Trending December 2023 # Poll: With The Future Of The Touch Bar In Doubt, How About This Compromise? # Suggested January 2024 # Top 20 Popular

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The future of the Touch Bar was thrown into doubt earlier this month thanks to separate reports from Ming-Chi Kuo and Bloomberg’s Mark Gurman. The former said that the touch-sensitive strip was definitely being removed, while the latter said Apple was considering it.

There doesn’t seem to be any real consensus view of the Touch Bar, making it a tough call for Apple, but one Apple patent does describe a possible compromise approach that takes the company some way toward a fully dynamic keyboard …


Apple introduced the Touch Bar as part of a MacBook Pro refresh in 2023. There were a lot of objections to the loss of the physical Escape key – a problem Apple solved in a later update – but in general there didn’t seem to be much agreement about whether or not it was a worthwhile feature.

Some dismissed it as a gimmick, some loved it, but the majority of MacBook Pro owners feel between the two – viewing it as ok but not a must-have.

For my own part, I was somewhat skeptical, did end up kind of liking it – but not really feeling like it justified the likely cost. Two years in, I concluded that I wouldn’t pay a single penny extra for the feature if given the choice. The majority of you, owners and not, agreed with me.

A couple of us here at 9to5Mac gave our own perspectives on the reports. First, I addressed the question of how credible it was.

Some love it, some hate it, but personally – and I suspect I may be in the majority here – I’m pretty indifferent to it. I kind of like the sliding volume and brightness controls, but that’s been the only benefit in my experience […] It doesn’t really matter whether the balance of opinion is against it, or merely neutral. Either way, it makes no sense to keep an expensive feature many don’t care about, some hate and relatively few seem to love. So yes, it’s absolutely plausible to me that this will go.

There has also never been a macOS update that introduces new Touch Bar capabilities as a flagship feature. The interface launched with customization in apps like Safari and Mail, and apps like Logic and Final Cut Pro make good use of the Touch Bar, but Apple has never pushed Touch Bar customization further […]

My issue with the Touch Bar in 2023 isn’t that it’s no longer better than a static row of function keys. It’s that Apple shipped the Touch Bar then never touched it again. The Touch Bar is considered just OK to lackluster four years later because it’s bizarrely still a 1.0 product.

Two potential future keyboard innovations

Apple has long shown interest in two potential future keyboard innovations. The more radical of these is a fully-virtual keyboard, where the ‘keyboard’ is just a screen, and the keys are displayed on it as with the iPad. Typing on one of these isn’t very comfortable, and doesn’t work for touch-typists, but Apple has patents for various ways to make a virtual keyboard feel like the real thing.

The second approach, likely to be adopted at an earlier stage, is a physical keyboard with dynamic functions, as developed by Sonder Keyboard.

With this approach, you have hardware keys, but e-ink displays to change their functionality depending on the app you are using or the mode of that app. Other companies have instead opted for OLED keycaps.

Sonder was acquired by iPhone assembler Foxconn, and Apple reportedly held talks with the company about potential adoption of the tech. An Apple patent granted at the end of last year described the same basic approach.

Each key may have a movable key member and an associated key display. Control circuitry in the keyboard may direct the key displays to display dynamically adjustable key labels for the keys.

Could this be the future of the Touch Bar?

Silver Mac gets a little carried away, and suggests this patent provides evidence that Apple is instead switching from the Touch Bar to a row of dynamic hardware function keys.

Many blogs and tech news sites are speculating that Apple is removing the touch bar on their upcoming MacBook Pro 16″ and possibly on the 13″ and the rumoured 14″ machines. The thing is, Apple is not removing it. Well, they do but they actually don’t.

What I believe Apple is in fact doing is modifying the touch bar so that they will have 12 independent keys, each being a configurable touchbar itself.

9to5Mac readers, of course, know better: Apple patents don’t provide any evidence at all of the company’s plans, only ideas it wishes to explore. The company patents all kinds of things that never make it to market.

But it is an interesting idea, potentially satisfying most people. Those who want physical keys get them; those who want dynamic functionality get that. The only thing it wouldn’t offer is sliding controls, which I rather like for volume and brightness.

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The Future Of Artificial Intelligence In Manufacturing

Industrial Internet of Things (IIoT) systems and applications are improving at a rapid pace. According to Business Insider Intelligence, the IoT market is expected to grow to over $2.4 trillion annually by 2027, with more than 41 billion IoT devices projected.

Providers are working to meet the growing needs of companies and consumers. New technologies, such as Artificial Intelligence (AI), and machine learning make it possible to realize massive gains in process efficiency. 

With the growing use of AI and its integration into IoT solutions, business owners are getting the tools to improve and enhance their manufacturing. The AI systems are being used to: 

Detect defects

Predict failures

Optimize processes

Make devices smarter

Using the correct data, companies will become more creative with their solutions. This sets them apart from the competition and improves their work processes.

Detect Defects

AI integration into manufacturing improves the quality of the products, reducing the probability of errors and defects.

Defect detection factors into the improvement of overall product quality. For instance, the BMW group is employing AI to inspect part images in their production lines, which enables them to detect deviations from the standard in real time. This massively improves their production quality.

Nokia started using an AI-driven video application to inform the operator at the assembly plant about inconsistencies in the production process. This means issues can be corrected in real time. 

Also read: Top 6 Tips to Stay Focused on Your Financial Goals

Predict Failures

Predicting when a production line will need maintenance is also simple with machine learning. This is useful in the sense that, instead of fixing failures when they happen, you get to predict them before they occur.

Using time-series data, machine learning models enhance the maintenance prediction system to analyze patterns likely to cause failure. Predictive maintenance is accurate using regression, classification, and anomaly detection models. It optimizes performance before failure can happen in manufacturing systems.

General Motors uses AI predictive maintenance systems across its production sites globally. Analyzing images from cameras mounted on assembly robots, these systems are identifying the problems before they can result in unplanned outages.

High speed rail lines by Thales are being maintained by machine learning that predicts when the rail system needs maintenance checks.

Optimize Processes

The growth of IIoT allows for automation of most production processes by optimizing energy consumption and predictions for the production line. The supply chain is also improving with deep learning models, ensuring that companies can deal with greater volumes of data. It makes the supply chain management system cognitive, and helps in defining optimal solutions. 

Make Devices Smarter

By employing machine learning algorithms to process the data generated by hardware devices at the local level, there is no longer a need to connect to the internet to process data or make real-time decisions. Edge AI does away with the limitation of networks.

The information doesn’t have to be uploaded to the cloud for the machine learning models to work on it. Instead, the data is processed locally and used within the system. It also works for the improvement of the algorithms and systems used to process information.

Also read: The 15 Best E-Commerce Marketing Tools

What’s Next?

The manufacturing market is seeing a huge boost thanks to the IIoT and AI progress. Machine learning models are being used to optimize work processes. 

The quality of products is getting improved by reducing the number of defects that are likely to occur. This is expected to improve over time, and it also will heavily improve the production process to reduce errors and defects in products.

There is still a huge potential of AI that has yet to be utilized. Generative Adversarial Networks (GAN) can be used for product design, choosing the best combination of parameters for a future product and putting it into production.

The workflow becomes cheaper and more manageable. Companies realize this benefit in the form of a faster time to market. New product cycles also ensure that the company stays relevant in terms of production.

Networks are set to upgrade to 5G, which will witness greater capacities and provide an avenue for artificial intelligence to utilize this resource better. It will also be a connection for the industrial internet of things and see a boost in production processes. Connected self-aware systems will also be useful for the manufacturing systems of the future.

Analyzing How Bitcoin May Shape The Future According To This Expert

The performance of the King coin hasn’t been in the best of spirits lately, especially given its position of extreme volatility in the last few weeks. However, despite the constant state of war that Bitcoin (BTC) has been in, Dan Morehead, CEO of Pantera Capital, felt otherwise.

In a recent episode of the Bankless podcast, he stated,

“I think we’re done with the bear market. The next six to 12 months are likely to see massive rallies investors flee stocks, bonds, and real estate for blockchain.”

Is the bear cycle becoming weak?

At the time of writing, BTC was trading at a value of $38,015 as per data from CoinGecko. The token was -1.6% down in the last 24 hours and was approximately lower by -3.9% in the last seven days. At press time, the Relative Strength Index (RSI) was fluctuating below neutral 50 at a score of 39.19. The Awesome Oscillator (AO) further substantiated the bearish movement of the token at press time.

According to the data chart given below, the “Net Transfer Volume to/from Exchanges” stands at -3,012.95 BTC at the time of writing. The negative volume indicates that token investors are willing to hold onto their investments and not get pressurized by the bear cycle just yet.

The future is “Bull”

Commenting on the performance of BTC and the overall cryptocurrency market, Dan Morehead expressed his astonishment at the ongoing state of all the cryptocurrencies. He also addressed the reasons for the ongoing bear market and the correlation between macro news and the cryptocurrency market.

“Bear markets are half as long as bull markets. With the Russian invasion of Ukraine and all of the policy responses, it’s hard to know how everything is going to play out but when the dust settles, it’s going to make a lot of people use crypto”, he stated.

Amid the ongoing bear run of the king token, Willy Woo, a BTC analyst, also shared a tweet supporting the bullish outlook of the market.

BTC price holding up well while equities tank and USD Index moons is testament to the unprecedented spot buying happening right now.

In other words: Investors already see BTC as a safehaven, it will take time for price to reflect. Wait for the futures sells to run out of ammo.

— Willy Woo (@woonomic) April 30, 2023

Is BTC the future then? Most likely not…

The Berkshire Hathaway Annual Shareholder meeting took place on 30 April 2023, where Warren Buffet, yet again, expressed his views on how cryptocurrencies are of no value to him. Commenting on the volatility of the current market, he stated,

“Whether it goes up or down in the next year, or five or 10 years, I don’t know. But the one thing I’m pretty sure of is that it doesn’t produce anything.”

Holding a $20 bill in his hand, he also stated,

“Assets, to have value, have to deliver something to somebody. We can put up Berkshire coins… but in the end, this is money. And there’s no reason in the world why the United States government… is going to let Berkshire money replace theirs.”

The Best Of Ces 2012: Popsci’s Products Of The Future

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It’s often tough to find a clean signal in all the noise of CES, but in putting this list together, we realized how excited we are about a lot of the new gear we saw this year. CES 2012 brought us the best TV we’ve ever seen, two killer new cameras, a fridge that can chill beer cans in five minutes, high-speed in-car mobile wireless, awesome new smartphones, and a lot more. Check out our picks for the Products of the Future in the gallery below.

Nokia Lumia 900

Modular Robotics Cubelets

The simplest way to wrap your head around what exactly the Cubelets are is to think of them as robotic Legos. Sold as a six-block starter kit, the Cubelets are pre-programmed 1.5-inch blocks, each with its own action—to move, sense nearby objects, display light, whathaveyou—and the way you stack them determines what your final robot will do. Snapping a battery block on top of a motion-sensing block and a roller block, for instance, will great a ‘bot that automatically moves when the lights go on (or off). Modular Robotics sells add-on blocks with other traits (sound sensitivity, loudspeakers, etc.) and will add a Bluetooth block this year, allowing users to re-program their bricks over a simple Web interface.

Samsung Super OLED TV

OLED televisions are super thin, ultra-contrasty, and have colors that are saturated to the point of near surreality. But other than Sony’s dimunitive XEL-1, which has been on sale since 2008, they’re usually trotted out at CES only as dreamy concept showpieces. Samsung’s 55″ Super OLED, though, marks an important moment in TVs: large-panel OLEDs are now practical to manufacture and sell. No word on price (it will be exorbitant, surely), but Samsung stated plainly that the set would be available for purchase this year. And good thing: after you’ve stood in front of this OLED beauty, it’s hard to look at normal TVs the same way again.

Basis Band

LG’s French Door Refrigerator With Blast Chiller

This fridge wants us to never deal with warm beer ever again, and for that we are eternally thankful. Its special blast chiller compartment can take your beverage of choice from room temperature to a delicious 42º F in no time—5 minutes for a single can, or 8 minutes for two cans or a bottle of wine. A gentle rocking motion exposes the liquids to the cold evenly, without leaving you with a fizzy carbonation bomb upon opening. See a video of the chiller in action here.

MakerBot Replicator

The 3-D printer you build yourself is new and improved for 2012, now sporting a larger building area and twin extruders for printing with two colors (or entirely different materials) simultaneously. Your very own personal assembly line for under $2,000.

MicroVision PicoP Gen2 HD Laser Projector

If there’s one thing we love at PopSci, it’s a pico projector. And, well, we’ve seen a lot of them over the last three years. But MicroVision’s new PicoP Gen2 is sorta the one we’ve been looking for. Not only is it the first pico to display 720p HD video, it’s also the first one we’ve seen that’s small and efficient enough to be built into more things that just standalone projectors–we’re talking projector phones that aren’t bricks and even portable gaming systems that can be standalone mobile entertainment centers.

Fujifilm X-Pro1

Parrot ZIK Headphones

There are always tons of headphones at CES. So why’d we pick out these wireless ones from Parrot, a company known for fun flying drones rather than audio equipment? A few reasons: Designed by Philippe Starck, a well-known industrial designer, the headphones look awesome, all black leather and curved silver metal. The way you use them is really, really cool: they have controls built in, but not in any boring way like an inline remote or (and here we utter a bad-design shudder) some play/pause/forward/back buttons on the outside of the ear cups. Instead, it uses a proximity sensor to figure out when you’re wearing and when you’ve taken the headphones off, and it pauses automatically when you remove them. To change the volume, you gently stroke the ear cups up and down, and to go to the next or previous track, you stroke left and right. (You can see in this picture that our own John Mahoney got pretty into the stroking part of this.) The ZIK has a bunch of other features too: it’s got self-contained noise cancellation (Parrot says the batteries last about five hours with the battery-draining cancellation turned on), Bluetooth to connect, and even NFC, which to my knowledge has never been implanted into a pair of headphones before. And they’re super comfortable. Audio nerds: we only tested them in the midst of a raucous western-themed press event, so we can’t vouch for audio quality in any respectable way. They sounded pretty good but we can’t comfortably say much more than that. They’ll be available sometime this year for an undisclosed (but undoubtedly steep) price.

Sennheiser RS220

Thanks to a new wireless streaming standard, the RS220 home-theater headphones may well be the best-sounding wireless pair you can get. The pair transmit uncompressed audio over the 2.4GHz range—yes, the same as Kleer and other high-end transmission standards—but this new DSSS trick modulates the signal across several clustered frequencies, and the headphones recompose the signal at the other end. The result: better dynamic range and super low-latency, which might not matter as much when listening to music, but makes a world of difference when you need to sync with a TV screen.

Lenovo IdeaPad Yoga

It’s become clear this week that 2012 is the Year of the UltraBook, but in reality there’s not a lot to distinguish one super-trim laptop from another. Unless we’re talking about the IdeaPad Yoga, which in this case we happen to be. The Windows-running clamshell can morph into any of four form factors. From standard laptop clamshell, rotate the keyboard behind the screen (like the cover of a spiral notebook) to enter tablet-style mode with the keyboard deactivated. Or, use the keyboard as a stand in either a sandwich-board-style orientation or a right-angle hinge.

Mobile High-Definition Link

Mobile High-Definition Link, or MHL, wasn’t announced at CES 2012, but it was during CES that we at PopSci really started to get excited about it. It’s a new kind of technology that can be applied to just about any connector, like HDMI, USB, or any kind of proprietary port (including Apple’s iPod/iPhone/iPad port), and it essentially gives those dumb old connectors a whole bunch of new powers. Some of those, like HDMI, for example, can’t deliver power. But an MHL-supporting HDMI port sure can. That’s how we can get things like the Roku Streaming Stick, which is an entire Roku the size of a USB thumb drive that plugs right into your TV’s HDMI port. Other cool features include the ability to control anything plugged into an MHL-enabled HDMI port with your TV’s remote–no need to have several remotes scattered around anymore.

Able Planet Personal Sound Amp PS2500AMP

Unlike the majority of personal hearing aids, Able’s Planet’s Personal Sound Amp tucks almost entirely inside the ear canal—nearly invisibly so. Like a noise-canceling headphone pair, the Amp senses what noise it’s up against—wind, music, the din of a loud room—and automatically tunes itself to cancel out those noises. FI the wearer is still having trouble hearing (ie: if the earbud has yet to re-tune to the room), he can cup his hand over his ear; the change in pressure from that action tells the Amp to re-tune itself.

OnStar & Verizon Wireless Chevy Volt with LTE

The promise of Verizon’s 4G LTE network has long—well, since 2010—been the ability to stream audio and video consistently from anywhere, even if you’re moving. Until now there are have been demos involving telepresence robots and LTE-equipped broadcast cameras, but the new OnStar shows LTE the way a real person would use it: in a car. LTE connectivity allows the system to constantly connect to road-trip-friendly cloud services like Skype and Pandora. Netflix? Maybe not the best idea.

Canon PowerShot G1 X

Continuing the theme of exceptional image quality in ever-smaller packages, the PowerShot G1 X is an entirely new beast for Canon—a camera system within itself. Forgoing a buy-in to an interchangeable lens system to keep costs down, the G1 X offers a fixed 4x zoom feeding light into a brand new CMOS sensor that’s just a hair smaller than those found in most DSLRs.

Corning Gorilla Glass 2

If you have a modern smartphone, chances are you have some Gorilla Glass in your pocket. At CES, Corning announced a new formula for their chemically-strengthened glass that’s 20 percent tougher, which means tablet touchscreens and notebook LCDs can go 20 percent thinner without sacrificing strength and durability. The profusion of slim, MacBook-Air-like ultrabooks this year is no coincidence; Gorilla Glass is one of the enabling technologies pushing our gadgets ever-sleeker.

The Future Of Machine Learning: Automl

Do you ever wonder how companies develop and train machine learning models without experts? Well, the secret is in the field of Automated Machine Learning (AutoML). AutoML simplifies the process of building and tuning machine learning models for organizations to harness the power of these technologies. Figure 1 gives a visual AutoML. In this blog, we’ll explore a look at some of its key benefits and limitations. Get ready to be amazed by the power of AutoML.

Learning Objectives

Understand the basics of AutoML and its methods

Explore the key benefits of using AutoML

Understand the limitations of AutoML

Understand the practical impact of AutoML

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is AutoML?

Methods of AutoML: A Comprehensive Overview

Effortless ML: The Merits of AutoML

AutoML: A Closer Look at the Drawbacks

AutoML in Practice: How Companies are Automating Machine Learning?


What is AutoML? The Future of Machine Learning

AutoML is a game-changer in the field of machine learning. It is a training of machine learning models to automate the process of selecting and tuning algorithms. This includes everything from data preprocessing to selecting the most suitable model for the given task. AutoML tools handle hyperparameter tuning and model selection tasks, which typically require time and expertise. With AutoML, users without experience in machine learning can train high-performing models with minimal effort. Whether you’re a small business owner, a researcher, or a data scientist, AutoML helps to achieve your goals with less time and effort. Examples of popular AutoML platforms include Google Cloud AutoML, chúng tôi and DataRobot.

AutoML provides explainable AI to improve the interpretability of the model. This allows data scientists to understand how the model makes predictions, which is particularly helpful in healthcare, finance, and autonomous systems. This can be used to identify bias in data and prevent wrong predictions. For example, AutoML can be used in healthcare fo gnosis by analyzing medical images, in finance for fraud detection, in retail for product recommendations, and in transportation for self-driving cars. Figure 2 shows the AutoML process.

ethods: A Comprehensive Overview

AutoML automates the use of machine learning for real-world problems. This includes tasks such as algorithm selection, hyperparameter optimization, and f rent methods are being developed to tackle the various aspects of the problem. Some popular approaches are given below

Neural Architecture Search (NAS):

This method uses a search algorithm to automatically find the best neural network architecture for a given task and dataset.

Bayesian Optimization: This method uses a probabilistic model to guide the search for the best set of hyperparameters for a given model and dataset.

Evolutionary Algorithms: This method uses evolutionary algorithms such as genetic algorithms or particle swarm optimization to search for the best set of model hyperparameters.

Gradient-based methods: This method uses gradient-based optimization techniques like gradient descent, Adam, etc., to optimize the model hyperparameters.

Transfer Learning: This method uses a pre-trained model on a similar task or dataset as a starting point and then fine-tunes it on the target task and dataset.

Ensemble methods: This method combines multiple models to create a more robust and accurate final model.

Multi-modal methods: This method uses multiple data modalities such as image, text, and audio to train models and improve performance.

Meta-learning: This method uses a model to learn how to learn from data, which can improve the efficiency of the model selection process.

One-shot or few-shot learning: This method can learn to recognize new classes from only one or a few examples.

AutoML is broadly classified into a model selection and hyperparameter tuning, as shown in Fig 3. Many differen integrated into existing workflows.

Effortless Machine Learning: The Merits of AutoML in Machine Learning

AutoML simplifies the machine learning process and brings many benefits, some of which are given below:

Time-saving: Automating the process of model selection and hyperparameter tuning can save a significant amount of time for data scientists and machine learning engineers.

Accessibility: AutoML allows users with little or no experience with machine learning to train high-performing models.

Improved performance: AutoML methods can often find better model architectures and hyperparameter settings than manual methods, resulting in improved model performance.

Handling large amounts of data: AutoML can handle large amounts of data and find the best model even with more features.

Scalability: AutoML can scale to large datasets and complex models, making it well-suited to big data and high-performance computing environments.

Versatility: AutoML can be used in various industries and applications, including healthcare, finance, retail, and transportation.

Cost-effective: AutoML can save resources and money in the long run by reducing the need for manual labor and expertise.

Reduced risk of human error: Automating the model selection and hyperparameter tuning process can reduce the risk of human error and improve the reproducibility of results.

Increased Efficiency: AutoML can be integrated with other tools and processes to increase efficiency in the data pipeline.

Handling multiple data modalities: AutoML can handle multiple data modalities such as image, text, and audio to train models and improve performance.

AutoML offers several benefits for data scientists and engineers that save time and resources by automating tedious and time-consuming tasks. This also improves the interpretability of the model by providing explainable AI. These combined benefits make AutoML a valuable tool in many industries and applications.

AutoML: A the Drawbacks

AutoML has become a popular tool for data scientists and analysts. However, it has limitations. There are following limitations are given below

Limited control over the model selection and hyperparameter tuning process: AutoML methods operate based on predefined algorithms and settings, and users may have limited control over the final model.

Limited interpretability of the resulting model: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions.

Higher costs than manually designing and training a model: AutoML tools and infrastructure can be costly to implement and maintain.

Difficulty in incorporating domain-specific knowledge into the model: AutoML relies on data and pre-defined algorithms, which can be less effective when incorporating domain-specific knowledge.

Potential for poor performance on edge cases or unusual data distributions: AutoML methods may not perform well on data that is significantly different from the training data.

Limited support for certain models or tasks: AutoML methods may not be well-suited to all models or tasks.

Dependence on large amounts of labeled data: AutoML methods typically require large amounts of labeled data to train models effectively.

Limited ability to handle data with missing values or errors: AutoML methods may not perform well on data with missing values or errors.

Limited ability to explain the model’s predictions and decisions: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions, which can be an issue for certain applications and industries.

Overfitting: AutoML methods may lead to overfitting on the training data if not properly monitored, which can result in poor performance on new unseen data.

AutoML is a powerful tool for automating the machine-learning process, but it is with its limitations. It is important to consider these limitations in the presence of expert supervision to validate the results.

AutoML in Practice: How Companies are Automating Machine Learning?

A few practical examples of AutoML are given below:

Google’s AutoML Vision allows users to train custom machine-learning models for image recognition using th mage datasets’s AutoML enables data scientists and analysts to automatically train and optimize machine learning models without having to write code

DataRobot provides an AutoML platform that can automatically build, evaluate and deploy machine learning models for a wide range of use cases, including fraud detection, customer churn prediction, and predictive maintenance

Amazon SageMaker is a fully managed service that enables data scientists and developers to quickly and , train, and deploy machine learning models at scale

IBM Watson AutoAI is a platform that automates the process of building, training, and deploying machine learning models and provides interpretability and explainability features that help users understand the models’ decision-making processes

Microsoft Azure ML is a cloud-based platform that provides a wide range of tools and services for building, deploying, and managing machine learning models, including AutoML capabilities.

These are a few examples of how companies leverage AutoML in different industries to automate model building and hyperparameter tuning, allowing data scientists to focus on model selection and evaluation.


AutoML automates the process of building and tuning machine-learning models. This method uses algorithms to search the best model and hyperparameters rather than relying on human expertise. AutoML includes increased efficiency and the ability to handle large amounts of data. It can be useful in the shortage of experienced machine learning practitioners. However, there are also limitations to AutoML. It can be computationally expensive and difficult to interpret the results of the automated search process. Additionally, the practical use of AutoML is limited by the data’s quality and computational resources’ availability. In practice, AutoML is mainly used in an indus prove productivity and model performance in scenarios like image, speech, text, and other forms of data.

Key Takeaways:

Simplify the process of building and training models.

AutoML suffers limitations such as a lack of control over the model selection process, huge data requirements, computationally expensive, and overfitting issues.

Expert supervision is important to validate the results of AutoML to counter available limitations.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


Find The Real Breakfast Of Champions With This Helpful Chart

Breakfast isn’t the most important meal of the day, no matter what your pops said. Eat it or don’t—it’s probably fine either way. Unfortunately, though, a lot of what Americans do eat for breakfast isn’t exactly getting them off to a great start.

Cereal commercials have been telling us for decades that they’re part of a healthy breakfast, and a blueberry muffin staring at you from the bakery case can seem like a smart choice compared to the tempting chocolate croissant beside it—but the truth is, neither of those options is good for you. And sure, we all know a donut isn’t healthy, per se, but what about granola? Or a bagel?

Lots of people are unsure, so we decided to demystify it for you. Here’s what our spectrum of breakfast foods looks like:

Your choices in the cereal aisle really matter. Infographic by Sara Chodosh

Each of those dots represents one food item, and its position on the scale shows its score according to the Nutrient Profiling Model. That’s the method that Oxford University researchers came up with so that the Food Standards Agency could evaluate “healthiness,” which is otherwise a fairly ambiguous term.

To calculate that score, you need to know the food item’s weight and caloric punch, plus how much sugar, salt, saturated fat, fiber, protein, and fruit or veggie content it has inside. That limited what types of things we could put on this spectrum. Pancakes, for instance, are made according to recipes that can vary widely, and an individual pancake will have a varying weight. Instead, we stuck to foods you can buy pre-made (plus eggs, which the USDA has nutritional data for), since manufacturers have to provide that information on the label along with serving size.

Some of these foods are things you’d grab on the go, like Starbucks oatmeal or a Dunkin Donuts old fashioned glazed, but others are store bought like yogurt and granola. It’s also worth noting that unless the food itself came with topping (instant oatmeal, which often includes things like nuts and fruits inside, is a good example), we didn’t include any. That means when we say “eggs” we mean plain eggs, and “bagels” don’t count all the saturated fat inside cream cheese.

For the most part, a lot of breakfast foods counted as at least marginally healthy. Even sugar-laden yogurts tended to be low in saturated fat, and though white bread isn’t as fiber-dense as whole grain, most toast has a place in a healthy and balanced diet. Donuts aren’t healthy, but no one ever really thought they were. Muffins’ position well to the left of the healthiness border might surprise people—the truth is, their saturated fat and sugar content means they’re basically breakfast cupcakes.

You’ll notice that while we selected several different branded products to chart, we don’t call them out by name. For most of the kinds of foods we examined, it doesn’t make much of a difference—donuts are always going to be dubious breakfast choices, and smoothies and yogurts are all going to be relatively benign. There are, of course, exceptions. But if you’re chowing down on a protein-packed “donut” baked according to your favorite health blog’s specifications or slurping a smoothie made with ice cream instead of skim milk, well, you probably know where you stand.

But for some foods, more arbitrary choices can make a huge difference.

Oatmeal can get unhealthy pretty quickly. Infographic by Sara Chodosh

Some cereals are just as bad as donuts in terms of nutritional content, but others are healthier than eggs. The same could be said for granola—the fiber content mostly balances out all the sugar, but you’ll want to look carefully at each nutrition panel to make sure you’re getting something that isn’t basically a dessert. Oatmeal also quickly becomes less healthy in instant form, mostly due to tons of added sugar and sodium (more processed foods generally need more salt to taste right).

Sugar sneaks into much of American cuisine, and that’s doubly true for breakfast. And hey—sometimes you’re gonna have a donut at 8 a.m. That’s okay! Cinnamon Toast Crunch is the ultimate breakfast fantasy, and a glazed cruller cannot be beat. A morning sugar overload once in a while won’t kill you. But when it comes to the stuff you eat every day, take a minute in the supermarket aisle to consider what you’re putting in your body. You may not even realize how unhealthy it really is.

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