{"id":28232,"date":"2019-08-15T11:48:19","date_gmt":"2019-08-15T10:48:19","guid":{"rendered":"https:\/\/www.intelligentcio.com\/africa\/?p=28232"},"modified":"2019-08-15T11:48:22","modified_gmt":"2019-08-15T10:48:22","slug":"axis-expert-asks-how-far-are-we-really-at-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/africa\/2019\/08\/15\/axis-expert-asks-how-far-are-we-really-at-with-artificial-intelligence\/","title":{"rendered":"Axis expert asks ‘How far are we really at with Artificial Intelligence?’"},"content":{"rendered":"\n

Justin Ludik, Axis Business Development Manager, Critical Infrastructure, looks at how far Artificial Intelligence has come and what it can do for society and more importantly, surveillance.<\/strong><\/p>\n\n\n\n

If we\nlook for applications of Artificial Intelligence (AI) in our daily lives we can\nsee it in autonomous vehicles, medical applications, search engines, Virtual\nAssistants, and image recognition. Or can we? <\/p>\n\n\n\n

It is\nargued that there is no \u2018real\u2019 AI in practice yet, and what we see is just the\nbasics of Machine Learning (ML), also that it will take many years to create a\ntrue AI application. <\/p>\n\n\n\n

But\nlet\u2019s unpack exactly how far AI has come and what it potentially can do for\nsociety and more importantly surveillance.<\/p>\n\n\n\n

Since\nthe term was introduced in 1956, AI technology has gone from being hailed as\nthe key to a bright new future, to a complete failure, to an interesting tool\nfor applications including surveillance. <\/p>\n\n\n\n

Since\n2015, AI technology \u2013 or at least the embryo of it \u2013 has become part of our\neveryday lives and is currently a popular tagline in many applications.<\/p>\n\n\n\n

We\nmight be far from a truly \u2018intelligent machine\u2019, but AI is already being used\nin voice recognition and search engines; in healthcare to help identify\nindicators for cancer; voice translations, although these still have a way to\ngo and in prototypes for self-driving cars and service robots.<\/p>\n\n\n\n

Some\npeople expect AI to eventually become a game-changer in surveillance solutions\nand it\u2019s easy to understand why. AI systems are able to analyse huge amounts of\ndata, which of course is advantageous when you have to review hours of\nsurveillance images. <\/p>\n\n\n\n

By \u2018learning\u2019\nhow the observed object moves, how tall or small it is, whether it has two arms\nand two legs and how fast it is going, the AI application ranks the information\nand decides whether it is probable that the object is human. This kind of\nfunctionality is common in the field of safety and security today.<\/p>\n\n\n\n

Artificial\nIntelligence, Machine Learning and Deep Learning<\/strong><\/p>\n\n\n\n

AI is the act of simulating an intellectual task. An essential component of AI is ML and sometimes the two expressions are used interchangeably. AI uses algorithms to go through data, learn from it, and then make predictions. However, to mimic the complex human brain a computer needs lots of training. So-called Deep Learning (DL) is an advanced way of implementing ML, but it requires lots of data, advanced classifications and several layers of analysis to produce \u2018deeper\u2019 knowledge. <\/p>\n\n\n\n

A\ncompute-intensive model, DL is today mostly run on servers with multiple GPU\nprocessors, but when edge devices increase their computing capacity, some\napplications will probably move there in the future.<\/p>\n\n\n\n

AI\nin surveillance \u2013 design with a purpose<\/strong><\/p>\n\n\n\n

A\nsurveillance solution \u2013 with or without AI \u2013 is rarely an off-the-shelf\nsolution since every business and use case is unique. Understanding the primary\npurpose of the system and how the footage eventually might be used is\nessential. <\/p>\n\n\n\n

Image\nusability can only be achieved if the system has been adapted to the specific\nconditions of the environment and is properly maintained. To succeed the\nprimary focus therefore can\u2019t be on the technology, but on the solution of a\nparticular use case. <\/p>\n\n\n\n

Only\nas part of a complete and well-designed solution, where human expertise has\ntaken all kinds of considerations into account, can AI offer great\nopportunities within surveillance.<\/p>\n\n\n\n

AI\nin surveillance \u2013 advantages<\/strong><\/p>\n\n\n\n

Whenever\nthere is a need to process and analyse huge amounts of data, computers beat\nhumans. A well-trained AI can take this to an even higher level of data and\nimage analysis. This functionality can be of practical use in many\napplications.<\/p>\n\n\n\n

Safety\nand security<\/strong>\n– An AI system can, if properly set up and configured, quickly correlate events\ndetected in several hours of video material from several sources. A\nsurveillance system using AI can trigger a direct alarm, where a potential\nburglar can be \u2018told off\u2019 before the crime has even been committed.<\/p>\n\n\n\n

Business\noptimisation and smart cities<\/strong> – Surveillance systems with AI can detect unusual\nbehaviour patterns and use the data to optimise operational efficiency in\nretail environments or even allow for a controlled flow of traffic and quick\nresponses to incident reports on a highway.<\/p>\n\n\n\n

AI\nin surveillance: Challenges<\/strong><\/p>\n\n\n\n

It\u2019s easy\nto get carried away by the potential of AI in a range of applications, such as\nimprovement of configuration, system optimisation, site design, image\nconfiguration and device management. However, there are still fundamental\nchallenges that need to be solved.<\/p>\n\n\n\n

Human\nexperience beats AI<\/strong> – An AI application can successfully detect a \u2018running person\u2019 but,\nunlike humans who can put the data into context, it cannot know why that person\nis running \u2013 to catch the bus or because he has just robbed the bank.<\/p>\n\n\n\n

Quality\nof the data<\/strong> –\nWithout \u2018raw material\u2019 in terms of high-quality footage and video, an AI system\nhas nothing to analyse. If the cameras can\u2019t handle challenging light or if the\nangles are wrong, it could jump to the wrong conclusions. <\/p>\n\n\n\n

Compute-intensive\ntechnology<\/strong> – Memory,\nprocessing power, and power consumption are the biggest challenges within AI,\nespecially for deep learning applications. Today\u2019s systems require vast amounts\nof space to store data for learning, either in the cloud or on a server with\nmultiple GPUs.<\/p>\n\n\n\n

Conclusion<\/strong> <\/p>\n\n\n\n

AI systems have developed rapidly over the past years and show potential within the field of surveillance, but we are far from having developed human-level intelligence, or \u2018true\u2019 AI. It will take some time before we see self-learning surveillance systems that can make accurate assumptions in general scenarios without risking too many false alarms or the violation of p <\/p>\n","protected":false},"excerpt":{"rendered":"

Justin Ludik, Axis Business Development Manager, Critical Infrastructure, looks at how far Artificial Intelligence has come and what it can do for society and more importantly, surveillance. If we look for applications of Artificial Intelligence (AI) in our daily lives we can see it in autonomous vehicles, medical applications, search engines, Virtual Assistants, and image […]<\/p>\n","protected":false},"author":25,"featured_media":28233,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[4950,5,6,13],"tags":[386,8791,401,10667,10666,392,7257,1290,10668,10098],"class_list":["post-28232","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis","category-enterprise-security","category-insights","category-top-stories","tag-artificial-intelligence","tag-axis","tag-deep-learning","tag-dl","tag-justin-ludik","tag-machine-learning","tag-ml","tag-smart-cities","tag-surveillance","tag-virtual-assistants"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/posts\/28232","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/users\/25"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/comments?post=28232"}],"version-history":[{"count":1,"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/posts\/28232\/revisions"}],"predecessor-version":[{"id":28234,"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/posts\/28232\/revisions\/28234"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/media\/28233"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/media?parent=28232"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/categories?post=28232"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/africa\/wp-json\/wp\/v2\/tags?post=28232"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}