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The sudden ethereum price has put its market valuation at US$2.7 trillionThe first and foremost altcoin,
Ethereum Price PredictionAccording to a previous report submitted by a panel of 42 cryptocurrency experts in October, etherum price was anticipated to breach the US$4,500 mark by the end of 2023 and reach US$10,000 by 2023. However, things have changed upside down now. Even previously some enthusiasts have boosted ethereum’s value to reach the US$10k mark before the end of 2023. However, not many were very positive about the anticipation and thought it was overvalued. But the recent price rally and ethereum’s capability to part away from bitcoin and perform well has brought value to its stance.
Anticipations and Expectations on EtherAccording to some analysts, bitcoin and ethereum were predicted to double their value before the end of the year. While an analyst behind Plan B handle on Twitter has said that bitcoin will reach US$98,000 this month, it also indicated an upcoming ethereum price rally. However, ether has proved that the cryptocurrency can grow on its own without bitcoin’s help. Ethereum is the first altcoin that emerged out of bitcoin’s existence. Usually, ether follows bitcoin’s trend and keeps up with it. Whether it is a price surge or plummet, ethereum will follow the same path as Bitcoin does. But the recent price rally has indicated otherwise. Without a bitcoin price rally, ethereum has experienced a value surge. According to Goldman Sachs, ethereum network could well jump 80% to US$8,000 in the next two months if it keeps tracking inflations expectations. However, they also warned that central banks won’t let inflation rise sharply. JPMorgan has also said that they have seen signs of inflation in the cryptocurrency market. It has driven many investors to hold on to bitcoin and others rather than a gold investment. On the other hand, billionaire investor Mark Cuban has said that ethereum has the most upside as an investment model. He added that because according to him, ethereum blockchain, smart contracts, or collection of code has changed a lot in the cryptocurrency market.
Why Ethereum 2.0 is a Big Success?Many anticipated that Ethereum 2.0 could be a big success and might outperform bitcoin soon. They are not wrong. Ethereum saw the difference between the number of tokens issued and destroyed turn negative in the last seven days on aggregate for the first time. This is a tactic that bitcoin has used to trade its digital token since its inception. Bitcoin started its supply with a cap of 21 million. Once this number is reached, we can’t mine anymore bitcoins. The scarcity of the digital token has kept its value in the virtual currency ecosystem. Now, ethereum has boarded the same train with its update to 2.0. Ether is gaining value from a process called burning, where coins are taken out of circulation. Whenever an ether transaction is made, a small quantity of the coin is burned. As more transactions take place, it puts more ether at the cost of burning, which could eventually drive its price. On the other hand, Etherem ETF is also approaching the government approval stage. Although Bitcoin ETF is already on the radar, US regulators are more likely to approve ethereum ETF before giving a green signal to bitcoin.
So, Is it the Right Time to Invest?The first and foremost altcoin, ethereum , has skyrocketed to an all-time high with the coin hitting a value of US$4,470 yesterday. At the time of writing, the cryptocurrency’s value has surged even more and was being traded at US$4,556.70 with a 5% 24-hour growth. The sudden growth has put ether ’s market valuation at US$2.7 trillion, giving a stronghold to the second most adopted digital token in the cryptocurrency market. But what triggered the ethereum price when bitcoin is still maintaining a moderate value for a week straight? It is the metaverse and NFT announcements. Ethereum is expected to expand its service range to the metaverse. Metaverse is a digital space where you can work, play, or even create a community in the digital environment just like the physical one. On the other hand, ethereum has also come forward to tell that its technology is being used to sell the digital craze, non-fungible tokens (NFTs). In a nutshell, ethereum price has gained over 1,000% in the past year. Owing to the increasing adoption of disruptive methods, ethereum price is predicted to skyrocket to further highs in the coming days. Some enthusiasts even suggest that ether will breach its US$10,000 resistance by this year-end. In this article, we explore the price predictions of ether and talk about the right time to invest in a profitable cryptocurrency.According to a previous report submitted by a panel of 42 cryptocurrency experts in October, etherum price was anticipated to breach the US$4,500 mark by the end of 2023 and reach US$10,000 by 2023. However, things have changed upside down now. Even previously some enthusiasts have boosted ethereum’s value to reach the US$10k mark before the end of 2023. However, not many were very positive about the anticipation and thought it was overvalued. But the recent price rally and ethereum’s capability to part away from bitcoin and perform well has brought value to its stance.According to some analysts, bitcoin and ethereum were predicted to double their value before the end of the year. While an analyst behind Plan B handle on Twitter has said that bitcoin will reach US$98,000 this month, it also indicated an upcoming ethereum price rally. However, ether has proved that the cryptocurrency can grow on its own without bitcoin’s help. Ethereum is the first altcoin that emerged out of bitcoin’s existence. Usually, ether follows bitcoin’s trend and keeps up with it. Whether it is a price surge or plummet, ethereum will follow the same path as Bitcoin does. But the recent price rally has indicated otherwise. Without a bitcoin price rally, ethereum has experienced a value surge. According to Goldman Sachs, ethereum network could well jump 80% to US$8,000 in the next two months if it keeps tracking inflations expectations. However, they also warned that central banks won’t let inflation rise sharply. JPMorgan has also said that they have seen signs of inflation in the cryptocurrency market. It has driven many investors to hold on to bitcoin and others rather than a gold investment. On the other hand, billionaire investor Mark Cuban has said that ethereum has the most upside as an investment model. He added that because according to him, ethereum blockchain, smart contracts, or collection of code has changed a lot in the cryptocurrency chúng tôi anticipated that Ethereum 2.0 could be a big success and might outperform bitcoin soon. They are not wrong. Ethereum saw the difference between the number of tokens issued and destroyed turn negative in the last seven days on aggregate for the first time. This is a tactic that bitcoin has used to trade its digital token since its inception. Bitcoin started its supply with a cap of 21 million. Once this number is reached, we can’t mine anymore bitcoins. The scarcity of the digital token has kept its value in the virtual currency ecosystem. Now, ethereum has boarded the same train with its update to 2.0. Ether is gaining value from a process called burning, where coins are taken out of circulation. Whenever an ether transaction is made, a small quantity of the coin is burned. As more transactions take place, it puts more ether at the cost of burning, which could eventually drive its price. On the other hand, Etherem ETF is also approaching the government approval stage. Although Bitcoin ETF is already on the radar, US regulators are more likely to approve ethereum ETF before giving a green signal to bitcoin.There is no time as the right time when it comes to cryptocurrency investment. Even investing in the dip is not a wise method. So if you are planning to try your hand on ehtereum, you can do it right away. But make sure you invest an amount that can be handled in case of value decrease.
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Apple Music Diary: Decision Time – Is It Worth The Money?
My first impressions of Apple Music weren’t great. I loved the first ‘For you’ recommendation it made, but the service then seemed to be completely ignoring what it should already know about my taste in music and offering me a lot of what I already owned. I also had a number of complaints about the user interface.
One week in, I was still unhappy with it continuing to offer to ‘introduce me’ to artists whose music I already owned, and was pretty unimpressed with the radio stations, but I was enjoying its hit-rate in recommending artists new to me.
With the three-month almost at an end, it’s time to make a decision about whether the service is worth paying for …
My complaints about the user interfaces of both iTunes and the iOS Music app mostly remain. Apple has done a little tinkering around the edges, but there’s still a lot of work to be done. Apple has at least acknowledged that, iTunes International VP Oliver Schusser stating that the company is “adding features and cleaning up certain things” – which I’m hoping goes beyond the changes seen in iOS 9.
My disillusionment with Apple Music’s radio offerings – both old and new – also continued. Beats 1 was never going to be for me, but none of the old radio stations grabbed me either. As I said last time, the one station that should have been tailor-made for me, Singer-songwriter, was a massive disappointment. Whoever curates it seriously needs to be fired. Alternative was okay-ish for background listening, but the ‘ish’ won out over the ‘okay’ and I abandoned that too.
For that reason, I do still check on on it every now and then, just to see what’s new, but mostly I view it as junk. Which brings us back to the all-important For You tab. If Apple Music was going to win me over, it needed to get this right.
Like any streaming music service, I needed to give Apple Music a chance to learn my tastes. Spotify has literally years of data on my listening preferences, and here I was going to be making the decision on whether or not to switch based on just three months’ usage of Apple Music. I decided that to be scrupulously fair, I would make a religion of training it.
Sure, there were times when I just left it playing in the background, but as someone who doesn’t own a TV, I’m a pretty active music listener a lot of the time. So a good 90% of the time, I religiously used the Love or Dislike options on every track that I felt strongly about one way or the other.
Which brings me to a brief aside on the lack of joined-up design between the Apple Music experience on OS X and iOS: in iTunes, you dislike a track or album by selecting the ‘…’ menu and choosing ‘Recommend Less Like This’; in the iOS Music app, you press on the recommendation and select ‘I don’t like this suggestion’ – despite the fact that the ‘…’ menu exists in the Music app too. Different method, different wording.
I also noted what we learned from The Loop – that Apple Music doesn’t care if we skip a track, as that could be just because we’re not in the mood at the time, but it does give credit for tracks listened to in full, assuming we like those. So I tried to ensure I listened to the very end of any track I liked, even if I was keen to listen to something else immediately afterwards.
I complained at length in my previous diary pieces that Apple Music didn’t seem to take into account my own music library, recommending albums I already owned, and even offering to ‘introduce’ me to some of my favorite artists. Phil Schiller insists that Apple Music does learn from our libraries, and presumably intends to resurface albums we own but haven’t listened to for a while. I was skeptical, because it seemed to recommend albums I not only own but have played recently. In the above clipping, for example, are three albums I own, one of which I’d played within the past week (no, it’s not Shania Twain).
So I decided I’d look on recently-played examples as bugs, and ignore the wording of ‘Introduction to’ recommendations and simply treat those as a way to play a different mix from an artist whose work I already owned. That mental shift made a surprisingly big difference, no longer finding myself irritated by those suggestions, viewing them instead as a ‘Hey, are you in the mood for some …’ prompt instead.
And you know what? Sometimes I was. In the first week, I was very focused on its ability to introduce me to new artists – on which more in a moment – so just viewed already-owned music as a distraction. But once I settled more into a mix of old and new music, sometimes its suggestions were good ones. I haven’t figured out if there’s any particular methodology behind them – like gentler music in the morning and louder music later – so maybe it’s just randomly pulling stuff from my library, but I can live with that.
But music discovery remains key for me. I don’t need a streaming music service to listen to my own music, even if it does give me prompts I wouldn’t otherwise get. And it’s here that Apple Music really has excelled. Even a week in, I found it was broadening my musical horizons significantly, and it’s continued to do so, recommending artists I’d likely never have discovered any other way. Some of those artists have become favourites.
In three months, it has introduced me to more new artists I like than Spotify has in literally years. More than I’ve discovered through recommendations from friends. More than I’ve found by Shazamming tracks in bars and coffee shops.
That’s huge. And while the UK does get a raw deal on pricing, it’s still less than than the cost of one album per month. So yes, for me Apple Music is worth it, and I’ll be continuing my subscription once Apple starts billing me in a week’s time. My Spotify subscription got cancelled a week in, and I won’t be renewing it.
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When Is It Time To Hire An Hr Person?
What is HR?
Human resources (HR) is the process of overseeing and shaping all employee matters. The term “human resources” can describe either this work or your company’s entire workforce.
What is an HR employee, and what do they do?An HR employee carries out the functions involved with overseeing and shaping employee matters. Small business owners might handle these functions themselves at first, since they own the company and must ensure all the gears are turning.
Eventually, however, you may want to hire an HR employee or team. You can either hire HR in-house or outsource HR.
No matter which choice you make, HR employees will handle five main HR functions.
Staffing: Your HR team will use specific recruitment strategies to attract prospective employees. It will assemble a workforce of employees in various capacities, including part-time, full-time and freelance. Administering benefits typically goes hand in hand with this HR function.
Development: Your HR team’s involvement doesn’t end with successfully hiring employees. Your team must also develop new-hire training programs, sometimes from a foundation of industrial-organizational psychology.
Compensation: HR employees administer your payroll. They may also play a role in shaping your pay scale and the salary ranges you offer. They must clearly detail your business’s pay structure to stakeholders and employees.
Safety and health: Your HR team should educate your employees on best workplace safety practices. This HR function is especially important if your team works with potentially dangerous equipment, but it always matters. After all, something as ordinary as an extension cord running across the floor could cause someone to trip and injure themselves.
Employee and labor relations: All disputes between employees fall to your HR team to solve. Your HR team can also represent you if your employees unionize, strike or otherwise seek changes to their working conditions. If employee discipline is necessary, your HR team will enforce the disciplinary action policy it helped create.
Did You Know?
HR is also a key player in business strategy, providing data and analytics to inform issues like cost management and succession planning.
The importance of human resourcesA human resource specialist’s typical tasks include managing the recruitment and hiring processes, training personnel, staying up-to-date on business labor law compliance, and ensuring the workplace is running efficiently. HR specialists also generally deal with employee pay, benefits packages, and working conditions. Of course, they also deal with employee complaints or disciplinary situations. The central theme that connects these tasks is that the HR specialist acts as a link between the employer and the employee, and as a mediator who prevents or resolves workplace issues.
“It’s really hard as a business owner to put ourselves in the employees’ shoes,” said Steve Kurniawan, content specialist and growth strategist at Nine Peaks Media. “We mainly think from the perspective of how to grow the business – increasing revenue [and] winning markets – while the employees think about their promotions, their personal struggles. HR managers, on the other hand, will think as an employee and can better relate to the employees’ requests and needs.”
The benefits an HR manager offers a small business are clear, but at what point in the business’s life cycle could you justify the added cost of bringing on an HR manager?
The right time to hire a human resources managerThere’s no single answer as to when a business should hire an HR manager. The window can shift depending on a business’s industry, growth rate, strategic planning and workload. Instead, look to these indicators to help you decide when the time is right to bring a dedicated HR specialist on board.
When business roles start to become specializedYour business’s general organization is one indicator of when you might need a dedicated HR manager. As businesses grow, their staff often shifts from general roles focused on necessary tasks into more specialized roles and, eventually, narrowly focused departments. As that shift occurs, it makes sense to develop an HR department.
“When a business is small, it is normal for everyone to be in ‘all hands on deck’ mode all the time,” said Jordan Brannon, president and COO of Coalition Technologies. “However, with any successful business, there is a point to be reached when you’re no longer so tight on cash and [staffing] that you cannot afford for your employees to specialize in their departments. At that point, it is time to hire a human resources person.”
When the business grows to a certain number of employeesBusiness owners could also judge their need for an HR manager by reaching a milestone number of employees, such as 50 or 100. However, according to Doug Coffey, HR expert and assistant teaching professor at the Rutgers School of Management and Labor Relations, specialization remains the real differentiator.
“Having an HR manager will probably be a necessity when the company approaches 100 or more employees, but the telltale sign is when the business starts to specialize into functions such as IT, sales and operations,” Coffey said. “If you need a dedicated manager in those areas, you probably need the specialized knowledge of a human resources manager as well.”
When a focus on revenue over HR tasks would boost the bottom lineAnother method to determine whether you could benefit from a full-time HR specialist is to track your time spent on HR tasks and ask yourself, “Would my time be better spent growing the business?”
Once HR tasks begin demanding an inordinate amount of a business owner’s attention, it might be time to bring a dedicated HR manager to the team. If an owner’s efforts to grow revenue could cover the cost of a new salary and then some, the choice is clear.
“Business owners should track the time they’re spending on employee-related issues,” said Sharon DeLay, owner and president of GO-HR. “They should then consider what the impact to revenue growth would be if they spent that time instead in growing revenue. If the revenue growth activities would far outweigh – or would soon outweigh – the cost of HR resources, whether outsourced or hired, then it’s time to look at HR solutions.”
Tip
If you decide to handle human resources tasks in-house, the best HR software can help you stay compliant and efficient in managing your human capital.
In-house HR vs. HR outsourcingWhen you’re ready to offload HR work to someone new, you can either hire new employees or enlist a third-party firm. Both entities will have the knowledge and skills to handle HR in ways that might be more difficult for you. Consider the following factors before you make your decision.
Cost: In 2023, the median salary for an HR specialist was $61,920. The equivalent number for an HR manager was about twice that. Hiring another employee will also affect what you pay for benefits and payroll administration. So, ask yourself: Is the total cost of an employee ultimately less expensive than outsourcing?
Control: HR employees operate directly under your oversight. Outsourced HR firms must keep you happy to retain you as a client, but they may follow their own internal protocols. If you need control over and complete transparency with your HR functions, you might fare better hiring HR in-house.
Expertise: HR job titles are more diverse than just “specialist” or “manager.” Some HR employees are experts in specific HR segments – say, compensation or labor relations specialists. If you want an expert in each of these areas, you might fare better hiring an HR firm. These firms typically have experts in every part of the field, whereas one or two in-house employees may lack this specialization.
Employee relationships: Employees who work in office settings see each other daily, and remote teams likely communicate daily in some way or other. This notion extends to in-house HR employees, who will build actual relationships with your other employees. Such connections are tougher to cultivate if you outsource to an HR firm.
Key Takeaway
Delegating HR functions to one of the best HR outsourcing companies can connect you with better expertise. Outsourcing HR can also lead to a loss of control and pose challenges for employee relations.
The risks of operating without an HR specialistBusiness owners might feel they can go it alone, and perhaps they can, but failing to hire an HR manager comes with risks that could develop into serious consequences, both organizationally and legally. Before deciding not to hire an HR manager as your business scales, you should be aware of the potential problems to which you are exposing your business.
Confusion and dissatisfactionNot having a knowledgeable HR professional can lead to confusion and disorder.
“A human resource manager helps to establish fairness and consistency throughout the organization,” Coffey said. “Employees come to know what to expect with policy development in hiring, performance management, reward allocation [and] leave policies. By not hiring HR managers, businesses may add to employee turnover, one of the highest HR-related costs. This happens when dissatisfied employees leave due to inconsistent, time-wasting, unfair policies.”
Legal and operational consequencesBeyond creating confusion or dissatisfaction amongst employees, the consequences for mismanaging logistics regarding HR-related tasks are steep. If a business owner is overwhelmed by the responsibilities of running the business and managing HR, they could find themselves in a record-keeping nightmare.
“When a business does not hire an HR manager, things will be missed,” Brannon said. “Important paperwork, completed photo releases that could lead to lawsuits, updating out-of-date paperwork, optimizing incorrect forms, and keeping employee tax information up to date are all essential points that could be missed by a business owner running their own HR department.”
Those documentation issues can quickly balloon into legal problems, potentially resulting in fines or lawsuits. While late paperwork might not seem to be a significant threat at first, it can be a slippery slope that creates extra work at best and a legal disaster at worst.
Did You Know?
Another HR outsourcing option is a professional employer organization (PEO). PEOs use a co-employment model, meaning your employees will appear on their books for legal and tax purposes.
The necessity of HR in businessThere is no escaping the need for HR. The issue is how to maximize efficiency and the return on your investment. Many business owners try to handle everything on their own until doing so is no longer feasible. However, when handling something as sensitive and critical as human resources, it’s crucial to establish a planned transition away from the startup phase, in which owners and staff all wear many hats, to a more specialized organization with dedicated department managers.
Thinking about HR early on – and at what point you should let go of specific responsibilities – will make it much easier to shift control to a dedicated HR manager down the line before things get messy.
Max Freedman contributed to the writing and research in this article. Source interviews were conducted for a previous version of this article.
Computer Vision And How It Is Shaping The World Around Us
This article was published as a part of the Data Science Blogathon
Since the initial breakthrough in Computer Vision achieved by A. Krizhevsky et al. (2012) and their AlexNet Network, we have definitely come a long way. Computer Vision has since been making its way into day-to-day human lives without even us knowing about it. The one thing Deep Learning Algorithms need is data, and with the progress in portable camera technology in our mobile devices, we have it. A lot more, and a lot better. With great data, comes great responsibility. Data Scientists and Vision Engineers have been using data to create value in the form of awesome Vision applications.
Computer Vision has found applications in very diverse and challenging fields and these algorithms have been able to assist, and in some cases, outperform human beings. Be it Medical Diagnosis (Biology), Production Automation (Industry), Recommender Systems (Marketing), or everyday activities like driving or even shopping, Vision Systems are everywhere around us. In this blog, I am going to discuss some applications of computer vision, and how companies are implementing scalable vision systems to solve problems and generate value for their customers.
A timeline of some seminal computer vision papers against milestone events in computer vision product applications
Self Driving VehiclesSource
Tesla uses 8 cameras on the vehicle to feed their models, and the models do pretty much everything that can be done using video data, to guide the vehicle. The granular sub-applications that Tesla Autopilot needs to function are:
Detection and Recognition of Objects (Road Signs, Crosswalks, Traffic Lights, Curbs, Road Markings, Moving Objects, Static Objects) (Object Detection)
Following the Car Ahead (Object Tracking)
Differentiating between Lanes/ Lanes and Sidewalk / Switching Lanes (Semantic Segmentation)
Identifying Specific Objects (Instance Segmentation)
Responding to events (Action Recognition)
Smart Summon (Road Edge Detection)
Depth Estimation
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Evidently, this is an extremely multitasked setting, where there is a need to know a lot about the scene at once. That is why the tech stack is designed in such a way that there are multiple outputs for a given input sequence of images. The way it is implemented is that for a set of similar tasks, there is a shared backbone, with a set of tasks, at the end, all of which give a specific output.
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Some tasks require features from specific camera feeds to make a prediction, so each camera has its own HydraNet trained for camera-specific tasks. But there are more complicated tasks like steering the wheel, depth estimation, or estimating road layout, which might need information from multiple cameras, and therefore, features from multiple HydraNets at the same time to make a prediction. Many of these complicated tasks can be recurrent, adding another layer of complexity to the network.
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Summing it up, Tesla’s Network consists of 8 HydraNets (for 8 cameras), each responsible for specific tasks. In addition to that, the features from these HydraNets go into another run of processing which requires camera interactions with each other, and spread over time, to derive meaningful insights and is responsible for more complex tasks.
According to Tesla, there are nearly a hundred such tasks. This modular approach has many benefits for Tesla’s specific use case:
It allows the network to be specifically trained for specific tasks. The network is subsampled for that specific task and is then trained for it.
It drastically reduces the overall number of trainable parameters, thus amortizing the process.
It allows certain tasks to be run in shadow mode while the overall system performs as usual.
It allows for quicker improvements to the overall network, as updates can be installed in parts rather than overall.
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What Tesla has done well, and many other efforts at autonomous driving failed to achieve is data generation. By giving more and more products in the hands of consumers, Tesla now has a large source of quality data. They are able to capture disagreements between the Human and the Autopilot by deploying models in live mode as well as shadow mode. In this way, they have been able to improve their models by inference capabilities on real-world data, capturing disagreements and mistakes made by both, the Human and the Autopilot. As long as they receive well-labeled data, their models keep on improving with minimal effort.
Medical ImagingSource
Arterys is one of such leading players, reducing subjectivity and variability in medical diagnosis. They have used Computer Vision to reduce the downtime to image blood flow in the heart, which took hours initially, to minutes. This allowed cardiologists to not only visualize, but also quantify blood flow in the heart and cardiovascular vessels, thus improving the medical assessment from an educated guess to directed treatment. It allowed cardiologists as well as AI to diagnose heart diseases and defects within minutes of MRI.
But why did it take hours for scans to generate flows in the first place? Let’s break this down.
Multiple in vivo scans are done to capture 3D Volume cycles over various cardiac phases and breathing cycles.
Iterative Reconstruction Methods on MRI data to evaluate flow increases reconstruction times automatically.
Source
Along with 4D flow generation, object detection algorithms (Fast R-CNN(2023), R-FCN(2023)) in Arterys’ tech stack help to identify unidentifiable abnormalities and contours in the heart, lungs, brain, and chest scans. It automatically indexes and measures the size of the lesions in 2D and 3D space. Image Classification Networks help to identify pathologies like fracture, dislocation, and pulmonary opacity. Arterys trained its CardioAI network, which can process CT and MRI scans, on NVIDIA TITAN X GPUs running locally and on Tesla GPU accelerators running in Google Cloud Platform. Both were supported by the Keras and TensorFlow deep learning libraries. Inference occurs on Tesla GPUs running in the Amazon cloud.
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Though these insights are very important for the medical professional, their availability to the medical professional can cause bias in the medical professional’s assessment of the case. Arterys mitigates this problem by flagging certain cases for attention but not specifying the exact location of the abnormality in the scan. These can be accessed once the specialist has made an unbiased assessment of the case.
Cloud-based deployment of its stack has allowed Arterys to provide reconstructions as well as invaluable visual and quantifiable analysis to its customers on a zero-footprint web-based portal in real-time. Computer Vision’s biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. The high-value applications are in rural and medically underdeveloped areas where physicians or specialists are hard to come by.
Visual SearchVisual Search is a search based on images rather than text. It heavily depends on computer vision algorithms to detect features that are difficult to put into words or need cumbersome filters. Many online marketplaces, as well as search tools, have been quick to adopt this technology and consumer feedback for the same has been strongly positive. Forbes has forecasted that early adopters of visual search are projected to increase their digital revenue by 30%. Let us talk about a few early adopters and even late adopters to the visual search technology and how they have gone about implementing it.
Pinterest’s Visual Lens added a unique value to their customers’ experience wherein they could search for something difficult to put in words. Essentially, Pinterest is a giant Bipartite Graph. On one side are objects, which are pinned by the users, and on the other side are boards, where mutually coherent objects are present. An edge represents pinning an object (with its link on the internet) on a board that contains similar objects. This structure is the basis of Pinterest’s data and this is how Lens can provide high-quality references of the object in similar as well as richly varied contexts. As an example, if Lens detects Apple as an object, it can recommend Apple Pie Recipe and Apple farming techniques to the user, which belong to very separate domains. To implement this functionality, Pinterest has separated Lens’ architecture into two separate components.
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In the first component, a query understanding layer has been implemented. Here, certain visual features are generated like lighting conditions and image quality. Basic object detection and colour features are also implemented as a part of the query understanding layer. Image Classification algorithms are also used to generate annotations and categories for the queried images.
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In the second component, results from many models are blended to generate a continuous feed of results relevant to the queried image. Visual search is one of these models which returns visually similar results where the object and its context are strongly maintained. Another one would be an object search model which gives results that have the given object in the results. The third model uses the generated categories and annotations from the query understanding layer to do a textual image search to get the results. The blender does a very good job of dynamically changing the blending ratios as the user scrolls through the search results. Confidence thresholds are also implemented such that low confidence results from the query understanding layer are skipped while generating the final search results. Evidently, the basic technology supporting Pinterest Lens is object detection. It supports Visual Search, Object Search, and Image Search. Let’s understand in detail how object detection is done at Pinterest.
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In step 1, the input image is fed into an already trained CNN, which identifies regions that might contain objects, and converts the image into a feature map. Once the feature map is generated, in step 2, A Region proposal network is used to extract sub-mappings of various sizes and aspect ratios from the original feature map. These sub-mappings are fed into a binary softmax classifier which predicts whether a given sub-region contains an object. If a promising object is found, it is indexed into a list of possible objects, along with the region bounded sub-mappings, which are then classified into object categories by an object detection network.
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This is how Lens has been able to use Computer Vision and Pinterest’s bipartite graph structure to generate highly relevant and diverse results for visual search.
GamingEye-tracking is a technology that makes it possible for a computer system to know where a person is looking. An eye-tracking system can detect and quantify the presence, attention, and focus of the user. Eye-tracking systems were primarily developed for gaming analysis to quantify the performance of top gamers; but since then, these systems have found utility in various devices like consumer and business computers.
Source
Tobii is the world leader in eye-tracking tech and the applications they support have moved from gaming to gesture control and VR. Data acquisition by Tobii is done via a custom-designed sensor which is pre-set on the device where eye-tracking information is needed. The system consists of projectors and customised image sensors as well as custom pre-processing with embedded algorithms. Projectors are used to create an infrared light-map on the eyeballs. The camera takes high-resolution images of the eyeballs to capture the movement pattern. Computer Vision algorithms are then used to map the movement of eyeballs from the images onto a point on the screen, thus generating the final gaze point. The stream of temporal data thus obtained is used to determine the attention and focus of the subject.
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Workplace SafetyThe human and economic cost of workplace injuries around the world is a staggering $250 billion per year. With AI-enabled intelligent hazard detection systems, workplaces prone to a high level of onsite injuries are realizing the decrease in number as well as the severity of injuries. Imagine a resource that works 24/7 without fatigue and keeps a watchful eye on whether safety regulations are being followed in the workplace!
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Intenseye is an AI-powered employee health and safety software platform that helps the world’s largest enterprises to scale employee health and safety across their facility footprints. With real-time 24/7 monitoring of safety procedures, they can detect unsafe practices in the workplace, flag them and generate employee level safety scores along with live safety-norm violation notifications. Assistance is also provided in operationalising the response procedures, thus helping the employer in being compliant with the safety norms. Along with normal compliance procedures, they have also developed Covid-19 compliance features which help in tracking whether covid appropriate norms like masking and social distancing are being followed in the workplace.
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The product is implemented on two levels. The basic driver for the product is Computer Vision. A big challenge for the org was to implement real-time predictions from live video streams. This is inherently a slow process and requires parallelisation and GPU computation to achieve due to the nature of the data pipeline. The vision systems employed range from anomaly detection to object as well as activity detection. Finally, the predictions generated are aggregated to rapidly create analysis, scores, and alerts on the suite available with the EHS professionals in the workplace who can ensure compliance from the workers.
Intenseye has developed general-purpose suites for workplaces, like PPE Detection, Area Controls, Vehicle Controls, Housekeeping, Behavioural Safety, and Pandemic Control measures. With their AI-based inspection system, Intenseye has been able to add a lot of value to the businesses they support. Along with saved lives, there have been decreased costs in damages, a boost in productivity, improved morale, and gain in goodwill for their clients.
Retail StoresIn-aisle innovation is shifting how we perceive the future of retail, opening the possibilities of what can be done to shape customer experiences. Computer vision is posed to tackle many retail store pain points and can potentially transform both customer and employee experiences.
Amazon opened the doors on its first AmazonGo store in Seattle in January 2023 after a year of testing its Just Walk Out technology on its employees at its headquarters. This concept creates an active shopping session, links the shopping activity to the amazon app, and allows the customer to have a truly hassle-free experience. It eliminates the need to group with other buyers at checkout points to make the purchase, thus creating a unique value proposition in the current pandemic stricken world.
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How does Amazon do it? The process which is primarily driven by Computer Vision can be divided into a few parts:
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Data Acquisition: Along with an array of application-specific sensors (pressure, weight, RFID), the majority of data is visual data extracted from several cameras. Visual data ranges from images to videos. Other data is also available to the algorithm, like amazon user history, customer metadata, etc.
Data Processing: Computer Vision algorithms are used to perform a wide array of tasks that capture the customer’s activity and add events to the current shopping session in real-time. These tasks include activity detection (eg. article picked up by customer), object detection (number of articles present in cart), image classification (eg. customer has a given product in the cart). Along with tracking customer activity, visual data is used to assist the staff in other store-specific operations like inventory management (object detection), store layout optimisation(customer heat maps), etc.
Charging the customer: As soon as the customer has made their purchase and moves to the store’s transition area, a virtual bill is generated on the items present in the virtual cart for the customer’s current shopping session. Once the system detects that the customer has left the store, their Amazon account is charged with that purchase.
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AmazonGo is a truly revolutionary use of computer vision and AI. It solves a very inherent problem for the in-store retail customers, assisting staff, improving overall productivity, and generating useful insights from data in the process. Though, it still needs to make economic sense in the post-pandemic world. There are privacy concerns that also need to be addressed before this form of retail shopping is adopted by the larger world.
Industrial Quality ControlThe ability of Computer Vision to distinguish between different characteristics of products makes it a useful tool for object classification and quality evaluation. Vision applications can sort and grade materials by different features, such as size, shape, colour, and texture so that the losses incurred during harvesting, production, and marketing can be minimized.
The involvement of humans introduces a lot of subjectivity, fatigue, delay, and irregularity in the quality control process for a modern-day manufacturing line/sorting line. Machines are able to sort unacceptable items better than humans 24/7 and with high consistency. The only requirement is a robust computer vision system. Vision systems are being implemented for the same on a large scale to push products in the refurbished market or improve on manufacturing shortcomings.
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Let’s solve the problem of detecting cracks in smartphone displays. The system needs a few important things to function as intended.
Data: Quality data is imperative for the success of any machine learning system. Here, we require a camera system that captures the screen at various angles in different lighting conditions.
Labels: We might want to eliminate human shortcomings in the process, but quality labels generated by humans under normal working conditions are crucial for the success of a vision system. For a robust, large-scale process, it is best to employ multiple professional labellers and let them agree on different labelling results, thus eliminating subjectivity from the process.
Modelling: Models must be designed and deployed keeping in mind the throughput required for a given sorting line. Simple classification/object detection models are enough for detecting cracks. The main focus should be on prediction time which will be different for different sorting lines.
Inference and Monitoring: Models can be initially deployed in shadow mode where their performance is evaluated against human workers on live data. They can be deployed if the performance is acceptable, otherwise, another modelling iteration can be adopted. Data Drift must be monitored manually / automatically along with model performance when data drift is high. Retraining should be done when results are not acceptable.
Many manufacturing lines have implemented automated control systems for screen quality, be it televisions, phones, tablets, or other smart devices. Companies like Intel are also providing services to develop such systems which provide great value to many businesses.
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Another quality control application using vision systems has been launched by food technology specialist, OAL, who have developed a vision system, April Eye, for automatic date code verification. The CV-based system achieves full automation of the date-verification process, removing the need for a human operator. They have reduced the risk of product recalls and emergency product withdrawals (EPWs) which are majorly caused by human error on packaging lines. The product is aimed at solving mistakes that cost food manufacturers £60-80 million a year. The system has managed to increase throughput substantially to 300 correct packs a minute. The precision of the system is also highly acceptable. A neural network trained on acceptable and unacceptable product date codes is used to generate predictions on live date codes in real-time. The system ensures that no incorrect labels can be released into the supply chain, thus protecting consumers, margins, and brands.
Computer Vision adds value to quality control processes in a number of ways. It adds automation to the process, thus making it productive, efficient, precise, and cost-effective.
With a deeper understanding of how scalable vision-based systems are implemented at leading product-based organisations, you are now perfectly equipped to disrupt yet another industry using computer vision. May the accuracy be with you.
ReferencesApril Eye
About meKshitij Gangwar
For immediate exchange of thoughts, please write to me at [email protected]
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Pebble Time Review: A Modern Day Smartwatch, That’s Stuck In The Past (Video)
Pebble Time is an interesting smartwatch. This is a bit different from anything else out there in the sense that it’s super low-tech. There’s absolutely nothing flashy about it. As far as smartwatches go, this is as barebones as it gets, but is that bad thing?
I’ve been wearing Pebble Time for about a week now and being an owner of the last generation, this was extremely easy to get used to. Pebble Time’s design is thinner, lighter, and more comfortable to use than its predecessor. It definitely feels like a second generation product, but still feels a bit behind in certain areas when compared to Apple Watch or Android Wear. The materials being used are mostly plastic, but there is a nice metal bezel around the outside (of the inner bezel) and Gorilla Glass covering the screen. Pebble Time is also water-resistant and feels quite durable.
Pebble Time features a colored backlit e-paper display, that’s easy on the eyes and works good enough to make your way through the menus and options Pebble Time has to offer. The company’s new Time OS is a re-imagining of the software you’ll use to navigate it. Everything on Pebble Time is controlled with four buttons. Animations are snappy and fun and the interface is very smooth, but you won’t find any touch screen here.
Check out our Pebble Time review video below:
One of the main features here is Pebble’s Timeline. This is accessed from the main screen using the up and down buttons on the right side. This is essentially just a look into the past and future based on your calendar events. From an event you can dig deeper to find out more information about it or you can remove it. I never found much use for it, but it’s a handy feature to have.
Within the Pebble Time companion app, you’ll find additional watch faces that can be downloaded, along with third-party apps for things like Twitter, Philips Hue, and Nest to name a few. The companion app is very straight forward and easy to use and apps can be arranged in a custom order. Pebble’s developer community is pretty strong and there’s a lot to discover here, but I really only ever cared about having notifications on my wrist.
The main reason I used Pebble Time was for notifications. When each notification arrive, you’ll feel a solid short vibration on your wrist. Unfortunately, it’s quite limited when compared to native smartwatch solutions on both iOS and Android, but one thing is clear: Pebble Time is much more useful if you’re using an Android device. A lot of what makes this a good smartwatch is currently unavailable for iOS users.
Three major things about Pebble Time and iOS that are extremely annoying:
There’s no granular control of notifications on iOS. It’s all or nothing here, unless you’d like to disable those app’s notifications on your iPhone as well.
There’s no way to prevent your iPhone from buzzing if notifications are being displayed on Pebble Time.
There are currently no voice replies using Pebble Time’s build in microphone.
To be fair, I think these limitations might have to do with restrictions in iOS. Pebble notes that voice replies to Gmail notifications are coming soon, but currently there’s nothing else to look forward to with Pebble Time on iOS. On Android, this is a completely different experience. Everything I’ve mentioned works great across all compatible apps and services. Pebble Time’s voice dictation feature will work with hundreds of apps already available on Android.
The best feature Pebble Time has to offer is its battery life. I used Pebble for four days straight and still had 40% battery life. Regardless of anything that’s limiting Pebble Time’s functionality, there’s definitely nothing standing in the way of battery life.
The real story here centers around the new hardware and software design. It’s nice for sure, but not the prettiest looking smartwatch available. Here’s the bottom line: Is Pebble Time a must-have smartwatch? Nope. Are there better solutions available? Yep. Pebble Time only set me back around $180, but I’d rather pay a bit more (or less in the case of certain Android Wear devices) for something that offers native integration on either iOS or Android. I’m sure software updates will improve Pebble Time in the future, but as for now, it feels like it’s stuck in the past.
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