

Leading firms set themselves apart by achieving a high level of performance and meeting or exceeding consumers’ expectations relative to the competition. It’s usually an arduous, years-long process. But sustaining that level of performance is a completely different challenge — one that few companies can overcome in the modern business landscape.

There’s plenty of substantive advice available on how to attain high-quality performance in the first place. Researchers have variously touted the ability of firms to create barriers to entry for competitors, for example, or to draw (pdf) on unique capabilities to differentiate themselves. But rivalslearn quickly, once-novel strategies can eventually be duplicated, mistakes can be made, and complacency can set in. What it takes to sustain top-quality performance, therefore, is also deserving of study — but it has received comparatively little attention from researchers. Indeed, most analysts have implicitly assumed that the capabilities required to attain high-quality performance are the same as those needed to sustain it.

A new study aims to shed light on the issue by analyzing which capabilities enable companies to sustain a consistent and high level of performance. It should be noted that for the study, the quality level and consistency of performance are two distinct concepts. Whereas a firm with a high quality level outshines its competitors in the short term,consistency involves maintaining that high level with minimal variance for a five-year period.

The authors analyzed data on 147 business units within large companies in the manufacturing sector that were based in either the U.S. or Taiwan. The reason to zero in on U.S. firms is obvious: They tend to set the tone for the global economy. The researchers chose to study Taiwanese firms as well in order to consider the differences between Eastern and Western cultures in their management approaches and assess any impact on performance. (In the final analysis, no significant differences between them appeared.) Taiwan also has a well-established reputation for advanced manufacturing.

To assemble a sample, the authors reached out to executives whose companies had won awards or earned acknowledgment from associations dedicated to recognizing high-performing businesses. The authors conducted surveys with quality or operations managers at the firms, who could speak to the specific strategies employed, and with general managers, who could field questions about the firm’s overall performance and the nuances of its business environment. For a subset of companies, the authors also obtained financial-performance data from the business unit’s accountant as well as internal audits that gauged the quality of its products and services.

After controlling for firm size, competitive intensity (pdf) of a given industry, and level of uncertainty faced — in the form of rapid technological developments or changing market conditions — the authors found that four particular capabilities emerged as integral to sustaining high-quality performance:

Improvement. This capability was defined as a firm’s ability to make incremental product or service upgrades, or to reduce production costs.

Innovation. Defined as how strong a company was at developing new products and entering new markets.

Sensing of weak signals. Defined as how well a company can focus on potential banana peels in order to improve overall performance, including analyzing mistakes, actively searching out production anomalies, and being aware of potential problems in the surrounding business environment.

Responsiveness. Defined as a business’s ability to solve problems that crop up unexpectedly and to use specialized expertise to counter those complications.

But these capabilities influenced different aspects of sustaining high performance, the authors found. For example, innovation capabilities primarily help firms maintain a certain level of quality, whereas the capacity for improvement affects mostly the consistency component. That’s probably because innovations are typically unique events that meet customers’ immediate needs and establish a certain level of quality, whereas incremental improvements are geared toward ensuring the long-term reliability of products and services, which translates into consistency.

Meanwhile, a firm’s capability for responsiveness had no significant effect on consistency, but had a decided positive impact on its level of quality — presumably because responding to quality-related problems quickly and efficiently is also a way of exceeding customers’ expectations in a one-off way.

Sensing of weak signals had a strong positive effect on consistency, but a moderately negative impact on the level of quality. This suggests a potential trade-off, the authors note, because maintaining both a high quality level and consistency is essential to sustaining performance. The authors speculate that a focus on sensing weak signals mandates that firms spend a lot of time collecting data and analyzing the occasional blip, which could cause them to get mired in minutiae and distract them from the more important tasks associated with sustaining a high level of performance. Although the benefits may pay off over time, a concentration on preventing failures rather than seeking out successes could also lead firms to take a short-term view and be overly conservative, too concerned with simply surviving, and to thus shy away from taking chances.

Intriguingly, the capabilities that increase consistency (improvement and sensing of weaknesses) are unaffected by the level of competitive intensity or uncertainty surrounding a firm, whereas those that affect the level of performance (innovation and responsiveness) depend heavily on the external context, the authors found. Presumably, the value of innovation and responsiveness is higher in the face of unanticipated external shocks, whereas improvement and sensitivity to failure are capabilities that are more internally oriented. As a result, firms may need to invest in certain capabilities more than others, depending on their business environment.

Source:An Empirical Investigation in Sustaining High-Quality Performance,” by Hung-Chung Su (University of Michigan–Dearborn) and Kevin Linderman (University of Minnesota), Decision Sciences, Oct. 2016, vol. 47, no. 5

Source/author:  strategy+business, S+B BLOGSPublished: January 5, 2017, 

Matt Palmquist is a freelance business journalist based in Oakland, Calif.

Inteligência Competitiva Tecnológica: How machines learned to speak human language

THIS past Christmas, millions of people will have opened boxes containing gadgets with a rapidly improving ability to use human language. Amazon’s Echo device, featuring a digital assistant called Alexa, is now present in over 5m homes. The Echo is a cylindrical desktop computer with no interface apart from voice. Ask Alexa for the weather, to play music, to order a taxi, to tell you about your commute or to tell a corny joke, and she will comply. The voice-driven digital assistants from America’s computer giants (Google Assistant, Microsoft’s Cortana and Apple’s Siri) have also vastly improved. How did computers tackle the problems of human language?

Once, the idea was to teach machines rules—for example, in translation, a set of grammar rules for breaking down the meaning of the source language, and another set for reproducing the meaning in the target language. But after a burst of optimism in the 1950s, such systems could not be made to work on complex new sentences; the rules-based approach would not scale up. Funding for human-language technologies went into hibernation for decades, until a renaissance in the 1980s.

In effect, language technologies teach themselves, via a form of pattern-matching. For speech recognition, computers are fed sound files on the one hand, and human-written transcriptions on the other. The system learns to predict which sounds should result in what transcriptions. In translation, the training data are source-language texts and human-made translations. The system learns to match the patterns between them. One thing that improves both speech recognition and translation is a “language model”—a bank of knowledge about what (for example) English sentences tend to look like. This narrows the systems’ guesswork considerably. Three things have made this approach take a big leap forward recently: First, computers are far more powerful. Second, they can learn from huge and growing stores of data, whether publicly available on the internet or privately gathered by firms. Third, so-called “deep learning”, which uses digital neural networks with several layers of digital “neurons” and connections between them, have become very good at learning from example.

All this means that computers are now impressively competent at handling spoken requests that require a narrowly defined reply. “What’s the temperature going to be in London tomorrow?” is simple (To be fair, you don’t need to be a computer to know it is going to rain in London tomorrow). Users can even ask in more natural ways, such as, “Should I carry an umbrella to London tomorrow?” (Digital assistants learn continually from the different ways people ask questions.) But ask a wide-open question (“Is there anything fun and inexpensive to do in London tomorrow?”) and you will usually just get a list of search-engine results. As machine learning improves, and as users let their gadgets learn more about them specifically, such answers will become more useful. This has implications that trouble privacy advocates, but if the past few years of mobile-phone use are any indication, consumers will be sufficiently delighted by the new features to make the trade-off.

Read the Technology Quarterly report on language and machines post here

Source: The Economist explains, Jan 11th 2017, 11:36 BY R.L.G.

Inteligência Competitiva Tecnológica: Language: Finding a voice – TECHNOLOGY QUARTERLY – The Economist

Technology Quarterly, Jan 7th, 2017

Finding a voice What language technology can and can’t do
I hear you Speech recognition
Hasta la vista, robot voice
Speech synthesis  Beyond Babel
The limits of computer translations
What are you talking about? The meaning of speech still eludes machines
Where humans still beat computers Brain scan: Terry Winograd
For my next trick Coming to grips with voice technology

Computers have got much better at translation, voice recognition and speech synthesis, says Lane Greene. But they still don’t understand the meaning of language

I’M SORRY, Dave. I’m afraid I can’t do that.” With chilling calm, HAL 9000, the on-board computer in “2001: A Space Odyssey”, refuses to open the doors to Dave Bowman, an astronaut who had ventured outside the ship. HAL’s decision to turn on his human companion reflected a wave of fear about intelligent computers.

When the film came out in 1968, computers that could have proper conversations with humans seemed nearly as far away as manned flight to Jupiter. Since then, humankind has progressed quite a lot farther with building machines that it can talk to, and that can respond with something resembling natural speech. Even so, communication remains difficult. If “2001” had been made to reflect the state of today’s language technology, the conversation might have gone something like this: “Open the pod bay doors, Hal.” “I’m sorry, Dave. I didn’t understand the question.” “Open the pod bay doors, Hal.” “I have a list of eBay results about pod doors, Dave.”

Creative and truly conversational computers able to handle the unexpected are still far off. Artificial-intelligence (AI) researchers can only laugh when asked about the prospect of an intelligent HAL, Terminator or Rosie (the sassy robot housekeeper in “The Jetsons”). Yet although language technologies are nowhere near ready to replace human beings, except in a few highly routine tasks, they are at last about to become good enough to be taken seriously. They can help people spend more time doing interesting things that only humans can do. After six decades of work, much of it with disappointing outcomes, the past few years have produced results much closer to what early pioneers had hoped for.

Speech recognition has made remarkable advances. Machine translation, too, has gone from terrible to usable for getting the gist of a text, and may soon be good enough to require only modest editing by humans. Computerised personal assistants, such as Apple’s Siri, Amazon’s Alexa, Google Now and Microsoft’s Cortana, can now take a wide variety of questions, structured in many different ways, and return accurate and useful answers in a natural-sounding voice. Alexa can even respond to a request to “tell me a joke”, but only by calling upon a database of corny quips. Computers lack a sense of humour.

When Apple introduced Siri in 2011 it was frustrating to use, so many people gave up. Only around a third of smartphone owners use their personal assistants regularly, even though 95% have tried them at some point, according to Creative Strategies, a consultancy. Many of those discouraged users may not realise how much they have improved.

In 1966 John Pierce was working at Bell Labs, the research arm of America’s telephone monopoly. Having overseen the team that had built the first transistor and the first communications satellite, he enjoyed a sterling reputation, so he was asked to take charge of a report on the state of automatic language processing for the National Academy of Sciences. In the period leading up to this, scholars had been promising automatic translation between languages within a few years.

But the report was scathing. Reviewing almost a decade of work on machine translation and automatic speech recognition, it concluded that the time had come to spend money “hard-headedly toward important, realistic and relatively short-range goals”—another way of saying that language-technology research had overpromised and underdelivered. In 1969 Pierce wrote that both the funders and eager researchers had often fooled themselves, and that “no simple, clear, sure knowledge is gained.” After that, America’s government largely closed the money tap, and research on language technology went into hibernation for two decades.

The story of how it emerged from that hibernation is both salutary and surprisingly workaday, says Mark Liberman. As professor of linguistics at the University of Pennsylvania and head of the Linguistic Data Consortium, a huge trove of texts and recordings of human language, he knows a thing or two about the history of language technology. In the bad old days researchers kept their methods in the dark and described their results in ways that were hard to evaluate. But beginning in the 1980s, Charles Wayne, then at America’s Defence Advanced Research Projects Agency, encouraged them to try another approach: the “common task”.

Step by step

Researchers would agree on a common set of practices, whether they were trying to teach computers speech recognition, speaker identification, sentiment analysis of texts, grammatical breakdown, language identification, handwriting recognition or anything else. They would set out the metrics they were aiming to improve on, share the data sets used to train their software and allow their results to be tested by neutral outsiders. That made the process far more transparent. Funding started up again and language technologies began to improve, though very slowly.

Many early approaches to language technology—and particularly translation—got stuck in a conceptual cul-de-sac: the rules-based approach. In translation, this meant trying to write rules to analyse the text of a sentence in the language of origin, breaking it down into a sort of abstract “interlanguage” and rebuilding it according to the rules of the target language.

These approaches showed early promise. But language is riddled with ambiguities and exceptions, so such systems were hugely complicated and easily broke down when tested on sentences beyond the simple set they had been designed for. Nearly all language technologies began to get a lot better with the application of statistical methods, often called a “brute force” approach.

This relies on software scouring vast amounts of data, looking for patterns and learning from precedent. For example, in parsing language (breaking it down into its grammatical components), the software learns from large bodies of text that have already been parsed by humans. It uses what it has learned to make its best guess about a previously unseen text. In machine translation, the software scans millions of words already translated by humans, again looking for patterns. In speech recognition, the software learns from a body of recordings and the transcriptions made by humans.

Thanks to the growing power of processors, falling prices for data storage and, most crucially, the explosion in available data, this approach eventually bore fruit. Mathematical techniques that had been known for decades came into their own, and big companies with access to enormous amounts of data were poised to benefit. People who had been put off by the hilariously inappropriate translations offered by online tools like BabelFish began to have more faith in Google Translate.

Apple persuaded millions of iPhone users to talk not only on their phones but to them. The final advance, which began only about five years ago, came with the advent of deep learning through digital neural networks (DNNs).

These are often touted as having qualities similar to those of the human brain: “neurons” are connected in software, and connections can become stronger or weaker in the process of learning. But Nils Lenke, head of research for Nuance, a language-technology company, explains matter-of-factly that “DNNs are just another kind of mathematical model,” the basis of which had been well understood for decades. What changed was the hardware being used.

Almost by chance, DNN researchers discovered that the graphical processing units (GPUs) used to render graphics fluidly in applications like video games were also brilliant at handling neural networks. In computer graphics, basic small shapes move according to fairly simple rules, but there are lots of shapes and many rules, requiring vast numbers of simple calculations.

The same GPUs are used to fine-tune the weights assigned to “neurons” in DNNs as they scour data to learn. The technique has already produced big leaps in quality for all kinds of deep learning, including deciphering handwriting, recognising faces and classifying images. Now they are helping to improve all manner of language technologies, often bringing enhancements of up to 30%.

That has shifted language technology from usable at a pinch to really rather good. But so far no one has quite worked out what will move it on from merely good to reliably great.

Computers have made huge strides in understanding human speech

WHEN a person speaks, air is forced out through the lungs, making the vocal chords vibrate, which sends out characteristic wave patterns through the air. The features of the sounds depend on the arrangement of the vocal organs, especially the tongue and the lips, and the characteristic nature of the sounds comes from peaks of energy in certain frequencies. The vowels have frequencies called “formants”, two of which are usually enough to differentiate one vowel from another. For example, the vowel in the English word “fleece” has its first two formants at around 300Hz and 3,000Hz. Consonants have their own characteristic features.

In principle, it should be easy to turn this stream of sound into transcribed speech. As in other language technologies, machines that recognise speech are trained on data gathered earlier. In this instance, the training data are sound recordings transcribed to text by humans, so that the software has both a sound and a text input. All it has to do is match the two. It gets better and better at working out how to transcribe a given chunk of sound in the same way as humans did in the training data. The traditional matching approach was a statistical technique called a hidden Markov model (HMM), making guesses based on what was done before. More recently speech recognition has also gained from deep learning.

English has about 44 “phonemes”, the units that make up the sound system of a language. P and b are different phonemes, because they distinguish words like pat and bat. But in English p with a puff of air, as in “party”, and p without a puff of air, as in “spin”, are not different phonemes, though they are in other languages. If a computer hears the phonemes s, p, i and n back to back, it should be able to recognise the word “spin”.

But the nature of live speech makes this difficult for machines. Sounds are not pronounced individually, one phoneme after the other; they mostly come in a constant stream, and finding the boundaries is not easy. Phonemes also differ according to the context. (Compare the l sound at the beginning of “light” with that at the end of “full”.) Speakers differ in timbre and pitch of voice, and in accent. Conversation is far less clear than careful dictation. People stop and restart much more often than they realise.

All the same, technology has gradually mitigated many of these problems, so error rates in speech-recognition software have fallen steadily over the years—and then sharply with the introduction of deep learning. Microphones have got better and cheaper. With ubiquitous wireless internet, speech recordings can easily be beamed to computers in the cloud for analysis, and even smartphones now often have computers powerful enough to carry out this task.

Bear arms or bare arms?

Perhaps the most important feature of a speech-recognition system is its set of expectations about what someone is likely to say, or its “language model”. Like other training data, the language models are based on large amounts of real human speech, transcribed into text. When a speech-recognition system “hears” a stream of sound, it makes a number of guesses about what has been said, then calculates the odds that it has found the right one, based on the kinds of words, phrases and clauses it has seen earlier in the training text.

At the level of phonemes, each language has strings that are permitted (in English, a word may begin with str-, for example) or banned (an English word cannot start with tsr-). The same goes for words. Some strings of words are more common than others. For example, “the” is far more likely to be followed by a noun or an adjective than by a verb or an adverb. In making guesses about homophones, the computer will have remembered that in its training data the phrase “the right to bear arms” came up much more often than “the right to bare arms”, and will thus have made the right guess.

Training on a specific speaker greatly cuts down on the software’s guesswork. Just a few minutes of reading training text into software like Dragon Dictate, made by Nuance, produces a big jump in accuracy. For those willing to train the software for longer, the improvement continues to something close to 99% accuracy (meaning that of each hundred words of text, not more than one is wrongly added, omitted or changed). A good microphone and a quiet room help.

Advance knowledge of what kinds of things the speaker might be talking about also increases accuracy. Words like “phlebitis” and “gastrointestinal” are not common in general discourse, and uncommon words are ranked lower in the probability tables the software uses to guess what it has heard. But these words are common in medicine, so creating software trained to look out for such words considerably improves the result. This can be done by feeding the system a large number of documents written by the speaker whose voice is to be recognised; common words and phrases can be extracted to improve the system’s guesses.

As with all other areas of language technology, deep learning has sharply brought down error rates. In October Microsoft announced that its latest speech-recognition system had achieved parity with human transcribers in recognising the speech in the Switchboard Corpus, a collection of thousands of recorded conversations in which participants are talking with a stranger about a randomly chosen subject.

Error rates on the Switchboard Corpus are a widely used benchmark, so claims of quality improvements can be easily compared. Fifteen years ago quality had stalled, with word-error rates of 20-30%. Microsoft’s latest system, which has six neural networks running in parallel, has reached 5.9% (see chart), the same as a human transcriber’s. Xuedong Huang, Microsoft’s chief speech scientist, says that he expected it to take two or three years to reach parity with humans. It got there in less than one.

The improvements in the lab are now being applied to products in the real world. More and more cars are being fitted with voice-activated controls of various kinds; the vocabulary involved is limited (there are only so many things you might want to say to your car), which ensures high accuracy. Microphones—or often arrays of microphones with narrow fields of pick-up—are getting better at identifying the relevant speaker among a group.

Some problems remain. Children and elderly speakers, as well as people moving around in a room, are harder to understand. Background noise remains a big concern; if it is different from that in the training data, the software finds it harder to generalise from what it has learned. So Microsoft, for example, offers businesses a product called CRIS that lets users customise speech-recognition systems for the background noise, special vocabulary and other idiosyncrasies they will encounter in that particular environment. That could be useful anywhere from a noisy factory floor to a care home for the elderly.

But for a computer to know what a human has said is only a beginning. Proper interaction between the two, of the kind that comes up in almost every science-fiction story, calls for machines that can speak back.

Hasta la vista, robot voice 

Machines are starting to sound more like humans

“I’LL be back.” “Hasta la vista, baby.” Arnold Schwarzenegger’s Teutonic drone in the “Terminator” films is world-famous. But in this instance film-makers looking into the future were overly pessimistic. Some applications do still feature a monotonous “robot voice”, but that is changing fast.
Creating speech is roughly the inverse of understanding it. Again, it requires a basic model of the structure of speech. What are the sounds in a language, and how do they combine? What words does it have, and how do they combine in sentences? These are well-understood questions, and most systems can now generate sound waves that are a fair approximation of human speech, at least in short bursts.
Heteronyms require special care. How should a computer pronounce a word like “lead”, which can be a present-tense verb or a noun for a heavy metal, pronounced quite differently? Once again a language model can make accurate guesses: “Lead us not into temptation” can be parsed for its syntax, and once the software has worked out that the first word is almost certainly a verb, it can cause it to be pronounced to rhyme with “reed”, not “red”.
Traditionally, text-to-speech models have been “concatenative”, consisting of very short segments recorded by a human and then strung together as in the acoustic model described above. More recently, “parametric” models have been generating raw audio without the need to record a human voice, which makes
these systems more flexible but less natural-sounding.
DeepMind, an artificial-intelligence company bought by Google in 2014, has announced a new way of synthesising speech, again using deep neural networks. The network is trained on recordings of people talking, and on the texts that match what they say. Given a text to reproduce as speech, it churns out a far more fluent and natural-sounding voice than the best concatenative and parametric approaches.
The last step in generating speech is giving it prosody—generally, the modulation of speed, pitch and volume to convey an extra (and critical) channel of meaning. In English, “a German teacher”, with the stress on “teacher”, can teach anything but must be German. But “a German teacher” with the emphasis on “German” is usually a teacher of German (and need not be German). Words like prepositions and conjunctions are not usually stressed. Getting machines to put the stresses in the correct places is about 50% solved, says Mark Liberman of the University of Pennsylvania.
Many applications do not require perfect prosody. A satellite-navigation system giving instructions on where to turn uses just a small number of sentence patterns, and prosody is not important. The same goes for most single-sentence responses given by a virtual assistant on a smartphone.
But prosody matters when someone is telling a story. Pitch, speed and volume can be used to pass quickly over things that are already known, or to build interest and tension for new information. Myriad tiny clues communicate the speaker’s attitude to his subject. The phrase “a German teacher”, with stress on the word “German”, may, in the context of a story, not be a teacher of German, but a teacher being explicitly contrasted with a teacher who happens to be French or British.
Text-to-speech engines are not much good at using context to provide such accentuation, and where they do, it rarely extends beyond a single sentence. When Alexa, the assistant in Amazon’s Echo device, reads a news story, her prosody is jarringly un-humanlike. Talking computers have yet to learn how to make humans want to listen.

Machine translation: Beyond Babel

Computer translations have got strikingly better, but still need human input

IN “STAR TREK” it was a hand-held Universal Translator; in “The Hitchhiker’s Guide to the Galaxy” it was the Babel Fish popped conveniently into the ear. In science fiction, the meeting of distant civilisations generally requires some kind of device to allow them to talk. High-quality automated translation seems even more magical than other kinds of language technology because many humans struggle to speak more than one language, let alone translate from one to another.

The idea has been around since the 1950s, and computerised translation is still known by the quaint moniker “machine translation” (MT). It goes back to the early days of the cold war, when American scientists were trying to get computers to translate from Russian. They were inspired by the code-breaking successes of the second world war, which had led to the development of computers in the first place. To them, a scramble of Cyrillic letters on a page of Russian text was just a coded version of English, and turning it into English was just a question of breaking the code.

Scientists at IBM and Georgetown University were among those who thought that the problem would be cracked quickly. Having programmed just six rules and a vocabulary of 250 words into a computer, they gave a demonstration in New York on January 7th 1954 and proudly produced 60 automated translations, including that of “Mi pyeryedayem mislyi posryedstvom ryechyi,” which came out correctly as “We transmit thoughts by means of speech.” Leon Dostert of Georgetown, the lead scientist, breezily predicted that fully realised MT would be “an accomplished fact” in three to five years.

Instead, after more than a decade of work, the report in 1966 by a committee chaired by John Pierce, mentioned in the introduction to this report, recorded bitter disappointment with the results and urged researchers to focus on narrow, achievable goals such as automated dictionaries. Government-sponsored work on MT went into near-hibernation for two decades. What little was done was carried out by private companies. The most notable of them was Systran, which provided rough translations, mostly to America’s armed forces.

La plume de mon ordinateur

The scientists got bogged down by their rules-based approach. Having done relatively well with their six-rule system, they came to believe that if they programmed in more rules, the system would become more sophisticated and subtle. Instead, it became more likely to produce nonsense. Adding extra rules, in the modern parlance of software developers, did not “scale”.

Besides the difficulty of programming grammar’s many rules and exceptions, some early observers noted a conceptual problem. The meaning of a word often depends not just on its dictionary definition and the grammatical context but the meaning of the rest of the sentence. Yehoshua Bar-Hillel, an Israeli MT pioneer, realised that “the pen is in the box” and “the box is in the pen” would require different translations for “pen”: any pen big enough to hold a box would have to be an animal enclosure, not a writing instrument.

How could machines be taught enough rules to make this kind of distinction? They would have to be provided with some knowledge of the real world, a task far beyond the machines or their programmers at the time. Two decades later, IBM stumbled on an approach that would revive optimism about MT. Its Candide system was the first serious attempt to use statistical probabilities rather than rules devised by humans for translation. Statistical, “phrase-based” machine translation, like speech recognition, needed training data to learn from. Candide used Canada’s Hansard, which publishes that country’s parliamentary debates in French and English, providing a huge amount of data for that time. The phrase-based approach would ensure that the translation of a word would take the surrounding words properly into account.

But quality did not take a leap until Google, which had set itself the goal of indexing the entire internet, decided to use those data to train its translation engines; in 2007 it switched from a rules-based engine (provided by Systran) to its own statistics-based system. To build it, Google trawled about a trillion web pages, looking for any text that seemed to be a translation of another—for example, pages designed identically but with different words, and perhaps a hint such as the address of one page ending in /en and the other ending in /fr. According to Macduff Hughes, chief engineer on Google Translate, a simple approach using vast amounts of data seemed more promising than a clever one with fewer data.

Training on parallel texts (which linguists call corpora, the plural of corpus) creates a “translation model” that generates not one but a series of possible translations in the target language. The next step is running these possibilities through a monolingual language model in the target language. This is, in effect, a set of expectations about what a well-formed and typical sentence in the target language is likely to be. Single-language models are not too hard to build. (Parallel human-translated corpora are hard to come by; large amounts of monolingual training data are not.) As with the translation model, the language model uses a brute-force statistical approach to learn from the training data, then ranks the outputs from the translation model in order of plausibility.

Statistical machine translation rekindled optimism in the field. Internet users quickly discovered that Google Translate was far better than the rules-based online engines they had used before, such as BabelFish. Such systems still make mistakes—sometimes minor, sometimes hilarious, sometimes so serious or so many as to make nonsense of the result. And language pairs like Chinese-English, which are unrelated and structurally quite different, make accurate translation harder than pairs of related languages like English and German. But more often than not, Google Translate and its free online competitors, such as Microsoft’s Bing Translator, offer a usable approximation.

Such systems are set to get better, again with the help of deep learning from digital neural networks. The Association for Computational Linguistics has been holding workshops on MT every summer since 2006. One of the events is a competition between MT engines turned loose on a collection of news text. In August 2016, in Berlin, neural-net-based MT systems were the top performers (out of 102), a first.

Now Google has released its own neural-net-based engine for eight language pairs, closing much of the quality gap between its old system and a human translator. This is especially true for closely related languages (like the big European ones) with lots of available training data. The results are still distinctly imperfect, but far smoother and more accurate than before. Translations between English and (say) Chinese and Korean are not as good yet, but the neural system has brought a clear improvement here too.

The Coca-Cola factor

Neural-network-based translation actually uses two networks. One is an encoder. Each word of an input sentence is converted into a multidimensional vector (a series of numerical values), and the encoding of each new word takes into account what has happened earlier in the sentence. Marcello Federico of Italy’s Fondazione Bruno Kessler, a private research organisation, uses an intriguing analogy to compare neural-net translation with the phrase-based kind. The latter, he says, is like describing Coca-Cola in terms of sugar, water, caffeine and other ingredients. By contrast, the former encodes features such as liquidness, darkness, sweetness and fizziness.

Once the source sentence is encoded, a decoder network generates a word-for-word translation, once again taking account of the immediately preceding word. This can cause problems when the meaning of words such as pronouns depends on words mentioned much earlier in a long sentence. This problem is mitigated by an “attention model”, which helps maintain focus on other words in the sentence outside the immediate context.

Neural-network translation requires heavy-duty computing power, both for the original training of the system and in use. The heart of such a system can be the GPUs that made the deep-learning revolution possible, or specialised hardware like Google’s Tensor Processing Units (TPUs). Smaller translation companies and researchers usually rent this kind of processing power in the cloud. But the data sets used in neural-network training do not need to be as extensive as those for phrase-based systems, which should give smaller outfits a chance to compete with giants like Google.

Fully automated, high-quality machine translation is still a long way off. For now, several problems remain. All current machine translations proceed sentence by sentence. If the translation of such a sentence depends on the meaning of earlier ones, automated systems will make mistakes. Long sentences, despite tricks like the attention model, can be hard to translate. And neural-net-based systems in particular struggle with rare words.

Training data, too, are scarce for many language pairs. They are plentiful between European languages, since the European Union’s institutions churn out vast amounts of material translated by humans between the EU’s 24 official languages. But for smaller languages such resources are thin on the ground. For example, there are few Greek-Urdu parallel texts available on which to train a translation engine. So a system that claims to offer such translation is in fact usually running it through a bridging language, nearly always English. That involves two translations rather than one, multiplying the chance of errors.

Even if machine translation is not yet perfect, technology can already help humans translate much more quickly and accurately. “Translation memories”, software that stores already translated words and segments, first came into use as early as the 1980s. For someone who frequently translates the same kind of material (such as instruction manuals), they serve up the bits that have already been translated, saving lots of duplication and time.

A similar trick is to train MT engines on text dealing with a narrow real-world domain, such as medicine or the law. As software techniques are refined and computers get faster, training becomes easier and quicker. Free software such as Moses, developed with the support of the EU and used by some of its in-house translators, can be trained by anyone with parallel corpora to hand. A specialist in medical translation, for instance, can train the system on medical translations only, which makes them far more accurate.

At the other end of linguistic sophistication, an MT engine can be optimised for the shorter and simpler language people use in speech to spew out rough but near-instantaneous speech-to-speech translations. This is what Microsoft’s Skype Translator does. Its quality is improved by being trained on speech (things like film subtitles and common spoken phrases) rather than the kind of parallel text produced by the European Parliament.

Translation management has also benefited from innovation, with clever software allowing companies quickly to combine the best of MT, translation memory, customisation by the individual translator and so on. Translation-management software aims to cut out the agencies that have been acting as middlemen between clients and an army of freelance translators. Jack Welde, the founder of Smartling, an industry favourite, says that in future translation customers will choose how much human intervention is needed for a translation. A quick automated one will do for low-stakes content with a short life, but the most important content will still require a fully hand-crafted and edited version. Noting that MT has both determined boosters and committed detractors, Mr Welde says he is neither: “If you take a dogmatic stance, you’re not optimised for the needs of the customer.”

Translation software will go on getting better. Not only will engineers keep tweaking their statistical models and neural networks, but users themselves will make improvements to their own systems. For example, a small but much-admired startup, Lilt, uses phrase-based MT as the basis for a translation, but an easy-to-use interface allows the translator to correct and improve the MT system’s output. Every time this is done, the corrections are fed back into the translation engine, which learns and improves in real time. Users can build several different memories—a medical one, a financial one and so on—which will help with future translations in that specialist field.

TAUS, an industry group, recently issued a report on the state of the translation industry saying that “in the past few years the translation industry has burst with new tools, platforms and solutions.” Last year Jaap van der Meer, TAUS’s founder and director, wrote a provocative blogpost entitled “The Future Does Not Need Translators”, arguing that the quality of MT will keep improving, and that for many applications less-than-perfect translation will be good enough.
The “translator” of the future is likely to be more like a quality-control expert, deciding which texts need the most attention to detail and editing the output of MT software. That may be necessary because computers, no matter how sophisticated they have become, cannot yet truly grasp what a text means.

Meaning and machine intelligence: What are you talking about?

Machines cannot conduct proper conversations with humans because they do not understand the world

IN “BLACK MIRROR”, a British science-fiction satire series set in a dystopian near future, a young woman loses her boyfriend in a car accident. A friend offers to help her deal with her grief. The dead man was a keen social-media user, and his archived accounts can be used to recreate his personality. Before long she is messaging with a facsimile, then speaking to one. As the system learns to mimic him ever better, he becomes increasingly real.

This is not quite as bizarre as it sounds. Computers today can already produce an eerie echo of human language if fed with the appropriate material. What they cannot yet do is have true conversations. Truly robust interaction between man and machine would require a broad understanding of the world. In the absence of that, computers are not able to talk about a wide range of topics, follow long conversations or handle surprises.

Machines trained to do a narrow range of tasks, though, can perform surprisingly well. The most obvious examples are the digital assistants created by the technology giants. Users can ask them questions in a variety of natural ways: “What’s the temperature in London?” “How’s the weather outside?” “Is it going to be cold today?” The assistants know a few things about users, such as where they live and who their family are, so they can be personal, too: “How’s my commute looking?” “Text my wife I’ll be home in 15 minutes.”

And they get better with time. Apple’s Siri receives 2bn requests per week, which (after being anonymised) are used for further teaching. For example, Apple says Siri knows every possible way that users ask about a sports score. She also has a delightful answer for children who ask about Father Christmas. Microsoft learned from some of its previous natural-language platforms that about 10% of human interactions were “chitchat”, from “tell me a joke” to “who’s your daddy?”, and used such chat to teach its digital assistant, Cortana.

The writing team for Cortana includes two playwrights, a poet, a screenwriter and a novelist. Google hired writers from Pixar, an animated-film studio, and The Onion, a satirical newspaper, to make its new Google Assistant funnier. No wonder people often thank their digital helpers for a job well done. The assistants’ replies range from “My pleasure, as always” to “You don’t need to thank me.”

Good at grammar

How do natural-language platforms know what people want? They not only recognise the words a person uses, but break down speech for both grammar and meaning. Grammar parsing is relatively advanced; it is the domain of the well-established field of “natural-language processing”. But meaning comes under the heading of “natural-language understanding”, which is far harder.

First, parsing. Most people are not very good at analysing the syntax of sentences, but computers have become quite adept at it, even though most sentences are ambiguous in ways humans are rarely aware of. Take a sign on a public fountain that says, “This is not drinking water.” Humans understand it to mean that the water (“this”) is not a certain kind of water (“drinking water”). But a computer might just as easily parse it to say that “this” (the fountain) is not at present doing something (“drinking water”).

As sentences get longer, the number of grammatically possible but nonsensical options multiplies exponentially. How can a machine parser know which is the right one? It helps for it to know that some combinations of words are more common than others: the phrase “drinking water” is widely used, so parsers trained on large volumes of English will rate those two words as likely to be joined in a noun phrase. And some structures are more common than others: “noun verb noun noun” may be much more common than “noun noun verb noun”. A machine parser can compute the overall probability of all combinations and pick the likeliest.

A “lexicalised” parser might do even better. Take the Groucho Marx joke, “One morning I shot an elephant in my pyjamas. How he got in my pyjamas, I’ll never know.” The first sentence is ambiguous (which makes the joke)—grammatically both “I” and “an elephant” can attach to the prepositional phrase “in my pyjamas”. But a lexicalised parser would recognise that “I [verb phrase] in my pyjamas” is far more common than “elephant in my pyjamas”, and so assign that parse a higher probability.

But meaning is harder to pin down than syntax. “The boy kicked the ball” and “The ball was kicked by the boy” have the same meaning but a different structure. “Time flies like an arrow” can mean either that time flies in the way that an arrow flies, or that insects called “time flies” are fond of an arrow.

“Who plays Thor in ‘Thor’?” Your correspondent could not remember the beefy Australian who played the eponymous Norse god in the Marvel superhero film. But when he asked his iPhone, Siri came up with an unexpected reply: “I don’t see any movies matching ‘Thor’ playing in Thor, IA, US, today.” Thor, Iowa, with a population of 184, was thousands of miles away, and “Thor”, the film, has been out of cinemas for years. Siri parsed the question perfectly properly, but the reply was absurd, violating the rules of what linguists call pragmatics: the shared knowledge and understanding that people use to make sense of the often messy human language they hear. “Can you reach the salt?” is not a request for information but for salt. Natural-language systems have to be manually programmed to handle such requests as humans expect them, and not literally.

Multiple choice

Shared information is also built up over the course of a conversation, which is why digital assistants can struggle with twists and turns in conversations. Tell an assistant, “I’d like to go to an Italian restaurant with my wife,” and it might suggest a restaurant. But then ask, “is it close to her office?”, and the assistant must grasp the meanings of “it” (the restaurant) and “her” (the wife), which it will find surprisingly tricky. Nuance, the language-technology firm, which provides natural-language platforms to many other companies, is working on a “concierge” that can handle this type of challenge, but it is still a prototype.

Such a concierge must also offer only restaurants that are open. Linking requests to common sense (knowing that no one wants to be sent to a closed restaurant), as well as a knowledge of the real world (knowing which restaurants are closed), is one of the most difficult challenges for language technologies.

Common sense, an old observation goes, is uncommon enough in humans. Programming it into computers is harder still. Fernando Pereira of Google points out why. Automated speech recognition and machine translation have something in common: there are huge stores of data (recordings and transcripts for speech recognition, parallel corpora for translation) that can be used to train machines. But there are no training data for common sense.

Brain scan: Terry Winograd

The Winograd Schema tests computers’ “understanding” of the real world

THE Turing Test was conceived as a way to judge whether true artificial intelligence has been achieved. If a computer can fool humans into thinking it is human, there is no reason, say its fans, to say the machine is not truly intelligent.

Few giants in computing stand with Turing in fame, but one has given his name to a similar challenge: Terry Winograd, a computer scientist at Stanford. In his doctoral dissertation Mr Winograd posed a riddle for computers: “The city councilmen refused the demonstrators a permit because they feared violence. Who feared violence?”

It is a perfect illustration of a well-recognised point: many things that are easy for humans are crushingly difficult for computers. Mr Winograd went into AI research in the 1960s and 1970s and developed an early natural-language program called SHRDLU that could take commands and answer questions about a group of shapes it could manipulate: “Find a block which is taller than the one you are holding and put it into the box.” This work brought a jolt of optimism to the AI crowd, but Mr Winograd later fell out with them, devoting himself not to making machines intelligent but to making them better at helping human beings. (These camps are sharply divided by philosophy and academic pride.) He taught Larry Page at Stanford, and after Mr Page went on to co-found Google, Mr Winograd became a guest researcher at the company, helping to build Gmail.

In 2011 Hector Levesque of the University of Toronto became annoyed by systems that “passed” the Turing Test by joking and avoiding direct answers. He later asked to borrow Mr Winograd’s name and the format of his dissertation’s puzzle to pose a more genuine test of machine “understanding”: the Winograd Schema. The answers to its battery of questions were obvious to humans but would require computers to have some reasoning ability and some knowledge of the real world. The first official Winograd Schema Challenge was held this year, with a $25,000 prize offered by Nuance, the language-software company, for a program that could answer more than 90% of the questions correctly. The best of them got just 58% right.

Though officially retired, Mr Winograd continues writing and researching. One of his students is working on an application for Google Glass, a computer with a display mounted on eyeglasses. The app would help people with autism by reading the facial expressions of conversation partners and giving the wearer information about their emotional state. It would allow him to integrate linguistic and non-linguistic information in a way that people with autism find difficult, as do computers.

Asked to trick some of the latest digital assistants, like Siri and Alexa, he asks them things like “Where can I find a nightclub my Methodist uncle would like?”, which requires knowledge about both nightclubs (which such systems have) and Methodist uncles (which they don’t). When he tried “Where did I leave my glasses?”, one of them came up with a link to a book of that name. None offered the obvious answer: “How would I know?”

Knowledge of the real world is another matter. AI has helped data-rich companies such as America’s West-Coast tech giants organise much of the world’s information into interactive databases such as Google’s Knowledge Graph. Some of the content of that appears in a box to the right of a Google page of search results for a famous figure or thing. It knows that Jacob Bernoulli studied at the University of Basel (as did other people, linked to Bernoulli through this node in the Graph) and wrote “On the Law of Large Numbers” (which it knows is a book).

Organising information this way is not difficult for a company with lots of data and good AI capabilities, but linking information to language is hard. Google touts its assistant’s ability to answer questions like “Who was president when the Rangers won the World Series?” But Mr Pereira concedes that this was the result of explicit training. Another such complex query—“What was the population of London when Samuel Johnson wrote his dictionary?”—would flummox the assistant, even though the Graph knows about things like the historical population of London and the date of Johnson’s dictionary. IBM’s Watson system, which in 2011 beat two human champions at the quiz show “Jeopardy!”, succeeded mainly by calculating huge numbers of potential answers based on key words by probability, not by a human-like understanding of the question.

Making real-world information computable is challenging, but it has inspired some creative approaches. Cortical.io, a Vienna-based startup, took hundreds of Wikipedia articles, cut them into thousands of small snippets of information and ran an “unsupervised” machine-learning algorithm over it that required the computer not to look for anything in particular but to find patterns. These patterns were then represented as a visual “semantic fingerprint” on a grid of 128×128 pixels. Clumps of pixels in similar places represented semantic similarity. This method can be used to disambiguate words with multiple meanings: the fingerprint of “organ” shares features with both “liver” and “piano” (because the word occurs with both in different parts of the training data). This might allow a natural-language system to distinguish between pianos and church organs on one hand, and livers and other internal organs on the other.

Proper conversation between humans and machines can be seen as a series of linked challenges: speech recognition, speech synthesis, syntactic analysis, semantic analysis, pragmatic understanding, dialogue, common sense and real-world knowledge. Because all the technologies have to work together, the chain as a whole is only as strong as its weakest link, and the first few of these are far better developed than the last few.

The hardest part is linking them together. Scientists do not know how the human brain draws on so many different kinds of knowledge at the same time. Programming a machine to replicate that feat is very much a work in progress.

Looking ahead: For my next trick

Talking machines are the new must-haves

IN “WALL-E”, an animated children’s film set in the future, all humankind lives on a spaceship after the Earth’s environment has been trashed. The humans are whisked around in intelligent hovering chairs; machines take care of their every need, so they are all morbidly obese. Even the ship’s captain is not really in charge; the actual pilot is an intelligent and malevolent talking robot, Auto, and like so many talking machines in science fiction, he eventually makes a grab for power.

Speech is quintessentially human, so it is hard to imagine machines that can truly speak conversationally as humans do without also imagining them to be superintelligent. And if they are super intelligent, with none of humans’ flaws, it is hard to imagine them not wanting to take over, not only for their good but for that of humanity. Even in a fairly benevolent future like “WALL-E’s”, where the machines are doing all the work, it is easy to see that the lack of anything challenging to do would be harmful to people.

Fortunately, the tasks that talking machines can take off humans’ to-do lists are the sort that many would happily give up. Machines are increasingly able to handle difficult but well-defined jobs. Soon all that their users will have to do is pipe up and ask them, using a naturally phrased voice command. Once upon a time, just one tinkerer in a given family knew how to work the computer or the video recorder. Then graphical interfaces (icons and a mouse) and touchscreens made such technology accessible to everyone. Frank Chen of Andreessen Horowitz, a venture-capital firm, sees natural-language interfaces between humans and machines as just another step in making information and services available to all. Silicon Valley, he says, is enjoying a golden age of AI technologies. Just as in the early 1990s companies were piling online and building websites without quite knowing why, now everyone is going for natural language. Yet, he adds, “we’re in 1994 for voice.”

1995 will soon come. This does not mean that people will communicate with their computers exclusively by talking to them. Websites did not make the telephone obsolete, and mobile devices did not make desktop computers obsolete. In the same way, people will continue to have a choice between voice and text when interacting with their machines.

Not all will choose voice. For example, in Japan yammering into a phone is not done in public, whether the interlocutor is a human or a digital assistant, so usage of Siri is low during business hours but high in the evening and at the weekend. For others, voice-enabled technology is an obvious boon. It allows dyslexic people to write without typing, and the very elderly may find it easier to talk than to type on a tiny keyboard. The very young, some of whom today learn to type before they can write, may soon learn to talk to machines before they can type.

Those with injuries or disabilities that make it hard for them to write will also benefit. Microsoft is justifiably proud of a new device that will allow people with amyotrophic lateral sclerosis (ALS), which immobilises nearly all of the body but leaves the mind working, to speak by using their eyes to pick letters on a screen. The critical part is predictive text, which improves as it gets used to a particular individual. An experienced user will be able to “speak” at around 15 words per minute.

People may even turn to machines for company. Microsoft’s Xiaoice, a chatbot launched in China, learns to come up with the responses that will keep a conversation going longest. Nobody would think it was human, but it does make users open up in surprising ways. Jibo, a new “social robot”, is intended to tell children stories, help far-flung relatives stay in touch and the like.

Another group that may benefit from technology is smaller language communities. Networked computers can encourage a winner-take-all effect: if there is a lot of good software and content in English and Chinese, smaller languages become less valuable online. If they are really tiny, their very survival may be at stake. But Ross Perlin of the Endangered Languages Alliance notes that new software allows researchers to document small languages more quickly than ever. With enough data comes the possibility of developing resources—from speech recognition to interfaces with software—for smaller and smaller languages. The Silicon Valley giants already localise their services in dozens of languages; neural networks and other software allow new versions to be generated faster and more efficiently than ever.
There are two big downsides to the rise in natural-language technologies: the implications for privacy, and the disruption it will bring to many jobs.

Increasingly, devices are always listening. Digital assistants like Alexa, Cortana, Siri and Google Assistant are programmed to wait for a prompt, such as “Hey, Siri” or “OK, Google”, to activate them. But allowing always-on microphones into people’s pockets and homes amounts to a further erosion of traditional expectations of privacy. The same might be said for all the ways in which language software improves by training on a single user’s voice, vocabulary, written documents and habits.

All the big companies’ location-based services—even the accelerometers in phones that detect small movements—are making ever-improving guesses about users’ wants and needs. The moment when a digital assistant surprises a user with “The chemist is nearby—do you want to buy more haemorrhoid cream, Steve?” could be when many may choose to reassess the trade-off between amazing new services and old-fashioned privacy. The tech companies can help by giving users more choice; the latest iPhone will not be activated when it is laid face down on a table. But hackers will inevitably find ways to get at some of these data.

The other big concern is for jobs. To the extent that they are routine, they face being automated away. A good example is customer support. When people contact a company for help, the initial encounter is usually highly scripted. A company employee will verify a customer’s identity and follow a decision-tree. Language technology is now mature enough to take on many of these tasks.

For a long transition period humans will still be needed, but the work they do will become less routine. Nuance, which sells lots of automated online and phone-based help systems, is bullish on voice biometrics (customers identifying themselves by saying “my voice is my password”). Using around 200 parameters for identifying a speaker, it is probably more secure than a fingerprint, says Brett Beranek, a senior manager at the company. It will also eliminate the tedium, for both customers and support workers, of going through multi-step identification procedures with PINs, passwords and security questions. When Barclays, a British bank, offered it to frequent users of customer-support services, 84% signed up within five months.

Digital assistants on personal smartphones can get away with mistakes, but for some business applications the tolerance for error is close to zero, notes Nikita Ivanov. His company, Datalingvo, a Silicon Valley startup, answers questions phrased in natural language about a company’s business data. If a user wants to know which online ads resulted in the most sales in California last month, the software automatically translates his typed question into a database query. But behind the scenes a human working for Datalingvo vets the query to make sure it is correct. This is because the stakes are high: the technology is bound to make mistakes in its early days, and users could make decisions based on bad data.

This process can work the other way round, too: rather than natural-language input producing data, data can produce language. Arria, a company based in London, makes software into which a spreadsheet full of data can be dragged and dropped, to be turned automatically into a written description of the contents, complete with trends. Matt Gould, the company’s chief strategy officer, likes to think that this will free chief financial officers from having to write up the same old routine analyses for the board, giving them time to develop more creative approaches.

Carl Benedikt Frey, an economist at Oxford University, has researched the likely effect of artificial intelligence on the labour market and concluded that the jobs most likely to remain immune include those requiring creativity and skill at complex social interactions. But not every human has those traits. Call centres may need fewer people as more routine work is handled by automated systems, but the trickier inquiries will still go to humans.

Much of this seems familiar. When Google search first became available, it turned up documents in seconds that would have taken a human operator hours, days or years to find. This removed much of the drudgery from being a researcher, librarian or journalist. More recently, young lawyers and paralegals have taken to using e-discovery. These innovations have not destroyed the professions concerned but merely reshaped them.

Machines that relieve drudgery and allow people to do more interesting jobs are a fine thing. In net terms they may even create extra jobs. But any big adjustment is most painful for those least able to adapt. Upheavals brought about by social changes—like the emancipation of women or the globalisation of labour markets—are already hard for some people to bear. When those changes are wrought by machines, they become even harder, and all the more so when those machines seem to behave more and more like humans. People already treat inanimate objects as if they were alive: who has never shouted at a computer in frustration? The more that machines talk, and the more that they seem to understand people, the more their users will be tempted to attribute human traits to them.

That raises questions about what it means to be human. Language is widely seen as humankind’s most distinguishing trait. AI researchers insist that their machines do not think like people, but if they can listen and talk like humans, what does that make them? As humans teach ever more capable machines to use language, the once-obvious line between them will blur.

Source: The Economist,  Technology Quarterly, Jan 7th, 2017

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