When to Use Machine Learning Does Your App Really Need ML?

Humans may appear to be swiftly overtaken in industries where AI is becoming more extensively incorporated. However, humans are still capable of doing a variety of complicated activities better than AI. For the time being, tasks that demand creativity are beyond the capabilities of AI computers. Netflix uses machine learning to analyze viewing habits and recommend shows and movies tailored to each user’s preferences, enhancing the streaming experience. AI enhances data security by detecting and responding to cyber threats in real-time.

They’re reporting productivity and efficiency gains, but they’re also grappling with data privacy, security and ethical challenges as they deploy AI in their organizations. OpenAI’s recent reveal of its stunning generative model Sora pushed the envelope of what’s possible with text-to-video. The new model, called Genie, can take a short description, a hand-drawn sketch, or a photo and turn it into a playable video game in the style of classic 2D platformers like Super Mario Bros. The games run at one frame per second, versus the typical 30 to 60 frames per second of most modern games. As the scientists in Will’s piece say, it’s still early days in the field of AI research.

Better quality and reduction of human error

AI significantly impacts the gaming industry, creating more realistic and engaging experiences. AI algorithms can generate intelligent behavior in non-player characters ChatGPT (NPCs), adapt to player actions, and enhance game environments. One of the critical AI applications is its integration with the healthcare and medical field.

It takes a significant amount of time to develop AI systems, which is something that cannot happen in the absence of human intervention. All forms of artificial intelligence, including self-driving vehicles and robotics, as well as more complex technologies like computer vision, and natural language processing, are dependent on human intellect. While the goal of artificial intelligence is to build and create intelligent systems that are capable of doing jobs that are analogous to those performed by humans, we can’t help but question if AI is adequate on its own. This article covers a wide range of subjects, including the potential impact of AI on the future of work and the economy, how AI differs from human intelligence, and the ethical considerations that must be taken into account. You can start with „Machine Learning Steps“ as your next Chapter on your path to conquering AI and Machine Learning. Machine learning algorithms automatically acquire data and utilize it to learn.

While a Bachelor’s degree might give you a theoretical understanding of the subject, it is essential to brush up on relevant programming languages such as Python, R, SQL, and SAS. On Oct. 30, 2023, President Joe Biden signed an executive order on artificial intelligence. AI improves the capability of translation services, enabling automated, real-time translation in multiple languages. You can foun additiona information about ai customer service and artificial intelligence and NLP. Translation requires a certain level of nuance, as translators need to be able interpret body language and emotions of the speaker or in the text they are translating.

Let’s say the initial weight value of this neural network is 5 and the input x is 2. Therefore the prediction y of this network has a value of 10, while the label y_hat might have a value of 6. Minimizing the loss function directly leads to more accurate predictions of the neural network, as the difference between the prediction and the label decreases. Now that we know what the mathematical calculations between two neural network layers look like, we can extend our knowledge to a deeper architecture that consists of five layers.

What do you understand by Leaky ReLU activation function?

Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s neural network. Put simply, AI systems work by merging large with intelligent, iterative processing algorithms. This combination allows AI to learn from patterns and features in the analyzed data. Each time an Artificial Intelligence ChatGPT App system performs a round of data processing, it tests and measures its performance and uses the results to develop additional expertise. „Machine learning and graph machine learning techniques specifically have been shown to dramatically improve those networks as a whole. They optimize operations while also increasing resiliency,“ Gross said.

• In supervised machine learning, a model makes predictions or decisions based on past or labeled data.
• Convolutional neural networks (mostly geared to image recognition) and recurrent neural networks are examples of deep learning (efficient for time series problems).
• Then you take a small set of the same data to test the model, which would give good results in this case.
• They found that in certain cases, models could seemingly fail to learn a task and then all of a sudden just get it, as if a lightbulb had switched on.
• While generative AI is designed to create original content or data, discriminative AI is used for analyzing and sorting it, making each useful for different applications.

Data analysts rely on tools like Excel, SQL, and Tableau for data analysis and visualization. Data scientists use more advanced tools such as Python, R, and big data technologies like Hadoop, alongside cloud platforms like AWS for data storage and processing. AI assistants and chatbots let users book flights, rent vehicles and find accommodations online and offer a personalized booking experience. AI can also perform flight forecasting, which helps prospective travelers find the cheapest time to book a flight based on automated analysis of historical price patterns. The value of the loss function for the new weight value is also smaller, which means that the neural network is now capable of making better predictions. You can do the calculation in your head and see that the new prediction is, in fact, closer to the label than before.

Cost-Efficient ML Development

Management advisers said they see ML for optimization used across all areas of enterprise operations, from finance to software development, with the technology speeding up work and reducing human error. The benefits of machine learning can be grouped into the following four major categories, said Vishal Gupta, partner at research firm Everest Group. Lasso(also known as L1) and Ridge(also known as L2) regression are two popular regularization techniques that are used to avoid overfitting of data. These methods are used to penalize the coefficients to find the optimum solution and reduce complexity. The Lasso regression works by penalizing the sum of the absolute values of the coefficients.

It operates by constructing multiple decision trees during the training phase. The random forest chooses the decision of the majority of the trees as the final decision. Classification is used when your target is categorical, while regression is used when your target variable is continuous. Both classification and regression belong to the category of supervised machine learning algorithms. AI is used for fraud detection, credit scoring, algorithmic trading and financial forecasting.

Tools such as AI chatbots or virtual assistants can lighten staffing demands for customer service or support. In other applications—such as materials processing or production lines—AI can help maintain consistent work quality and output levels when used to complete repetitive or tedious tasks. For example, variational autoencoding could include teaching a computer program to generate human faces using photos as training data. Over time, the program learns how to simplify the photos of people’s faces into a few important characteristics — such as the size and shape of the eyes, nose, mouth, ears, and so on — and then use these to create new faces. Data scientists are in demand across many industries, including technology, finance, healthcare, retail, e-commerce, and government.

RNNs are designed to recognize patterns in data sequences, such as time series or natural language. They maintain a hidden state that captures information about previous inputs. Moreover, many skilled trades involve significant human interaction, emotional intelligence and interpersonal skills. For example, an electrician must not only fix wiring issues, but also reassure homeowners about safety concerns, which involves a level of empathy and understanding that AI cannot offer. While AI enhances medical care and diagnostics, it can’t replace the nuanced judgment and emotional support provided by doctors and healthcare workers.

Adobe Photoshop’s new Generative Fill feature is one example of the way generative AI can augment the graphic design profession. The feature lets people with no photo editing experience make photorealistic edits using a text prompt. Other tools — such as Dall-E and Midjourney — also create realistic looking images and detailed artistic renderings from a text prompt. But the argument could be made that job augmentation for some means job replacement for others. For example, if a worker’s job is made 10 times easier, the positions created to support that job might become unnecessary.

Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today. Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers. During the training process, this neural network optimizes this step to obtain the best possible abstract representation of the input data. This means that deep learning models require little to no manual effort to perform and optimize the feature extraction process. As companies have rushed to build AI models, the demand for “data annotation” and “data labeling” work has increased.

All weights between two neural network layers can be represented by a matrix called the weight matrix. Many organizations are using or exploring how to use intelligence software to improve how people learn. Another top reason organizations are adopting AI technologies is to boost productivity and generate more efficiencies, said Sreekar Krishna, U.S. leader and head of data engineering of AI at professional services firm KPMG. At Simplilearn, our industry experts deliver instruction through a format that fits your busy lifestyle, so you can quickly start working to become a machine learning expert. Start one of our courses related courses today to leverage the positive trajectory of machine learning job trends.

Data Scientists analyze vast amounts of raw information to find patterns that streamline a company’s processes. They use statistical tools and algorithms to generate insights that drive strategic business decisions. Predictive AI models analyze historical data, patterns, and trends to make informed predictions about future events or outcomes. Building a predictive AI model requires collecting and preprocessing data from various sources and cleaning it by handling missing values, outliers, or irrelevant variables. The data is then split into training and testing sets, with the training set used to train the model and the testing set used to evaluate its performance.

ReLU (or Rectified Linear Unit) is the most widely used activation function. The RNN can be used for sentiment analysis, text mining, and image captioning. Recurrent Neural Networks can also address time series problems such as predicting the prices of stocks in a month or quarter. The process of standardizing what is machine learning and how does it work and reforming data is called “Data Normalization.” It’s a pre-processing step to eliminate data redundancy. Often, data comes in, and you get the same information in different formats. In these cases, you should rescale values to fit into a particular range, achieving better convergence.

Any organization that engages regularly with large numbers of users — businesses, government units, nonprofits — will be compelled to implement AI in the decision-making processes and in their public- and consumer-facing activities. AI will allow these organizations to make most of the decisions much more quickly. ML technology and solutions may dive into customer data to understand each client segment’s unique requirements, preferences, and problem areas. As a result, businesses can develop highly tailored products/services, offers and discounts, and marketing tactics to meet the needs of specific customers. A corporation may retain long-term relationships with happy customers in the long run. Yes, machine learning jobs can come with big paychecks, but the salary can vary widely depending on location, industry, experience level, and job responsibilities.

Machine learning applications can bring you more clients, increase sales and reduce business costs. However, if not used properly, they may lead to customer outflow, money loss and reputation damage. Reinvent critical workflows and operations by adding AI to maximize experiences, real-time decision-making and business value. Also, around this time, data science begins to emerge as a popular discipline. 1980

Neural networks, which use a backpropagation algorithm to train itself, became widely used in AI applications.

Moreover, professionals are increasingly needed to manage AI projects, including deploying, monitoring, and maintaining AI systems in real-world environments, a discipline often referred to as MLOps. A deep learning engineer’s duty is to be an expert in the design and implementation of learning algorithms based on deep and complicated neural network topologies. Because the techniques utilized are more sophisticated theoretically, this is more technical work than that of a „traditional“ machine learning engineer. In agriculture, for example, deep learning enables machines to recognize plants and apply the appropriate treatment, lowering pesticide usage and increasing output. Convolutional neural networks (mostly geared to image recognition) and recurrent neural networks are examples of deep learning (efficient for time series problems).

AI ethics is a multidisciplinary field that studies how to optimize AI’s beneficial impact while reducing risks and adverse outcomes. Principles of AI ethics are applied through a system of AI governance consisted of guardrails that help ensure that AI tools and systems remain safe and ethical. Like all technologies, models are susceptible to operational risks such as model drift, bias and breakdowns in the governance structure.

It is more likely to occur with nonlinear models that have more flexibility when learning a target function. An example would be if a model is looking at cars and trucks, but only recognizes trucks that have a specific box shape. It might not be able to notice a flatbed truck because there’s only a particular kind of truck it saw in training. Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one. Meta-learning approaches can make use of RNN-based long-short term memory (LSTM) networks to train a meta-learner model to capture both short-term knowledge from each training task and long-term knowledge common to each task.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. It can generate human-like responses and engage in natural language conversations. It uses deep learning techniques to understand and generate coherent text, making it useful for customer support, chatbots, and virtual assistants.

You will be responsible for the whole Deep Learning development life cycle, including data gathering, feature engineering, model training, and testing. One will be able to develop a cutting-edge Deep Learning algorithm and apply it to real-world end-to-end production. Machine learning is not limited to a single industry; it spans healthcare, finance, e-commerce, autonomous vehicles, natural language processing, and more. This diversity allows machine learning engineers to explore different domains and apply their skills to real-world challenges. Their use of business insights derived from data enables businesses to improve sales and operations; make better decisions; and develop new products, services and policies. They use predictive modeling to forecast future events, such as customer churn, and data visualization to display research results visually.

According to the website Ditch That Textbook, parents prefer human teachers for their kids. The COVID-19 pandemic proved the challenges of remote learning for students, and most families were eager to send their kids back to school for in-person learning. However, stories of political candidates attempting to share duties with AI chatbots are surfacing. For example, Victor Miller, a mayoral candidate in Cheyenne, Wyo., filed paperwork for him and his customized ChatGPT bot named Virtual Integrated Citizen, which he calls Vic.

Additionally, you should be able to use statistical software packages and be familiar with programming languages such as Python or R. Data scientists also typically have a certification from an accredited program. Once your internship period is over, you can either join in the same company (if they are hiring), or you can start looking for entry-level positions for data scientists, data analysts, data engineers. From there you can gain experience and work up the ladder as you expand your knowledge and skills. Data science is the area of study that involves extracting knowledge from all of the data gathered.

AI skills are highly sought in various sectors, such as gaming, robotics, facial recognition software, military applications, speech and vision recognition, expert systems, and search engines. This course is ideal for people who want to use machine learning technologies to tackle real-world challenges. This predictive analytics course is offered by Coursera and is accessible as part of the $49 monthly subscription. Predictive AI courses can provide you with the skills and knowledge required to leverage the power of data for predicting and decision-making. These courses are perfect for data scientists, analysts, and business professionals interested in predictive modeling and analytics.

Types of Artificial Intelligence models are trained using vast volumes of data and can make intelligent decisions. Let’s now take a look at how the application of AI is used in different domains. AI is extensively used in the finance industry for fraud detection, algorithmic trading, credit scoring, and risk assessment. Machine learning models can analyze vast amounts of financial data to identify patterns and make predictions. Machine learning systems typically use numerous data sets, such as macro-economic and social media data, to set and reset prices. This is commonly done for airline tickets, hotel room rates and ride-sharing fares.

AI systems will likely become much more knowledgeable about each of us than we are about ourselves. Our commitment to protecting privacy has already been severely tested by emerging technologies over the last 50 years. Take advantage of Simplilearn’s Caltech Post Graduate Program In AI And Machine Learning in collaboration with Caltech to advance your profession. This AI and Machine Learning course uses case studies from prominent industries and Caltech Masterclasses to teach the best ML practices.

Deep Learning is a branch of machine learning dealing with artificial neural networks that are inspired by the structure and function of the brain. It is a sort of machine learning and artificial intelligence (AI) that mimics how people acquire knowledge. Data science encompasses both statistics and predictive modeling, as well as deep learning. A  deep learning engineer is especially well served by deep learning since it speeds up and simplifies the process of gathering, analyzing, and interpreting massive amounts of data. In its simplest form, deep learning can be viewed as a method of automated predictive analytics.

‚Astro Bot‘ for Sony PlayStation 5: Where To Buy Online, Pricing, Game

The small robot must save fellow bots from danger, totaling 300 bots to rescue throughout the game. Reality Blurb was created to bring readers the latest and up to date reality TV news, updates and exclusive interviews. We cover an array of reality shows as well as reality television stars. The Real Housewives of Orange County season 18 airs Thursdays at 9/8c on Bravo.

Lucas, the bot’s creator, charges people £200 (about $256) up front for the right to use the bot, with another £50 subscription fee charged every six months. (Think of it as a sort of Netflix, but purely for buying shoes.) Lucas, however, grants no more than 100 licenses a month, which keeps them a hot commodity. Business logic attacks accounted for 42.6% of attacks on retail sites in the past year, up from 26% the year before. This increase correlates with the growing volume of traffic to retail sites from APIs, which accounted for 45.8% of traffic in the past year, up from 41.6% the year before. Hiding from the clothes websites that you’re using a bot is a bit more complicated; companies will likely ban you if they suspect you’re scraping their website.

These bot shoppers are every sneakerhead’s nightmare

„We are, as humans, biologically built to value stuff that there’s not much of,“ he says. I certainly felt that pull, when I was doggedly trying to snag a PS5 by training my brain to hear the Discord sound. Semiconductor manufacturing stalled as lockdowns kept workers off assembly lines, particularly in Taiwan, the global hub of chip production.

It reveals 47% of respondents believe bots have stopped them getting in-demand goods and services online. Many of those affected (58%) were trying to buy tickets for a live event, but buyers of fashion items (35%), consumer goods (39%) and travel (20%) also suspected high levels of interference. You can foun additiona information about ai customer service and artificial intelligence and NLP. State Reps. Mike McFall and Graham Filler today introduced a bipartisan plan to combat the rampant use of automated bots in online ticket sales.

While we have seen celebrities such as Taylor Swift and Ed Sheeran take a stand against scalper bot activity, legislative change is slow and there is a need for businesses to act now. Netacea’s research discovered that 26% of 18 – 35-year-olds admit they have resorted to using a bot in the last year, with a total of 1 in 6 Americans stating that they have used a bot. Criminals utilize the resources and tools available to make a quick buck. Whether that’s buying and selling stolen goods for a vast markup, laundering dirty money, or hacking accounts for their valuable personal information.

Given how inexpensively and easily they can obtain stolen customer accounts online in marketplaces and private Discord and Telegram communities, they can make enormous profits, he explained. Many companies still rely on ineffective anti-bot defenses that cannot detect automated abuse against their customers’ account login,” he said. Bot attackers can also take over consumer accounts on e-commerce sites and create false accounts to send purchases to their own addresses. Jain is familiar with such practices from his time working at eBay validating user identity and handling risk and trust assessments for that commerce platform. The concept of automating online purchases has not gone away, according to Ashish Jain, CPO/CTO at Arkose Labs. Although automating bulk purchases using bots is not illegal [in certain jurisdictions], some attackers use them to obtain consumers’ credentials to carry out fraudulent purchases.

Fidelity Investments data breach impacts more than 77,000 customers

So observed John Breyault, the vice president of public policy, telecommunications, and fraud at the consumer advocacy-focused National Consumers League, over email. In many cases, bots are built by former sneakerheads and self-taught developers who make a killing from their products. Insider has spoken to three different developers who have created popular sneaker bots in the market, all without formal coding experience. State lawmakers want to crack down on individuals using automated software programs, or bots, to snag concert tickets and drive prices for highly sought-after performers.

Lockdowns also kept people inside, where they needed things like

laptops

and monitors to work, or took time to order

refrigerators

and freezers to stock up on food. According to the new study by researchers at Ohio State University, ChatGPT consumers don’t always want to talk to a real person when they’re shopping online. A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

For example, „data center“proxies make it appear as though the user is accessing the website from a large company or corporation while a „residential proxy“ is traced back to an alternate home address. Whichever type you use, proxies are an important part of setting up a bot. In some cases, like when a website has very strong anti-botting software, it is better not to even use a bot at all. While bots are relatively widespread among the sneaker reselling community, they are not simple to use by any means. Insider spoke to teen reseller Leon Chen who has purchased four bots. He outlined the basics of using bots to grow a reselling business.

• Belugas are a specific colorway of the Yeezy 350 Boost from Adidas, one of the most sought after sneakers today.
• The sale price for a new pair of vintage “Chicago OG” Air Jordan 1s from 1985 went from $3,000 in 2017 to $7,500 in May 2020 to $19,000 in February, according to StockX.
• Built on a vast network of API connections and third-party dependencies, online retailers are increasingly vulnerable to business logic abuse and client-side attacks.
• But, of course, it’s not just T-shirts; it’s keychains, Mophie battery packs, New York City Metro­Cards, ramen noodle bowls, sleeping bags, even 18-inch steel crowbars with „Shit happens“ etched on the handle.

In our first 24 hours we had over 500 checkouts of Switches,” a representative for Phantom told Motherboard in an email. Admins for Scottbot and Swift did not respond to a request for comment. There’s hundreds of people with bots that are running for Switches, Oculus, and Webcams,” one moderator of the community said in the Discord group chat. Adidas America brand director Simon Atkins told me it’s a constant struggle to keep bots out of the company’s systems. New bots are developed constantly and can often be found on dedicated subreddits, Twitter, online sneaker forums, and YouTube. But Alex says his biggest competition are the manufacturers themselves.

Desperate consumers are driven to unofficial secondary marketplaces to buy the desired ticket, clothes or consoles for a sum that vastly exceeds RRP, despite 91% of people fearing that their payment details may be stolen when doing so. So, if you’re having problems landing a PS5 or Xbox Series X, blaming bots is not as conspiratorial ChatGPT App as you might imagine. They are real, they are legion and they are getting consoles before you. Sometimes for real, desperate customers who are fighting fire with fire to get a unit for themselves, but often for resellers looking to make a profit, which is why 3,500 PS5s can land in the lap of a single scalper group.

The people behind the technology

This, in turn, damages the brand reputation and customer loyalty, and contributes to a negative perception of the industry. Customers with negative experiences with online ticket purchasing and ticket scalping are less likely to attend future events, reducing repeat business and long-term loyalty. For instance, one Twitter account called @Table_of_Chefs sells memberships to participate in bot purchases of PS5s and other popular products. These software programs are coded to purchase in-demand products as soon as they go on sale, completing the transaction far faster than is physically possible for a human. Bots have long been prevalent in the sneaker industry, given the high resale value of coveted shoe releases, but programmers have been expanding to target other products and services. That includes tech releases and even grocery delivery slots for services like Instacart — a serious issue given the surge in demand for food deliveries amid the ongoing coronavirus pandemic.

It’s been typical to see bots target big shopping events like Black Friday. Before the pandemic, they were growing in popularity as a result of the retail industry’s increasing reliance on hype and limited stocks. “We are seeing more and more hard sales recently, with limited stock,” says Benjamin Fabre, CTO of DataDome, a cybersecurity company.

Sierra Greenslade was one of the anxious fans watching the ticket debacle unfold on social media while her friend waited in the ticket queue. After several hours of waiting, they managed to get tickets to one of the Arlington shows. „In the past, these bots would be bots for purchasing online in the hands of specific hackers that know their stuff,“ Zioni said. „But now it’s a prolific business to release it or sell it for others to use.“ And — surprise — during the same month, the people at Akamai saw one of the year’s highest rates of bot activity.

The 2020 law — passed after roughly six years of debate — repealed a state ban that was rarely enforced and left the door open to bad actor ticket scalpers who never had a ticket in the first place. At first, he created Bird Bot for him and his friends to purchase Nintendo Switch consoles during the quarantine shortage. But then he put it on GitHub, where software developers upload and share each other’s creations.

Lansing — Michigan lawmakers on Wednesday introduced bills to crack down on the use of automated software programs snarling concert ticket sales and driving up prices for big name performers. When most people think of bots, they think of sophisticated programmers working behind the scenes with a custom bot designed to bypass Ticketmaster’s bot checks. The reality is that Bots as a Service (BaaS) means you can buy a Ticketmaster bot for as little as $50 that allows you reserve multiple tickets and potentially bypassing household limits.

Shoppers beware: ‘Grinch bots’ are a holiday shopping problem

Since July, bad bot attacks on retail sites have increased 14% with most attacks occurring on US-based ecommerce sites, followed by sites in France. The rise of automated attacks are likely to continue through Black Friday and Cyber Monday. Grinch bots could again be involved in the disruption of holiday sales events and limited product launches. The eCommerce industry remains a lucrative target for cybercriminals due to its vast network of API connections and third-party dependencies. Cybercriminals are motivated to compromise user accounts for personal data and payment information and a successful security incident can lead to higher infrastructure and support costs, degraded online services, and customer churn. Although these security risks are persistent throughout the year, attacks often peak during the holiday shopping season when there is greater online traffic.

According to Hansen, scalping is a profitable business that has existed since the 1800s. As more online scalpers transition to using automated tools, the scope of the problem is growing. Scalping bots are cheap, easy to run and customize, and provide a high return on investment for scalpers.

Queuing for ticket sales, online or in person, would seem like a fair way to sell high-demand tickets to fans. „Many retailers are working very hard to allow their loyal customers to buy these limited inventory items as opposed to have bot operators hoover them all up and resell on third-party marketplaces,“ Sullivan of Akamai added. Bird Bot fits into this space, performing many of the same tasks as a sneaker bot, but for Switches from Walmart and Best Buy.

“I tweeted it out, got a bunch of likes and retweets, made a Discord group for it,” Nate said. Ahead of a special release, the New Balance 990v3 to celebrate Bodega’s 15th anniversary, the boutique and Shopify had devised a few obstacles to slow the bots down. The first was to place the product on a brand-new website with an unguessable address — analogwebsitewrittenonpaper.com. “While they have to act like they’re trying to stop bots, it’s making them a huge profit,” he said.

At about $300, AIY Solutions bots are among the most expensive consumer sneaker bots. Comparable bots like NikeSlayer and Better Nike Bot start at $150 and $200, respectively. Half of New Yorkers plan to spend under $500 for their holiday shopping, and 70 percent said they plan to do at least some shopping online, the poll found. There’s no real way to directly prevent the groups from skirting purchasing limits.

That seems to be the thinking of a coalition of U.S. lawmakers who, on Monday, reintroduced proposed legislation seeking to prevent automated bot accounts from dominating online sales. Dubbed the Stopping Grinch Bots Act, the measure aims to prevent what are in effect scalpers for physical goods ahead of the holiday season. When sneakers are released in limited quantities, it’s often a race to see which sneakerheads can input their credit card information on a website or app the fastest in order to checkout before the product sells out. Bots are specifically designed to make this process instantaneous, offering users a leg-up over other buyers looking to complete transactions manually.