{"id":4491,"date":"2018-05-29T12:00:49","date_gmt":"2018-05-29T11:00:49","guid":{"rendered":"https:\/\/www.sage.com\/en-gb\/blog\/?p=4491"},"modified":"2024-01-10T15:14:40","modified_gmt":"2024-01-10T15:14:40","slug":"arshak-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.sage.com\/en-gb\/blog\/arshak-artificial-intelligence\/","title":{"rendered":"How AI and automation can tackle the big data challenge"},"content":{"rendered":"
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\n\t\t\t\t\t\t\tTechnology & Innovation<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t
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\n\t\t\t\t\t\tHow AI and automation can tackle the big data challenge\t\t\t\t\t<\/h1>\n\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t

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\n\t\t\t\n\t\t\t\t\"Asavin\"\t\t\t\tAsavin Wattanajantra<\/span>\n\t\t\t<\/a>\n\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

Is artificial intelligence (AI) worth the hype as business technology? There\u2019s certainly a huge buzz around it, judging from the noises coming from global enterprise vendors and analysts.<\/p>\n\n\n\n

Research and advisory firm Gartner for example, believes that by 2020, AI<\/a> will be pervasive in every product and service.<\/p>\n\n\n\n

Numbers seem to justify the heat. Gartner predicts that by 2025, the size of the AI market worldwide is likely to grow to $2.4bn (\u00a31.8bn).<\/p>\n\n\n\n

The largest proportions of revenues come from the AI for the enterprise applications market, which will see businesses pay to make use of image recognition, object identification, detection and classification, as well as automated geophysical feature detention.<\/p>\n\n\n\n

Sentient Technologies is an AI startup that specialises in evolutionary algorithms (EA), inspired by natural selection and biolo\u00adgical evolution. EA uses mechanisms that imitate the evolutionary concepts of reproduction, mutation, recombination and selection.<\/p>\n\n\n\n

The power of evolutionary algorithms<\/strong><\/h2>\n\n\n\n

They can be used in applications such as image processing, vehicle routing, mobile communication optimisation, software engineering and the training of artificial neural networks.<\/p>\n\n\n\n

These are used in tools such as Google Maps, games (including The Sims) and even to aid in clinical cancer treatment decisions.<\/p>\n\n\n\n

Distributed computing is a model where components of a software system are shared among multiple computers to improve efficiency and performance.<\/p>\n\n\n\n

EA can be massively distributed across multiple data centres, allowing lots of computing power to be drawn for data-intensive tasks.<\/p>\n\n\n\n

Sentient started off in trading, where EA was immediately seen as useful. But instead of going down the path of becoming a financial company, Sentient became a technology product business, generating more than $143m (\u00a3107m) in funding since its inception \u2013 and was the most well-funded AI startup worldwide between 2010 and 2016.<\/p>\n\n\n\n

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“We took a hard problem in deep learning, which was image captioning, and showed how evolutionary search through evolutionary algorithms could do the work of state-of-the-art deep learning architecture. In terms of performance, evolutionary search performed better by 10%” – Arshak Navruzyan<\/strong><\/p>\n<\/blockquote>\n\n\n\n

Arshak Navruzyan is the company’s chief technology officer. He\u2019s well placed in providing insight into enterprise AI, being responsible for leading the engineering direction of Sentient\u2019s core distributed AI platform. He is also the founder of Fellowship.AI, a machine learning fellowship programme that allows machine learning engineers to build real-world applications.<\/p>\n\n\n\n

Arshak says: \u201cWe\u2019ve been working in a number of domains, ranging from investment management to digital marketing applications. The underlying technology is always the same \u2013 it\u2019s a combination of the world\u2019s largest and most capable distributed AI, which combines EA, deep learning and scale compute \u2013 both GPU and CPU.<\/p>\n\n\n\n

\u201cTo give you an idea of the scale we\u2019ve worked with in the past, we published a paper in 2016 that showed how EA can do the work of a deep learning researcher.<\/p>\n\n\n\n

“We took a hard problem in deep learning, which was image captioning, and showed how evolutionary search through evolutionary algorithms could do the work of state-of-the-art deep learning architecture. In terms of performance, evolutionary search performed better by 10%.\u201d<\/p>\n\n\n\n

Arshak says Sentient is going through a period of discovery, understanding where its brand of AI could apply to different industries on a case-by-case basis. And to make it easier for businesses to adopt its brand of AI, they what to make sure that future customers won\u2019t have to do a lot of systems integration.<\/p>\n\n\n\n

He says: \u201cOur long-term goal is to start applying our AI technology to a variety of industries, such as agriculture, energy, healthcare and finance. We\u2019re about creating vertically focused applications that solve problems across industries and horizontals.\u201d<\/p>\n\n\n\n

Deep learning for facial recognition<\/strong><\/h2>\n\n\n\n

Arshak says Sentient worked with business executives across all industries to understand their priorities and what problems they needed to solve, finding out where AI could help.<\/p>\n\n\n\n

In manufacturing, for example, he reveals he was having conversations with companies that made hardware on a global scale, such as tablet, PC and smartphone manufacturers. He says these companies were actively trying to deploy deep learning in their environment for applications such as facial recognition, digital identification capabilities and speech recognition.<\/p>\n\n\n\n

Arshak says: \u201cWhen these companies want to integrate this AI into their platform, they come to the realisation that the footprint of deep learning models is such that they have needed to live in the cloud.<\/p>\n\n\n\n

\u201cWe\u2019ve been working with a very large industrial organisation in seeing how you could reduce the footprint of these deep learning networks, which often have hundreds of millions of parameters, down to a size which can be easily deployed at the edge of a network in device and not in the cloud.<\/p>\n\n\n\n

\u201cWith reliable connectivity, these devices have low bandwidth, so it\u2019s essentially about getting state-of-the-art performance in deep learning networks which have been shrunk to fit in a small device.\u201d<\/p>\n\n\n\n

\"\"
The automation industry is going through a period of disruption in part due to the development of electric and automated vehicles<\/figcaption><\/figure>\n\n\n\n

“The automotive industry won\u2019t be the same in 10 years”<\/strong><\/h2>\n\n\n\n

Arshak and Sentient have also been working with customers in the automotive space<\/a>, a particularly interesting sector due to the amount of disruption that is happening partly due to electric and automated vehicles<\/a>, as well as changing economic models such as ridesharing.<\/p>\n\n\n\n

He says: \u201cThe automotive industry won\u2019t be the same in 10 years. When you talk to technology leadership in that area, they are looking at lots of possible AI user cases such as self-driving cars, safety improvements and driver assistance.<\/p>\n\n\n\n

“Look at innovations by high-end automotive brands, such as cruise control and adaptive breaking, as well as an increasing demand for autopilot capability. There\u2019s a role for the next generation car to be more actively involved in the driving process, even if there\u2019s a human is at the wheel.”<\/p>\n\n\n\n

Arshak adds: \u201cAutomotive leaders aren\u2019t even sure the dealer distribution model is the way to go forward \u2013 to what extent should customers be able to have a car tailored to their preferences, and how much you should customise an individual car. They\u2019re thinking of revolutionising the car-buying process.<\/p>\n\n\n\n

\u201cEverything is on the table, such as the way consumers are engaged with, the economic model and even the nature of the product. With AI there are lots of ways it can disrupt this industry.\u201d<\/p>\n\n\n\n

How artificial intelligence and automation can benefit business decision-making<\/strong><\/h2>\n\n\n\n

With globalisation and the increase of digital supply chain network complexity, the amount of data that will be generated is only going to increase. Arshak feels that supporting better decision-making through data is one of the most important benefits of AI and automation<\/a>.<\/p>\n\n\n\n

He says: \u201cI think it\u2019s going to be harder for human analysts to keep up with this volume of data and to deliver consistent high-quality decision making, so for me, AI is first and foremost about automation.<\/p>\n\n\n\n

\u201cI don\u2019t we\u2019re at the point where we can completely replace human analysts, because a lot of business user cases require human judgment. But we need automation to scale up a business to keep up with demand from the customer side. It\u2019s hard to do it any other way.\u201d<\/p>\n\n\n\n

\"Tesla
Tesla boss Elon Musk has his reservations about artificial intelligence<\/figcaption><\/figure>\n\n\n\n

Artificial intelligence and its moral obligations<\/strong><\/h2>\n\n\n\n

As well as the hype around AI, it has also inspired famous figures in science and technology to highlight their perceived dangers around the technology. Stephen Hawking famously warned that AI could end mankind<\/a>, while Elon Musk described it as an “immortal dictator from which we would never escape”<\/a>.<\/p>\n\n\n\n

Arshak isn\u2019t a policy person but he shares an opinion about what he thinks the real dangers of AI are: \u201cI think we\u2019re worried about the wrong things. My biggest fear short-term is about misapplications of AI.<\/p>\n\n\n\n

“You hear examples where people are feeding images into an AI programme and, for example, classifying their sexual preferences or whether they are a criminal based on this data.<\/p>\n\n\n\n

\u201cMy biggest fear is seeing people who don\u2019t know what they\u2019re doing making inadequate models that will be the basis of important decisions in areas like criminal justice, approving loans or recruitment for example.<\/p>\n\n\n\n

\u201cNot that AI will become this evil overlord \u2013  there\u2019s no evidence to support that. It\u2019s more about people misusing AI and trusting judgments that are biased and unscientific in nature.<\/p>\n\n\n\n

What business leaders interested in AI need to know<\/strong><\/h2>\n\n\n\n

What advice would Arshak give to business leaders around taking of advantage of AI, with many businesses looking to make their first steps?<\/p>\n\n\n\n

He believes there is a propensity for lots of Fortune-level businesses to set up a Silicon Valley innovation arm and make statements that they had people there being innovative. These organisations would have a laboratory, showcasing that they\u2019re on the “bleeding edge” because they\u2019d brought in a name-brand researcher.<\/p>\n\n\n\n

Arshak says: \u201cI don\u2019t know if that\u2019s going to have the transformative effect that they\u2019re hoping for. I\u2019ve seen a lot of companies in a lot of industries funnel tens of millions of dollars into that approach and not end up with much in the way of practical results, or intangible changes to their product offerings or business model. Then they might forget about it, get flustered and move on.<\/p>\n\n\n\n

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“The average Fortune-level company needs to think about how they make themselves attractive to machine-learning researchers and data scientists” – Arshak Navruzyan<\/strong><\/p>\n<\/blockquote>\n\n\n\n

\u201cThose businesses that are serious about AI have to start treating it as a core competency, not as a sideshow. It\u2019s not about shiny labs and ticking boxes. The ones that succeed will make a serious investment and think along the lines of attracting and incentivising skilled AI talent to work for them and grow their careers there.<\/p>\n\n\n\n

\u201cBusinesses won\u2019t be known for their work with AI. Now if you want to do AI work you go to companies like Google, Open AI or Facebook. The average Fortune-level company needs to think about how they make themselves attractive to machine-learning researchers and data scientists. How they can amass enough of this type of talent to make a difference in the business problems they want to solve.<\/p>\n\n\n\n

\u201cThe problems that automotive manufacturers need to solve with AI [include] the development of automated vehicles, changing their economic model and engaging better with consumers. These are not small issues that you solve out of a lab \u2013 it\u2019s massive business transformation they need to achieve.\u201d<\/p>\n\n\n

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Is artificial intelligence (AI) worth the hype as business technology? There\u2019s certainly a huge buzz around it, judging from the noises coming from global enterprise vendors and analysts. Research and advisory firm Gartner for example, believes that by 2020, AI will be pervasive in every product and service. Numbers seem to justify the heat. Gartner […]<\/p>\n","protected":false},"author":356,"featured_media":4498,"menu_order":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_sage_video":false,"post_featured_image_hide":false,"footnotes":""},"categories":[11],"tags":[50,125],"business_type":[3],"lilypad":[],"context":[],"industry":[],"persona":[70],"imagine_tag":[87],"coauthors":[351],"class_list":["post-4491","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology-innovation","tag-ai","tag-workplace-technology","business_type-medium-sized-business"],"sage_meta":{"region":"en-gb","author_name":"Asavin Wattanajantra","featured_image":"https:\/\/www.sage.com\/en-gb\/blog\/wp-content\/uploads\/sites\/10\/2018\/05\/Sentient-Chief-Technology-Officer-Arshak-Navruzyan-Asavin-2.jpg","imagine_tags":{"87":"Enterprise management"}},"distributor_meta":false,"distributor_terms":false,"distributor_media":false,"distributor_original_site_name":"Sage Advice UK","distributor_original_site_url":"https:\/\/www.sage.com\/en-gb\/blog","push-errors":false,"_links":{"self":[{"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/posts\/4491","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/users\/356"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/comments?post=4491"}],"version-history":[{"count":0,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/posts\/4491\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/media\/4498"}],"wp:attachment":[{"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/media?parent=4491"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/categories?post=4491"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/tags?post=4491"},{"taxonomy":"business_type","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/business_type?post=4491"},{"taxonomy":"lilypad","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/lilypad?post=4491"},{"taxonomy":"context","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/context?post=4491"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/industry?post=4491"},{"taxonomy":"persona","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/persona?post=4491"},{"taxonomy":"imagine_tag","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/imagine_tag?post=4491"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.sage.com\/en-gb\/blog\/api\/wp\/v2\/coauthors?post=4491"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}