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Sam started his recruitment career 3 years ago, following a two year tenure in the University of Portsmouth careers department during his bachelors degree. Initially placing software engineers in the London technology market, Sam moved into the New York City computer vision and machine learning space.
Sam joined Harnham to spearhead the US computer Vision function, based in our New York Office. As the first hire on that team, Sam is building the group for the North East, partnering with leading businesses across greater New York City, Greater Boston, Washington DC and Pittsburgh.
US$180000 - US$200000 per year + Bonus, Equity
A rapidly growing and well funded robotics business with global offices is growing their core computer vision team
Visit our Blogs & News portal or check out our recent posts below.
5 Animal Farm, Fahrenheit 451, and 1984 all wove tales of Big Brother watching. Remember when only Superman had X-ray vision and leapt tall buildings in a single bound? Well, as big tech grapples with the cause and effect of its place when it comes to facial recognition, security, and object detection, there’s a new superhero in town. Computer Vision. The Eyes Which Sweep Our World While we still can’t be everywhere or see everything at once, we have eyes everywhere. Despite security cameras, motion detectors, satellites, computers, and smartphones, there are still dangers we may miss. So, by making our machines and computers visually-enabled using Artificial Intelligence, we significantly ramp up what we can see and how fast we can process it. Still growing, Computer Vision remains in an infant stage, given that autonomous cars still can’t differentiate between a rock and paper bag or worse, a person and a static object. Despite this, there are plenty of startups on the scene working toward a solution. As Artificial Intelligence increasingly blends with Biometric technology, it lends itself more easily to image recognition, allowing computers to correctly match fingerprints and facial patterns. But, no longer is it just matching two images. Now, it’s being taught to learn the difference between static images and liveness. This could prove invaluable for: Spotting weaponsSuspicious behaviorsDangerous object detection Safety Begins at Home With products such as security cameras prevailing within the security industry, businesses are hard at work creating and improving their products using the latest technologies. One such company is working to boost the clarity of their home security cameras. Think grainy gray, blurry images from night vision options or overly bright and distorted in the day. Their camera chip will be HDR and will be able to take much clearer pictures even in low-light. At home, you might find this product in a doorbell camera which could prove quite useful for smart homes which offer the option of allowing service people into your home from a remote app on your phone. And these cameras aren’t limited to your front door: ATMsIndoor/Outdoor camerasCCTV CamerasNumber Plate Recognition Cameras Though cameras have played a role in all of these areas for some time, the idea now is to keep them from being hacked and causing damage on both a product and a personal level. And like any type of Artificial Intelligence recognition system, these camera applications are created with advanced features to protect against hacking as well super speed processing of whether or not an object is an animal, a shadow, or some kind of inclement weather. The best part? We’re only a few months away from products which can boost the benefits of many of available security cameras. The Caveat of Image Recognition Systems More and more people around the world own security cameras, not the least of which is security personnel. With this increased level of ownership, the market is expected to have over $20 billion in revenue in less than five years. With such high demand, it’s no wonder only about five percent of footage ever gets viewed. But what if security professionals could navigate up to 80 video sources on a single server? What if the algorithms, analytics, and video processing worked with cameras of all types including, but not limited to: Car dashboardsBody vestsDrone mounts With all these cameras surrounding us, the one thing to watch is that it is still humans who input the information the cameras use to process. When it comes to security professionals such as law enforcement, investigation, and government authorities its import to make sure the camera doesn’t discriminate or operate on bias. This an important issue even that even our most advanced intelligence agencies admits the algorithms for their facial recognition software are wrong about 15 percent of the time. However, one organization is working to improve and make these algorithms more ethically developed when it comes to image recognition as well as respecting individual privacy concerns. As we navigate the growing pains of new technologies, it's important to understand these solutions are meant to ensure customers, the public, and communities can trust the solutions being created. If you’re looking for a new role in Computer Vision, take a look at our latest opportunities or get in touch with one of our expert consultants: For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
09. May 2019
It might surprise you to learn that we, humans, and machines, learn in much the same way. When we “size up a person, product, or situation” or “eyeball” a distance or measurement, our brain makes lightning quick calculations and guesses. From this, we’re able to reason and making relational assessments of separate items bound together by their similarities. These tasks, we accomplish as early as about 18-months old. This is what we’re working on getting computers to do as well, though we’re not quite there, yet. And just as children learn block by block and bit by bit, so too can we teach AI. Computer Vision, when it comes to the industry of robotics, offers one of the most challenging aspects of the latest technological advancements. Keeping Up Appearances Humans take in, process, acknowledge, and understand information in order to act, but the replication of this human software has been challenging. One way in which companies are meeting this challenge is combine Computer Vision hardware with software algorithms and Deep Learning to help teach computers to better “see” objects and identify them accurately. But like the inner-workings of the human mind, Machine Vision and Deep Learning touch on several areas such as actual and predicted Computer Vision reporting, the three-pronged recipe of a Computer Vision system, and a product development ecosystem which gets to the root of the technology. According to one report, Computer Vision in robotics is expected to grow significantly over a 7-year period. Some of the markets expected to see growth and making major investments include semiconductor manufacturers, software companies, and product developers. The Building Blocks The Deep Learning Engineer is to Computer Vision what the Data Engineer is to Data Science. In both regards, these professionals must begin with a solid foundation and build from there. In order to achieve 100% accuracy, hardware and software improvements must get underway and are tantamount to a fully developed ecosystem of Computer Vision product development. Acceleration of this rapidly evolving industry is due to a number of factors: Wide availability of wireless networks to millions of people worldwide.Deep Learning advances.More cost-effective chipsets.Images that can be processed, analyzed, and transmitted more easily with availability of high bandwidth.Open source libraries help to build differentiated products without reinventing the wheel every time when it comes to infrastructure. On the flipside, there are some barriers to overcome as well. These include, but are not limited to: Cost issues due to the fact that most advances take place in university research labs or big companies.A skills shortage of hardware engineers with Computer Vision and Deep Learning experience.A lack of recognized applications for these products, though the closest attempt may be self-driving cars. Though these considerations may seem unsurmountable in the short-term, they do provide plenty of opportunity for those in design automation. From the initial analysis to servers in the cloud, and high-level solutions to help computers make informed decisions. Looking Ahead So, what happens when we teach computers to think as we do? Will there be a battle for domination of one species over another? Probably not. But one of the biggest challenges to get to the next level in automated machine learning is understanding how much our thinking process involves predictions. This is important in fields which must coordinate information based on exterior data. This type of learning has impacted a variety of fields from online shopping to medical diagnosis. The massive amount of data available for consumption is staggering. Not only are Deep Learning and Machine Learning products of corporate and scientific solutions, they are also being put in the palm of our hand; our homes, our cars, and our handheld devices in order to help us more efficiently complete jobs which might be too slow going or for which mistakes are prone. If you’re interested in industrial robotics and the AI space, we may have a role for you. We specialize in junior and senior roles and have numerous opportunities in Computer Vision. Take a look at our current vacancies or get in touch with one of our expert consultants: For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
02. May 2019
Big Data tomes sit on a number of business reference shelves. Machine Learning, Analytics, and Edge Computing books compete for space in our minds, on our computers, in the cloud, and on the shelf. Over the past year, we’ve talked about the Data Scientist shortage, what Web Analytics mean to businesses, how AI will work hand-in-hand with humans and, if you’re looking for a career, how to stand out from the crowd. As the year comes to a close and we look to the new year, we wonder what 2019’s trends will be. What will change exponentially? What and who is lagging and leading? And how to navigate the soon to be third stage of ubiquitous data. Data is everywhere and, in some instances, can be too much to wade through. So, in a world of juxtapositions, the next wave of trends is to make Big Data small which will ultimately utilize AI more efficiently. Biting Off More than We Can Chew Much like the idea of music in your pocket with the introduction of the iPod, the latest trend in Big Data is to make it small, bite-sized, and navigable. So, how do you make Big Data small? The tsunami of data we encounter on a daily basis is staggering and overwhelming. As data teams become unsiloed, so too, does data. As vendors, digital leaders, business executives, and data professionals come together into a centralized team, data is being streamlined into a single view within a hub. Open source sharing, collaborating, and use of enterprise data catalogs within the hub add more value to businesses and can help to drive data management strategy. But, though education, training, and apprentice-like experiences, even the best data professionals can have trouble navigating the swathes of data they encounter each day. Enter AI. These systems are intended to cut through the data, filter the information based on algorithms it’s given and, when needed, “learn” what it needs to know to process information, and accurately share what it has discovered. From there, humans can take the information and analyze how it can be of benefit to the business and what actionable insights can, and should, be implemented. I, Human One of the more nefarious predictions of the past few years has been the fear that robots and AI would take over jobs. But, just as the dishwasher and laundry machine were developed to ease time at those chores, AI is the answer to how to increase productivity, not take over. Though AI has the capability to handle a range of tasks, it cannot replace hands-on, human-centric tasks. In retail, for example, AI might be used to make the process of shopping and buying more streamlined while freeing up the salesclerk to offer more focused customer service. A restauranteur could create the perfect ambience setting based on data about noise level, food preferences, busy vs slow times, and in so doing develop a customer base with whom they could discuss where the food comes from, offer classes, and more. AI is intended as a partnership to humans. Assisted productivity to free up time for more creative and complex pursuits. Beyond the industry executive, 2019 is predicted to be the year AI enables IT to move past routine automation tasks and proactively streamlines processes. With the assistance of AI, people will be able to work smarter, not harder, be more effective, and more productive. If you’re interested in Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 6070 or send an email to firstname.lastname@example.org.
20. December 2018
In the wake of Cambridge Analytica, Facebook data misuse, the Equifax breach, and the latest round of political finger pointing in regard to voter and election fraud, it’s no small wonder that demand for Fraud Analysts and Cybersecurity professionals is high. If you search: “why should businesses hire fraud analysts”, you’ll see Amazon near the top, hiring for fraud security roles. If they’re taking action, should you be as well? And as the 2018 mid-terms near, Facebook is back in the news. Determined to curtail a repeat of the issues surrounding the 2016 election, the social media giant is working diligently to remove misinformation from the platform. But with all these giant companies making fresh efforts to tackle fraud, what does this mean for you and your business? Fraud Security Begins at Home Your employees are your first line of defense when it comes to security of your business. No matter the size, it’s important to build a culture of anti-fraud policies right from the start. Below are a few essentials that all businesses should consider: Create a list of anti-fraud policies and share them with your employees, staff, and board members. Offer fraud training for both management and employees. Create a culture of reward for whistleblowers and open lines of communication such as a hotline/tip line. Though any business should be wary of prevailing scams and frauds in the marketplace, small businesses should be especially vigilant. Business News Daily offers additional tips for small businesses looking to prevent fraud right from the hiring process. The Customer Is Always Right: Balancing Fraud Risk Management with Customer Experience Experian® released their 2018 Global Fraud and Identity Report, based on results from 500 businesses and over 5,000 customers worldwide to understand what customers think of today’s security protocols. Trust was, far and away, the biggest talking point. With over 90% of consumers using smartphones and mobile devices followed by over 80% on laptops to search and buy, online security is paramount, and the new digital currency is trust. However, businesses are now having to grapple with the tension between managing fraud and maintaining a positive customer experience. Whilst they may need to lead customers to better solutions, businesses are finding that customers favor more familiar, time-tested methods like passwords. Ironically, those methods just might be compromising the experience they are advocating for by introducing an unintended nuisance and security risk; one-quarter of consumers have forgotten a username or password within the past six months. Building a Fraud and Risk Management Team In order to protect against fraud, IT needs to play a big role in guiding your business and bringing multiple solutions together. However, we all understand that teams dedicated to technology, risk, fraud detection, and data security take time and resources to integrate. So, to begin, here are a few ways to introduce Fraud Analysis and Risk Management into your business: Make data and fraud security guidelines part of your business plan and include them in your budget process – will the risk outweigh the reward? Involve staff, employees, management, and stakeholders at every level in the process. Be a hero in the eyes of your customer – balance detection with the customer experience. No matter where you are in your fraud detection and security hiring efforts, we may be able to help you. We specialize across roles of all levels in Data and Analytics on a global scale, with an eye for placing the right candidates in the right roles. For more information about recruiting top talent in the credit & risk sector, get in touch: Our West Coast Team can be reached at (415) 614 4999 or email@example.com. Our Mid-West and East Coast Teams can be reached at (212) 796 6070 or firstname.lastname@example.org.
09. August 2018