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Michael a Osborne.Indd An interview with Michael A Osborne Associate professor at the University of Oxford The New Innovation Wave: Machine Learning and Al Transform to the power of digital Michael A Osborne whereas at the end of the 20th century, of some of the most quintessential it was less than 2%. There was quite human characteristics. The boundaries a seismic change in the makeup between all these different fields are of employment due to technology. not very clear. However, you might However, none of these changes associate computer vision, language actually affected unemployment much; processing and speech recognition as the unemployment rate was about 5% other subsets of artificial intelligence. at the beginning of the 20th century and was still 5% at the end of the 20th century. Could you give us some examples of current applications of artificial intelligence and machine learning in business or non-business Roman Emperor environments? Michael A Osborne A great example of this is the retail Associate professor Tiberius, had business, where ‘recommendation at the University of Oxford [an inventor] sentenced engines’ are being used extensively. Take Amazon as example. It recommends to death, fearing the products to millions of customers on the loss of jobs and creative basis of historic data. Algorithms can process data, characterize the spending destruction. patterns of millions of customers, identify latent trends and patterns, and identify clusters and communities for recommendation. Your latest research with Professor Is the current digital innovation Carl Benedikt Frey and Citi on wave different from the previous innovation shows that throughout innovation waves? history powerful interests have Yes, in the past technology has largely hindered innovation. Is history In the past technology just substituted the human muscle, repeating itself? whereas now technologies can has largely just History might be repeating itself. increasingly substitute human brain labor Technological progress has always or human cognitive work. If machines substituted the human created significant disruptions that can substitute for cognitive labor, as muscle, whereas now powerful interests tried to combat to they have done in the past for physical maintain the status quo. In Ancient labor, it’s not very clear to what extent technologies can Rome, the great author, Pliny the Elder, there might be any demand remaining records the story of an inventor who for human labor. Another thing that is increasingly substitute discovered a way to manufacture glass different this time is the accelerating rate human cognitive work. that was unbreakable. Hoping to receive of technological change. It is undeniable a reward, the inventor presented his that we really are making quite dramatic invention to Roman Emperor Tiberius. leaps forwards in designing intelligent Self-driving cars are another application Unfortunately for the inventor, Tiberius algorithms that might be able to where machine learning is really playing had him sentenced to death, fearing the substitute for human workers. quite a pivotal role. Cars today have loss of jobs and creative destruction due sensors, onboard computing and safety to the new technology. Can you give us your definition of systems, and use intelligent algorithms The various innovation waves in history machine learning and artificial to render themselves autonomous. have led to dramatic upheavals in intelligence? These algorithms observe how you society but we have never seen large- drive using the onboard sensors and scale technological unemployment. For Machine learning is a subset of artificial quietly learn to travel from the current example, at the beginning of the 20th intelligence (AI) and can be defined as to desired positions on the basis of this century in the U.S., about 40% of our the study of algorithms that can learn data. employment was engaged in agriculture, and act. It’s about the reproduction Michael A Osborne Speech recognition is one key application What are the most promising an enormous economic impact for taxi of machine learning. Voice recognition opportunities for both AI and and truck drivers. In the near term, self- software, such as Apple’s Siri, has been machine learning? driving technologies are going to have an driven by advances in machine learning. impact in the automation of professions, In our recent paper, “The Future of Deep learning is becoming a mainstream such as forklift drivers, agricultural Employment”, Frey and I have identified technology for speech recognition and vehicle drivers, or mining vehicle drivers. occupations that we thought were the has successfully replaced traditional most susceptible to automation. These algorithms for speech recognition at are the kind of jobs that would be most an increasingly large scale. Historically, Are there sectors where you see easily automated within the near future people were trying to induce speech a lot of potential for machine using machine learning techniques. The recognition by recognizing speech learning? model predicts that most workers in features like words and phrases. transportation and logistics occupations, The retail sector, for example, offers But recently, algorithm designers together with the bulk of office and tremendous opportunities. We have have turned to massive data that has administrative support workers, and seen restaurants deploying tablets on become available for characterizing labor in production occupations, are tables. These tablets can be used to take human speech. If this trend continues, at risk. I think that in all these kinds of orders from customers. They can also we are likely to continue to move from areas, there will be an ever-increasing recommend customers a set of dessert the 95% accuracy rate that we’re able to degree of automation using machine options tailored to what customers achieve on our mobile devices today to learning algorithms. might want based on what they have nearly 99%. This would probably make ordered previously. A customer might these speech recognition techniques even have a profile that’s built up over completely widespread. multiple desserts in the same restaurant More or less anything or even a family of restaurants using the that does not require same software. So, you might get to the Machine learning is point at which this tablet can instantly one of the three know, as soon as you walk through a subset of artificial a door, what your usual order is, and bottlenecks – i.e. expedite the ordering of it. intelligence (AI) and can creativity, social Even in the traditional mall, the customer be defined as the study movement around the store can be intelligence and of algorithms that can closely monitored, indicating customer the requirement interest in products. The change here learn and act. is that these intelligent algorithms to manipulate can become more personalized and gather a lot more data about us as complex objects in You mentioned ‘deep learning’. individuals. Therefore, they can make Can you shed some light on this an unstructured recommendations to us that are more concept? directly targeted at what we want and environment – will what the store might actually want to “Deep learning” is the new big trend in promote. machine learning. It promises general, be potentially powerful, and fast machine learning, moving us one step closer to artificial intelligence. automatable. Can individual companies invest in Deep learning is a set of techniques, inspired artificial intelligence? by biological neural networks, for teaching More or less anything that does not The wonderful thing is that you don’t machines to find patterns and classify require one of the three bottlenecks necessarily need to be developing novel massive amounts of data. It tackles crisp and — i.e. creativity, social intelligence and artificial intelligence techniques and well-defined problems, such as classifying the requirement to manipulate complex machine learning algorithms to benefit images as to whether they contain a car or objects in an unstructured environment from the state-of-the-art. It’s relatively a book, for example. It’s worth noting that — will be potentially automatable in low cost, an individual corporation can deep learning is still not well-understood, or the near future. When self-driving cars just hire a data scientist or a machine provably robust, and that it is likely unsuitable become a reality, there is going to be learning PhD. There is this wealth of for safety-critical applications. Making Machines Learn Artificial Intelligence and Machine Learning What is machine learning? How big is machine learning? How hot is machine learning? $300 A subset of artificial Research on AI constitutes Venture capitalists have intelligence (AI). approximately 10% of all invested over $300 million Study of algorithms that computer science in AI startups in 2014, a 1 can learn and act. research today . twentyfold increase from 2010 2. It aims to reproduce some of the most quintessential human characteristics. Working with machines “We need to equip the At the same time, next generation of organizations need to protect workers with the themselves from deskilling – necessary skills that they loss of human skills due to will need for an economy intrusion of automation in that is increasingly occupations. automated”. - Michael A Osborne No heavy investments needed – a wealth of open-source, state-of-the-art algorithms already available, it costs very little to reproduce an amazing algorithm and it benefits everyone.
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