The Advantages And Disadvantages Of Computer Vision

Kian Dixon our consultant managing the role
Author: Kian Dixon
Posting date: 4/25/2019 9:28 AM
“Don’t judge a book by its cover”. We use this adage to remind ourselves to go deeper and to look beyond the superficial exterior. Except, sometimes, we can’t, or won’t. Sometimes, our perceptions are pre-programmed. Think family, peer pressure, and social influences. But what about computers? What do they see? In a digital landscape that demands privacy but needs information, what are the advantages and disadvantages of Computer Vision?

The Good: Digital Superpowers 


Let’s be clear, Computer Vision is not the same as image recognition, though they are often used interchangeably. Computer Vision is more than looking at pictures, it is closer to a superpower. It can see in the dark, through walls, and over long distances and, in a matter of moments, rifle through massive volumes of information and report back its findings.

So, what does this mean? First and foremost, it means Computer Vision can support us in our daily activities and business. It may not seem like it at first glance, but much of what the computer sees is to our advantage. Let’s take a deeper look into the ways we use Computer Vision today.

  • Big Data: From backup cameras on cars to traffic patterns, weather reports to shopping behaviours and everything in between. Everything we do, professional to personal, is being watched, recorded, and used for warning, learning, saving, spending, and social. 
  • Geo-Location: Want to know how to get from Point A to Point B? This is where Geo-location comes in. In order to navigate, the satellite must first pinpoint where we are and along the way, it can point out restaurants, shops, and services to ease us on our way.
  • Medical Imaging: X-rays, ultrasounds, catheterisations, MRIs, CAT Scans, even LASIK are already in use. Add telemedicine and the possibilities are endless. The application of these functions will allow faster and more accurate diagnoses and help save lives.
  • Sensors: Motion sensors that only turns a light on when a heat signature is nearby are already saving your home or business money on your electric bill. Now, during a shop visit when you are eyeing an intriguing product, your phone may buzz with a coupon for that very item. Computer Vision sensors are now tracking shopper movements to help optimize your shopping experience.
  • Thermal Imaging: Heat signatures already help humans detect heat or gas and avoid dangerous areas, but soon this function will be integrated into every smart phone. Thermal imaging is no longer used just to catch dangerous environments, it’s used in sport. From determining drug use to statistics and strategy, this is yet another example .

The Bad: Privacy Will Forever Change 


Google is 20 years old this year. Facebook is 15. Between these two media tech giants, technological advances have ratcheted steadily toward the Catch-22 of both helping our daily lives, whilst exposing our data to our employers, governments, and advertisers. Computer Vision will allow them to see you and what you’re doing in photos and may make decisions based on something you did in your school or university days. We’re already pre-wired to make snap judgements and judge books by their cover, but what will these advancements do to our daily lives? Privacy will change forever. 

We document our lives daily with little regard to the privacy settings on our favourite social media apps. GDPR has been a good start, but it’s deigned to protect businesses and create trust from consumers, rather than truly offer privacy. So far, the impact on our privacy has been limited as it still takes such a long time to sift through the amount of data available. However, the time is coming soon, where we’ll need to perhaps think of a privacy regulation businesses, employers, and governments must follow to protect the general population.

Fahrenheit 451, 1984, and Animal Farm were once cautionary tales of a far-off future. But Big Brother is already watching and has been for quite some time. Police monitor YouTube videos. Mayors cite tweets to justify their actions. And we, thumb through our phones tagging friends and family without discretion. 

Like every new technological advancement there are advantages and disadvantages. As Computer Vision becomes increasingly prevalent, we’ll all need to be aware of the kind of data we supply from to text to image. We can’t go back to the way things were, but we can learn about ourselves through the computer’s lens. And when it comes to computers and their capabilities, don’t judge a book its cover.

If you’re interested in Data & Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants for more information. 

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Machine Learning: How AI Learns

Machine Learning: How AI Learns

Amazon has begun curating summer reading lists. How? Patterns. Facebook shows you ads for items you may have been searching for online. How? It learns from your browsing habits. Ever wondered how Facebook knows you were just looking at that pair of shoes or that particular guitar. The Data you feed it, feeds its brain. In other words, this is how Artificial Intelligence learns. Machine Learning. Whilst it can be disconcerting to know that a machine understands our buying habits, that’s not the only thing it’s used for. It’s also a pivotal tool in such areas as Bionformatics, Biostatistics, Computational Biology, Robotics, and more.  What is Machine Learning? Ultimately, it’s a method of Data Analysis which helps to automate model building and is part of Artificial Intelligence. In other words, it helps to solve Computational Biology problems by learning from Data to identify patterns and make decisions with little human intervention. This helps scientific researchers learn about many aspects of biology. However, running a Machine Learning project can be difficult for beginners, who may experience issues when trying to navigate the information without making mistakes or second guessing themselves. This is one of the reasons a Computational Biologist might want to upskill with a course or two in Machine Learning for a more robust understanding of the information being learned and applied.  The Good News and the Bad With each shift of industrial revolution, there has been one system which has made an indelible mark on our daily lives and the Fourth Industrial Revolution is no different. Just like we can no longer imagine factories without assembly lines, we can also no longer imagine not having Siri, Google Maps, or online recommendations. But, as exciting and as important as these things are, Machine Learning has become so crucial to our daily lives, so complex, it takes a technology expert to master it leaving it nearly inaccessible to those who could benefit from it. Why is Machine Learning Important? By building models to peel back the layers and discover connections, organisations can more easily and more quickly make improved decisions with little to no human intervention. Computational processing is both more affordable and more powerful. It’s possible to quickly scale and produce models which can analyse bigger and more complex data and there’s also a chance to identify opportunities and to help avoid any unknowns such as risk. Machine Learning is used in every industry from Retail to Financial Services to Healthcare. Here are just a few ways it has already transformed our world. Retail – Retailers are able to learn from their customers buying habits, predictive buying habits, how to personalise a shopping experience, price optimisation, and customer insights.Financial services – Machine Learning helps to prevent fraud and identify Data insights.Healthcare – Wearable devices allow for real-time data to assess a patient’s health. Medical professionals can also more quickly find red flags which can help improve diagnoses and treatment.Oil and gas – It cannot only help find where oil might be, but also predict refinery sensory failure, and streamline distribution.Transportation – Help to make routes more efficient and predict problems that could affect the bottom line. While humans can create at least one or two models a week; Machine Learning can create thousands.  Ultimately, the goal of Machine Learning is to understand the structure of Data. As it learns to determine what Data is needed for its structure, it can be easily automated and sift through Data until a pattern is found. This is how machines learn. If you’re looking to take your next step in the field of Machine Learning, we may have a role for you. Take a look at our latest opportunities, or get in touch to see if we can help you take that next step.

Hunted’s Ben & Danni On Cybersecurity

Hunted’s Ben & Danni On Cybersecurity

Ben Owen and Danni Brooke are the Co-Directors for the EMEA Practice at Fortalice Solutions, a leading global cyber security and intelligence operations company.  They travel globally to assist clients with their cyber security requirements, bespoke training needs, intelligence and investigations both online and physical and counter fraud training/consultation. They deliver and manage a portfolio of pro-active intelligence solutions to keep people, nations and businesses safe from threats and head up the EMEA operations.  Ben and Danni also feature on the hit Channel 4 show, Hunted and Celebrity Hunted which has been airing for over four years with another series set to be filmed this summer. I caught up with them recently to discuss the latest Fraud, tools and challenges for the Cybersecurity industry. Cybersecurity is an ever-changing landscape. What trends do you anticipate for the next 12 months and beyond? It is always difficult to pin down what the next real trend is going to be in the Cybersecurity space as adversaries are becoming ever more sophisticated.  What was once a very difficult process for skilled individuals is becoming more readily available to novices with advances in software, particularly those shared on the Dark Web. What is an inevitable threat trend in the next 12-months and beyond is the exponential rise in the Internet of Things (IoT).  With a world where everything is hooked up to the web, it is apparent that tech companies selling these devices are under immense pressure to get products to market. The need for speed could mean that some security principles and best practices may be overlooked.   As the UK encountered during the Mirai Botnet attack of 2016, a network of electronic devices acting in concert can cripple the internet or, worst case, become a weapon that could cause actual physical damage as well as cyber damage, power stations, hospital networks to name but a few.   How have Data & Analytics impacted the detection, and prevention, of cyber-crime? A company will have to protect themselves against an enormous amount of cyber threats every second.  A cyber-criminal will only need one successful attempt. Data & Analytics are proving successful in the fight against cyber-crime and their proactive and holistic approach is at keeping people and businesses safe.  Of course, it is Data that is being stolen, but very often Data can come to the rescue.  It helps in a number of ways, e.g. identifying anomalies in employee and contractor computer usage and patterns, detecting irregularities in networks, identifies irregularities in device behaviour (a huge advantage with the rise of the IoT). What one must remember, however, is the people behind the Data.  You can’t simply collect Data and assume you will be able to detect and respond with the right actions.  You need the people with the right analytical skills to sift through the Data, find the right signals and then react to the threat with an appropriate and timely response.   What tools and technologies do you think will become increasingly important in the fraud and cyber-crime landscape? Here at Fortalice we are investing a lot of time into coverage of the Dark Web.  We live in a rapidly changing digital landscape. Criminals, fraudsters, and others are now operating with more sophistication and anonymity. Where do they go to exchange fraudulent details and ideas about current victims? What medium do they use to discuss organisational targets or new ways of defrauding companies? The answer is the Dark Web.  Traditionally, companies fight fraud from the inside out. We want to change this landscape by accessing the entirety of the Dark Web, its pages, shady storefronts, and treasure troves of Data, and drawing on monitoring toolsets to give our clients a 360-degree resource for identifying adversarial communications and movements. It’s all about Internet coverage.  Wherever it is difficult to find – that’s where your threat will be.   A final point to this question is one of sharing tools and techniques.  A collaborative approach is always a good way of making sure the wider audience benefits.  We always work with our clients and offer other services and support outside of our remit to make sure they’re fully protected from a cyber and physical space.   What are the biggest security threats for businesses? Security is fundamentally broken because the design of many security solutions does not design for the human psyche.  Security solutions are bolted on, clunky, and hard to use but because security teams prioritise defending against easier cyber threats, they often don’t focus on the hardware side. The biggest risk to companies and individuals is always defined by the Data that is most important to you or to the business.  For individuals, this might be privacy or identity. For businesses, this could be customer Data, intellectual property, and the company’s money in the bank. The reality is that business executives can’t outspend the (cybersecurity) issue and they must be prepared. Cybersecurity no longer exists in a vacuum and it must be elevated to the conversations held in the boardroom and with senior leadership as well as entire divisions, departments, and organisations. For someone trying to get into security analytics, what skills do you think are key to being successful in the industry? The detail is in the name of the role.  A huge ability to interpret large amounts of technical Data is key to the role, as well as being able to assimilate what it means and how to action it.  Risk management is also key to this role.  Very often you will identify potential risks and you will have to triage those priorities on your own as co-workers won’t have the technical expertise to assist.  You will need to be able to communicate successfully to all levels of a workforce and last but by no means least – a good sense of humour!  When you think you have gotten to understand a new threat or vulnerability a new one will replace it within seconds.  Time to put the kettle on, smile, and get back to work with your analytical prowess.   Within fraud, it's well known that criminals are sharing their approaches, is this mirrored in cyber-security and if so, how is the industry combating this? Criminal collaboration is huge on the web.  First of all, there is no talent shortage for fraud rings or cybercriminals. There are no requirements for fancy university degrees or certifications and the crime ring pays for performance.  They don’t care what you look like, how you dress, or if you clock in during normal work hours. They care about getting the job done - hacking into and stealing information from others. Together they are sadly stronger and more effective.  On Dark Web forums, you will see fraudsters sharing and selling their ‘IP’ knowing that others will also contribute. That way they are all winners.  In the private world ideas equal money. That is of course not a bad thing for business, but it is bad for collaboration. Businesses generally don’t like to share ideas with one another because it has taken them lots of time and expense to get to their product or solution. As cliché as this comment sounds - we have to change this landscape for the greater good.  There are lots of smart government initiatives for national defences in cyber security and fighting high-end cyber-crime but seldom does this have a positive impact locally with smaller businesses.  There is a huge amount of information out there for individuals and advice, but we need to bridge the gap still between criminal collaboration and that of the good guys. If you could change one thing in the industry, what would it be? The mind set of security professionals that humans are the weakest link. We’re not! Humans are at risk because technology is by design, open.  I’d also change the mind set of those not in the Cyber Security industry.  All too often the severity of what is being reported is not taken seriously, nor are budgets set aside for cyber security issues.  That said, it is improving but there is a long way to go.  Ben and Danni spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.

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