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While machine learning offers many benefits to the company, try to move your employees around to other human-based areas of the business. Here are some ways that you can begin using machine learning in a warehouse environment.

6 Ways Machine Learning Is Revolutionizing the Warehouse

Cory Levins, Director of Business Development | Air Sea Containers

Advancements in technology are impacting the warehouse industry all the time with new ways to track shipments, communicate, organize warehouses and more. But, machine learning is one of the newest types of technology on the block, and it's helping to improve warehouse safety and keep warehouses more organized and on top of shipments. When it comes to machine learning, its important to remember how this is impacting human jobs as more automated machines take the place of human workers. If you own or manage a warehouse and youre interested in integrating machine learning tech, its important to consider what will happen to employees. While machine learning offers many benefits to the company, try to move your employees around to other human-based areas of the business. Here are some ways that you can begin using machine learning in a warehouse environment.

Machine learning is a phrase used to refer to a series of algorithms and statistics that a computer uses to notice patterns and essentially learn how to complete a given task. Machine learning is a subset of artificial intelligence, which is the development of a computer that is able to carry out tasks typically performed only by humans. Unlike with robotic machines that are programmed to do one specific task or movement, machine learning encourages the computer to analyze and understand data so it can figure out how to do the task, not mindlessly carry out an order. This means that machines with DRL (deep reinforcement learning) are able to sense their surroundings and react to a limited extent. One of the greatest benefits of machine learning is the fact that it can eliminate a lot of human errors, though machine learning is not perfect either.

Machine learning can vastly improve your overall supply chain because these machines were designed to pick up patterns. If a device analyzes your supply chain system, it may be able to notice areas where defeats are created or identify parts of the system that can be improved, making the entire process more efficient. With a human assessor, it would take more time to inspect each product and notice a pattern of defective items. A computer can do this analysis quickly, and there is a smaller chance that the machine will accidentally skip over a defeat, whereas the human eye may miss something that could become a larger problem in the future.

Many companies are beginning to move toward entirely automated warehouses in which machines perform the tasks of preparing packages for shipment and tracking inventory. Although this would eliminate human jobs, it would be much more efficient and, again, eliminate the chance of error. Still, there would be a need for humans to help fix machines and oversee the process, shifting the jobs from one segment of the warehouse industry to another. In the beginning, it may seem expensive to invest in the equipment, but in the long run, having machine-learned robots run the warehouse would reduce your overhead costs.

Not only can machine-learned computers package your shipments, but they are also able to organize products. From the moment a shipment enters the warehouse, these devices can scan and report the shipment, keeping accurate track of your inventory. For employees working in warehouses, this task can be monotonous and time-consuming, but when machines are used in place of humans, the task can be completed much quicker and leave your employees with more time to carry out tasks that only a human can accomplish.

If your warehouse is carrying items that have a specific expiration date or food products that can go bad, you want to ensure you dont store any of these items past the sell-by date. With machines that have been conditioned with some level of artificial intelligence, its easy to transmit data detailing when items will expire and need to be sold or disposed of. Humans can easily forget which items need to be sent out first, which causes waste when products are thrown away. Machine learning can help reduce this issue. Integrating eco-friendly packaging into your warehouse procedures can also help reduce waste by lowering your warehouses carbon footprint.

Its always important to provide your customers with actual human customer support, as it can be frustrating trying to explain an issue to a robot. There are still some benefits to machine-learned customer support. If you have a website for your warehouse, you can add a support chat feature that allows people to communicate with a computer via a messaging app. This is a great way to allow people to ask quick and simple questions without clogging your phone lines or asking people to wait on long hold times. You can also use automated customer support on your phone line to filter out simple questions, but you must always offer a human support option as well.

Warehouses can be dangerous places with so many heavy boxes and large machinery moving around one space. Of course, you should have strict safety practices in place to keep your employees as safe as possible. When you integrate machines into the process, you can make the environment even less dangerous and improve warehouse safety. If AI robots are responsible for driving dangerous machinery and storing inventory in hard-to-reach places, its less likely that an accident will occur. And even if it does, a human worker will not be the one to suffer the consequences.

If youre looking for ways to upgrade your warehousing procedures, consider adding some machines that have been programmed with machine learning capabilities. Youll be able to make your business more efficient and reduce the chances of workplace injuries. You dont need to automate the entire warehouse if you value the work and impact of human employees, but youll find that machine-learned robots can speed up the process and make things easier for workers as well.

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6 Ways Machine Learning Is Revolutionizing the Warehouse - Robotics Tomorrow

Machine Learning in Tax and Accounting:

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2020-2026 Machine Learning in Tax and Accounting Market Status and Forecast, By Players, Types and Applications - Science In Me

Both weather and climate models have improved drastically in recent years, as advances in one field have tended to benefit the other. But there is still significant uncertainty in model outputs that are not quantified accurately. Thats because the processes that drive climate and weather are chaotic, complex, and interconnected in ways that researchers have yet to describe in the complex equations that power numerical models.

Historically, researchers have used approximations called parameterizations to model the relationships underlying small-scale atmospheric processes and their interactions with large-scale atmospheric processes. Stochastic parameterizations have become increasingly common for representing the uncertainty in subgrid-scale processes, and they are capable of producing fairly accurate weather forecasts and climate projections. But its still a mathematically challenging method. Now researchers are turning to machine learning to provide more efficiency to mathematical models.

Here Gagne et al. evaluate the use of a class of machine learning networks known as generative adversarial networks (GANs) with a toy model of the extratropical atmospherea model first presented by Edward Lorenz in 1996 and thus known as the L96 system that has been frequently used as a test bed for stochastic parameterization schemes. The researchers trained 20 GANs, with varied noise magnitudes, and identified a set that outperformed a hand-tuned parameterization in L96. The authors found that the success of the GANs in providing accurate weather forecasts was predictive of their performance in climate simulations: The GANs that provided the most accurate weather forecasts also performed best for climate simulations, but they did not perform as well in offline evaluations.

The study provides one of the first practically relevant evaluations for machine learning for uncertain parameterizations. The authors conclude that GANs are a promising approach for the parameterization of small-scale but uncertain processes in weather and climate models. (Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2019MS001896, 2020)

Kate Wheeling, Science Writer

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Machine Learning Improves Weather and Climate Models - Eos

The legal profession, the technology industry and the relationship between the two are in a state of transition. Computer processing power has doubled every year for decades, leading to an explosion in corporate data and increasing pressure on lawyers entrusted with reviewing all of this information.

Now, the legal industry is undergoing significant change, with the advent of machine learning technology fundamentally reshaping the way lawyers conduct their day-to-day practice. Indeed, whilst technological gains might once have had lawyers sighing at the ever-increasing stack of documents in the review pile, technology is now helping where it once hindered. For the first time ever, advanced algorithms allow lawyers to review entire document sets at a glance, releasing them from wading through documents and other repetitive tasks. This means legal professionals can conduct their legal review with more insight and speed than ever before, allowing them to return to the higher-value, more enjoyable aspect of their job: providing counsel to their clients.

In this article, we take a look at how this has been made possible.

Practicing law has always been a document and paper-heavy task, but manually reading huge volumes of documentation is no longer feasible, or even sustainable, for advisors. Even conservatively, it is estimated that we create 2.5 quintillion bytes of data every day, propelled by the usage of computers, the growth of the Internet of Things (IoT) and the digitalisation of documents. Many lawyers have had no choice but resort to sampling only 10 per cent of documents, or, alternatively, rely on third-party outsourcing to meet tight deadlines and resource constraints. Whilst this was the most practical response to tackle these pressures, these methods risked jeopardising the quality of legal advice lawyers could give to their clients.

Legal technology was first developed in the early 1970s to take some of the pressure off lawyers. Most commonly, these platforms were grounded on Boolean search technology, requiring months and even years building the complex sets of rules. As well as being expensive and time-intensive, these systems were also unable to cope with the unpredictable, complex and ever-changing nature of the profession, requiring significant time investment and bespoke configuration for every new challenge that arose. Not only did this mean lawyers were investing a lot of valuable time and resources training a machine, but the rigidity of these systems limited the advice they could give to their clients. For instance, trying to configure these systems to recognise bespoke clauses or subtle discrepancies in language was a near impossibility.

Today, machine learning has become advanced enough that it has many practical applications, a key one being legal document review.

Machine learning can be broadly categorised into two types: supervised and unsupervised machine learning. Supervised machine learning occurs when a human interacts with the system in the case of the legal profession, this might be tagging a document, or categorising certain types of documents, for example. The machine then builds its understanding to generate insights to the user based on this human interaction.

Unsupervised machine learning is where the technology forms an understanding of a certain subject without any input from a human. For legal document review, the unsupervised machine learning will cluster similar documents and clauses, along with clear outliers from those standards. Because the machine requires no a priori knowledge of what the user is looking for, the system may indicate anomalies or unknown unknowns- data which no one had set out to identify because they didnt know what to look for. This allows lawyers to uncover critical hidden risks in real time.

It is the interplay between supervised and unsupervised machine learning that makes technology like Luminance so powerful. Whilst the unsupervised part can provide lawyers with an immediate insight into huge document sets, these insights only increase with every further interaction, with the technology becoming increasingly bespoke to the nuances and specialities of a firm.

This goes far beyond more simplistic contract review platforms. Machine learning algorithms, such as those developed by Luminance, are able to identify patterns and anomalies in a matter of minutes and can form an understanding of documents both on a singular level and in their relationship to each another. Gone are the days of implicit bias being built into search criteria, since the machine surfaces all relevant information, it remains the responsibility of the lawyer to draw the all-important conclusions. But crucially, by using machine learning technology, lawyers are able to make decisions fully appraised of what is contained within their document sets; they no longer need to rely on methods such as sampling, where critical risk can lay undetected. Indeed, this technology is designed to complement the lawyers natural patterns of working, for example, providing results to a clause search within the document set rather than simply extracting lists of clauses out of context. This allows lawyers to deliver faster and more informed results to their clients, but crucially, the lawyer is still the one driving the review.

With the right technology, lawyers can cut out the lower-value, repetitive work and focus on complex, higher-value analysis to solve their clients legal and business problems, resulting in time-savings of at least 50 per cent from day one of the technology being deployed. This redefines the scope of what lawyers and firms can achieve, allowing them to take on cases which would have been too time-consuming or too expensive for the client if they were conducted manually.

Machine learning is offering lawyers more insight, control and speed in their day-to-day legal work than ever before, surfacing key patterns and outliers in huge volumes of data which would normally be impossible for a single lawyer to review. Whether it be for a due diligence review, a regulatory compliance review, a contract negotiation or an eDiscovery exercise, machine learning can relieve lawyers from the burdens of time-consuming, lower value tasks and instead frees them to spend more time solving the problems they have been extensively trained to do.

In the years to come, we predict a real shift in these processes, with the latest machine learning technology advancing and growing exponentially, and lawyers spending more time providing valuable advice and building client relationships. Machine learning is bringing lawyers back to the purpose of their jobs, the reason they came into the profession and the reason their clients value their advice.

James Loxam, CTO, Luminance

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The impact of machine learning on the legal industry - ITProPortal

Machine learning is by no means a new phenomenon. It has been used in various forms for decades, but it is very much a technology of the present due to the massive increase in the data upon which it thrives. It has been widely adopted by businesses, reducing the time and improving the value of the insight they can distil from large volumes of customer data.

However, in the public sector there is a different story. Despite being championed by some in government, machine learning has often faced a reaction of concern and confusion. This is not intended as general criticism and in many cases it reflects the greater value that civil servants place on being ethical and fair, than do some commercial sectors.

One fear is that, if the technology is used in place of humans, unfair judgements might not be noticed or costly mistakes in the process might occur. Furthermore, as many decisions being made by government can dramatically affect peoples lives and livelihood then often decisions become highly subjective and discretionary judgment is required. There are also those still scarred by films such as iRobot, but thats a discussion for another time.

Fear of the unknown is human nature, so fear of unfamiliar technology is thus common. But fears are often unfounded and providing an understanding of what the technology does is an essential first step in overcoming this wariness. So for successful digital transformation not only do the civil servants who are considering such technologies need to become comfortable with its use but the general public need to be reassured that the technology is there to assist, not replace, human decisions affecting their future health and well-being.

Theres a strong case to be made for greater adoption of machine learning across a diverse range of activities. The basic premise of machine learning is that a computer can derive a formula from looking at lots of historical data that enables the prediction of certain things the data describes. This formula is often termed an algorithm or a model. We use this algorithm with new data to make decisions for a specific task, or we use the additional insight that the algorithm provides to enrich our understanding and drive better decisions.

For example, machine learning can analyse patients interactions in the healthcare system and highlight which combinations of therapies in what sequence offer the highest success rates for patients; and maybe how this regime is different for different age ranges. When combined with some decisioning logic that incorporates resources (availability, effectiveness, budget, etc.) its possible to use the computers to model how scarce resources could be deployed with maximum efficiency to get the best tailored regime for patients.

When we then automate some of this, machine learning can even identify areas for improvement in real time and far faster than humans and it can do so without bias, ulterior motives or fatigue-driven error. So, rather than being a threat, it should perhaps be viewed as a reinforcement for human effort in creating fairer and more consistent service delivery.

Machine learning is an iterative process; as the machine is exposed to new data and information, it adapts through a continuous feedback loop, which in turn provides continuous improvement. As a result, it produces more reliable results over time and evermore finely tuned and improved decision-making. Ultimately, its a tool for driving better outcomes.

The opportunities for AI to enhance service delivery are many. Another example in healthcare is Computer Vision (another branch of AI), which is being used in cancer screening and diagnosis. Were already at the stage where AI, trained from huge libraries of images of cancerous growths, is better at detecting cancer than human radiologists. This application of AI has numerous examples, such as work being done at Amsterdam UMC to increase the speed and accuracy of tumour evaluations.

But lets not get this picture wrong. Here, the true value is in giving the clinician more accurate insight or a second opinion that informs their diagnosis and, ultimately, the patients final decision regarding treatment. A machine is there to do the legwork, but the human decision to start a programme for cancer treatment, remains with the humans.

Acting with this enhanced insight enables doctors to become more efficient as well as effective. Combining the results of CT scans with advanced genomics using analytics, the technology can assess how patients will respond to certain treatments. This means clinicians avoid the stress, side effects and cost of putting patients through procedures with limited efficacy, while reducing waiting times for those patients whose condition would respond well. Yet, full-scale automation could run the risk of creating a lot more VOMIT.

Victims Of Modern Imaging Technology (VOMIT) is a new phenomenon where a condition such as a malignant tumour is detected by imaging and thus at first glance it would seem wise to remove it. However, medical procedures to remove it carry a morbidity risk which may be greater than the risk the tumour presents during the patients likely lifespan. Here, ignorance could be bliss for the patient and doctors would examine the patient holistically, including mental health, emotional state, family support and many other factors that remain well beyond the grasp of AI to assimilate into an ethical decision.

All decisions like these have a direct impact on peoples health and wellbeing. With cancer, the faster and more accurate these decisions are, the better. However, whenever cost and effectiveness are combined there is an imperative for ethical judgement rather than financial arithmetic.

Healthcare is a rich seam for AI but its application is far wider. For instance, machine learning could also support policymakers in planning housebuilding and social housing allocation initiatives, where they could both reduce the time for the decision but also make it more robust. Using AI in infrastructural departments could allow road surface inspections to be continuously updated via cheap sensors or cameras in all council vehicles (or cloud-sourced in some way). The AI could not only optimise repair work (human or robot) but also potentially identify causes and then determine where strengthened roadways would cost less in whole-life costs versus regular repairs or perhaps a different road layout would reduce wear.

In the US, government researchers are already using machine learning to help officials make quick and informed policy decisions on housing. Using analytics, they analyse the impact of housing programmes on millions of lower-income citizens, drilling down into factors such as quality of life, education, health and employment. This instantly generates insightful, accessible reports for the government officials making the decisions. Now they can enact policy decisions as soon as possible for the benefit of residents.

While some of the fears about AI are fanciful, there is a genuine cause for concern about the ethical deployment of such technology. In our healthcare example, allocation of resources based on gender, sexuality, race or income wouldnt be appropriate unless these specifically had an impact on the prescribed treatment or its potential side-effects. This is self-evident to a human, but a machine would need this to be explicitly defined. Logically, a machine would likely display bias to those groups whose historical data gave better resultant outcomes, thus perpetuating any human equality gap present in the training data.

The recent review by the Committee on Standards in Public Life into AI and its ethical use by government and other public bodies concluded that there are serious deficiencies in regulation relating to the issue, although it stopped short of recommending the establishment of a new regulator.

The review was chaired by crossbench peer Lord Jonathan Evans, who commented:

Explaining AI decisions will be the key to accountability but many have warned of the prevalence of Black Box AI. However our review found that explainable AI is a realistic and attainable goal for the public sector, so long as government and private companies prioritise public standards when designing and building AI systems.

Fears of machine learning replacing all human decision-making need to be debunked as myth: this is not the purpose of the technology. Instead, it must be used to augment human decision-making, unburdening them from the time-consuming job of managing and analysing huge volumes of data. Once its role can be made clear to all those with responsibility for implementing it, machine learning can be applied across the public sector, contributing to life-changing decisions in the process.

Find out more on the use of AI and machine learning in government.

Simon Dennis, Director of AI & Analytics Innovation, SAS UK

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Machine learning: the not-so-secret way of boosting the public sector - ITProPortal

Regal Intelligence has added latest report on Machine Learning Software Market in its offering. The global market for Machine Learning Software is expected to grow impressive CAGR during the forecast period. Furthermore, this report provides a complete overview of the Machine Learning Software Market offering a comprehensive insight into historical market trends, performance and 2020 outlook.

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Key Companies included in this report: Microsoft, Google, TensorFlow, Kount, Warwick Analytics, Valohai, Torch, Apache SINGA, AWS, BigML, Figure Eight, Floyd Labs

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What Will Be the Future Prospects Of the Machine Learning Software Market? Trends, Factors, Opportunities and Restraints - Science In Me

Based on the data thats collected, it tells me how Im moving compared to previously and how Im moving compared to my ideal movement signature, as they call it, Mr. Ross said. Sparta Science then tailors his workouts to move him closer to that ideal.

The Pittsburgh Steelers, the Detroit Lions and the Washington Redskins, among others, use the system regularly, Dr. Wagner said. Sparta Science is also used to evaluate college players in the National Football Leagues annual scouting combine.

Of course, it is inevitable that machine learnings predictive power would be applied to another lucrative end of the sports industry: betting. Sportlogiq, a Montreal-based firm, has a system that primarily relies on broadcast feeds to analyze players and teams in hockey, soccer, football and lacrosse.

Mehrsan Javan, the companys chief technology officer and one of its co-founders, said the majority of National Hockey League teams, including the last four Stanley Cup champions, used Sportlogiqs system to evaluate players.

Josh Flynn, assistant general manager for the Columbus Blue Jackets, Ohios professional hockey franchise, said the team used Sportlogiq to analyze players and strategy. We can dive levels deeper into questions we have about the game than we did before, Mr. Flynn said.

But Sportlogiq also sells analytic data to bookmakers in the United States, helping them set odds on bets, and hopes to sell information to individual bettors soon. Mr. Javan is looking to hire a vice president of betting.

They key to all of this sports-focused technology is data.

Algorithms come and go, but data is forever, Mr. Alger is fond of saying. Computer vision systems have to be told what to look for, whether it be tumors in an X-ray or bicycles on the road. In Seattle Sports Sciences case, the computers must be trained to recognize the ball in various lighting conditions as well as understand which plane of the foot is striking the ball.

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Want to Be Better at Sports? Listen to the Machines - The New York Times

This article is part of our latest Artificial Intelligence special report, which focuses on how the technology continues to evolve and affect our lives.

Most dog owners intuitively understand what their pet is saying. They know the difference between a bark for Im hungry and one for Im hurt.

Soon, a device at home will be able to understand them as well.

Furbo, a streaming camera that can dispense treats for your pet, snap photos and send you a notification if your dog is barking, provides a live feed of your home that you can check on a smartphone app.

In the coming months, Furbo is expected to roll out a new feature that allows it to differentiate among kinds of barking and alert owners if a dogs behavior appears abnormal.

Thats sort of why dogs were hired in the first place, to alert you of danger, said Andrew Bleiman, the North America general manager for Tomofun, the company that makes Furbo. So we can tell you not only is your dog barking, but also if your dog is howling or whining or frantically barking, and send you basically a real emergency alert.

The ever-expanding world of pet-oriented technology now allows owners to toss treats, snap a dog selfie and play with the cat all from afar. And the artificial intelligence used in such products is continuing to refine what we know about animal behavior.

Mr. Bleiman said the new version of Furbo was a result of machine learning from the video data of thousands of users. It relied on 10-second clips captured with its technology that users gave feedback on. (Furbo also allows users to opt out of sharing their data.)

The real evolution of the product has been on the computer vision and bioacoustics side, so the intelligence of the software, he said. When you have a camera that stares at a dog all day and listens to dogs all day, the amount of data is just tremendous.

The Furbo team is even able to refine the data by the breed or size of a dog: I can tell you, for example, that on average, at least as much as our camera picks up, a Newfoundland barks four times a day and a Husky barks 36 times a day.

Petcube is another interactive pet camera, the latest iteration of which is equipped with the Amazon Alexa voice assistant.

Yaroslav Azhnyuk, the companys chief executive and co-founder, is confident that A.I. is helping pet owners better understand their animals behavior. The company is working on being able to detect unusual behaviors.

We started applying algorithms to understand pet behavior and understand what they might be trying to say or how they are feeling, he said. We can warn you that OK, your dogs activity is lower than usual, you should maybe check with the vet.

Before the coronavirus pandemic forced many pet owners to work from home during the day, they were comforted by the ability to check on their pet in real time, which had driven demand for all kinds of cameras. Mr. Bleiman said the average Furbo user would check on their pet more than 10 times a day during the workweek.

Petcube users spent about 50 minutes a week talking to their pet through the camera, Mr. Azhnyuk said.

The same way you want to call your mom or child, you want to call your dog or cat, he said. Weve seen people using Petcubes for turtles and for snakes and chickens and pigs, all kinds of animals.

Now that shes working from home as part of measures to contain the spread of coronavirus in New York City, Patty Lynch, 43, has plenty of time to watch her dog, Sadie. When shes away from her Battery Park apartment, she uses a Google Nest to keep an eye on her. Ms. Lynch originally bought the camera three years ago to stream video of Sadie while she recovered from surgery.

I get alerts whenever she moves around, Ms. Lynch said. I also get noise alerts if she starts barking at something. Ill be able to go in and then see her in real time and figure out what shes doing.

Sometimes I just like to check in on her, she said. I just look at her and she makes me smile.

Lionel P. Robert Jr., associate professor at the University of Michigans school of information and a core faculty member at Michigans Robotics Institute, said A.I.-enabled technology has so far centered on the owners need for assurance that their pet was OK while they were away from home.

He predicted that future technology would focus more on the wellness of the pet.

There are a lot of people using these cameras because when they see their pet they feel assured and they feel comfortable. Right now, its less for the pet and more for the humans, he said.

Imagine if all that data was being fed to your veterinarian in real time and theyre sending back data. The idea of well-being for the pet, its weight, how far its walking.

Mr. Robert noted that other parts of the world had gone a step further with technology: Theyre actually adopting robotic pets.

While products like Petcube and Furbo are mostly used by dog owners, there are A.I. devices out there for cats as well. Many people track them throughout the day using interactive cameras, and one start-up has devised an intelligent laser for automated playtime.

Yuri Brigance came up with the idea about four years ago, after his divorce. He was away from the house, working up to 10 hours a day, and was worried about his two cats at home.

This idea came up of using a camera to track animals, where their positions are in the room and moving the laser intelligently instead of randomly so that they have something more real to chase, he said.

The result was Felik, a toy that can be scheduled via an app for certain playtimes and has features such as zone restriction, which designates areas in the home the laser cant go, such as on furniture.

Mr. Brigance said his product did not store video in the cloud and required an internet connection to work, like many video products. It analyzes data in the device.

We use machine-learning models to perform whats called semantic segmentation, which is basically separating the background, the room and all the objects in it, from interesting objects, things that are moving, like cats or humans, Mr. Brigance explained.

The device then determines where the cat has been and what it is currently doing, and predicts what it is about to do next, so it can create a playful game that mirrors chasing live prey.

The laser toy, Mr. Brigance said, has provided his cats, and those of his customers, with hours upon hours of playtime.

Some people are using it almost on a daily basis and theyre recording things like where they used to have a cat that would scratch furniture, that would get really agitated if it had nothing to do, that this actually prevents them from destroying the house, he said.

Or cats that meow in the morning and try to wake up their owners if you set a schedule for this thing to activate in the morning, it can distract the cat and let you sleep a little bit longer.

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With A.I., the Secret Life of Pets Is Not So Secret - The New York Times

TLDR: These two full-service video production kits from iRig including a multi-function stand, mic and ring light to immediately upgrade your videos.

Youre home. Youre bored. Youve got a lot of time to kill. Youve got a smartphone. So were going to assume every single one of you checking all those boxes at some point recently also thought, I should really make a video.

Whether you want to pontificate on YouTube, interact with friends on Facebook Live streaming or just start creating phone videos that look and sound a whole lot better, its time to make a small investment in the necessary equipment to make that leap.

Thankfully, if youve already got a smartphone, then making that type of investment isnt nearly as expensive as you might think. In fact, iRig has a pair of video kits that essentially put everything you need to elevate your game altogether in one convenient package.

The iRig Video Creator Tool Bundle ($89.99; originally $99.99) offers a collection of three must-have items to give any videos a more professional look. First, the iKlip Grip Pro is a multi-function iPhone and camera stand thats practically a Transformer. After you lock in your phone, the base can convert from a secure tabletop tripod to a handheld monopod to a telescoping selfie stick style arrangement that extends your camera for a shooting distance of up to 2 feet. It also comes with a standard UNC -inch universal mount if youd rather shoot with a small digital camera or a GoPro action cam.

As for your audio, the iRig Mic Lav is a compact pro-grade lavalier mic that attaches right to your phone for premium sound recording that your phones own built-in mic cant touch. And youll end up looking a whole lot better with the 6-inch LED ring light with adjustable color and brightness levels to better illuminate, soften harsh glares and overall flatter your face.

Meanwhile, you can also take a step up to the iRig Video Creator HD Tool Bundle ($169.99; originally $199.99). In addition to the iKlip Grip Pro and a larger 10-inch ring light, this package gives you a big audio boost with the iRig Mic HD 2, a handheld digital microphone for capturing radio-ready sound quality that connects via Lightning or USB cable.

Either way, each package will have you producing videos that instantly boost your credibilityeven if youre blathering on with the same stupid stuff youve always said.

Prices are subject to change.

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Turn your standard smartphone video into a professional-grade machine and stream on Twitch or Instagram Live - The Next Web