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Apple iPhone 16 Pro vs Samsung Galaxy S24

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Apple iPhone 16 Pro vs Samsung Galaxy S24

This time around we’re comparing the the smallest flagships from the two largest smartphone manufacturers in the world. This is the Apple iPhone 16 Pro vs Samsung Galaxy S24 comparison. Granted, the iPhone 16 Pro is not the base model in the iPhone 16 series, but it is the smallest flagship in the series aka the smallest ‘Pro’ iPhone 16 model. So, this comparison does make sense, as the Galaxy S24 is by far the smallest smartphone in the Galaxy S24 family.

With that being said, the iPhone 16 vs Galaxy S24 comparison is also on the way. The iPhone 16 Pro is notably more expensive than the Galaxy S24, so keep that in mind. We will first list the specifications of these two smartphones, and will then move to compare them across a number of different sections. We’ll compare the designs of the two phones, their displays, performance, battery, cameras, and audio output. Let’s get down to it.

Specs

Apple iPhone 16 Pro vs Samsung Galaxy S24, respectively

Screen size:
6.3-inch LTPO Super Retina XDR OLED ( flat, 120Hz, HDR, 2,000 nits max brightness)
6.2-inch Dynamic AMOLED 2X (flat, 120Hz, 2,600 nits max brightness)
Display resolution:
2622 x 1206
2340 x 1080
SoC:
Apple A18 Pro (3nm)
Qualcomm Snapdragon 8 Gen 3/Samsung Exynos 2400
RAM:
8GB
8GB (LPDDR5X)
Storage:
128GB/256GB/512GB/1TB (NVMe)
128GB (UFS 3.1)/256GB/512GB (UFS 4.0)
Rear cameras:
48MP (wide, f/1.8 aperture, 1/1.28-inch sensor, 1.22um pixel size, sensor-shift OIS), 48MP (ultrawide, f/2.2 aperture, 0.7um pixel size, PDAF), 12MP (periscope telephoto, f/2.8 aperture, 1/3.06-inch sensor, 1.12um pixel size, 3D sensor-shift OIS, 5x optical zoom)
50MP (wide, f/1.8 aperture, OIS, Dual Pixel PDAF), 12MP (ultrawide, 120-degree FoV, f/2.2 aperture, 1.4um pixel size), 10MP (telephoto, f/2.4 aperture, OIS, PDAF, 3x optical zoom)
Front cameras:
12MP (f/1.9 aperture, PDAF, 1/3.6-inch sensor size, OIS)
12MP (wide, f/2.2 aperture, Dual Pixel PDAF, 22mm lens)
Battery:
3,582mAh
4,000mAh
Charging:
38W wired, 25W MagSafe wireless, 15W Qi2 wireless, 7.5W Qi wireless, 5W reverse wired
25W wired, 15W wireless, 4.5W reverse wireless (charger not included)
Dimensions:
149.6 x 71.5 x 8.3 mm
147 x 70.6 x 7.6mm
Weight:
199 grams
167/168 grams
Connectivity:
5G, LTE, NFC, Wi-Fi, USB Type-C, Bluetooth 5.3
Security:
Face ID (3D facial scanning)
Ultrasonic in-display fingerprint scanner
OS:
iOS 18
Android 14 with One UI 6.1
Price:
$999+
$799.99+
Buy:
Apple iPhone 16 Pro
Samsung Galaxy S24 (Best Buy)

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Apple iPhone 16 Pro vs Samsung Galaxy S24: Design

The iPhone 16 Pro is made out of titanium and glass. On the flip side, the Galaxy S24 utilizes aluminum and glass. Both smartphones have flat sides all around, which are curved towards the very edges. They both include flat front and back sides too, and have a similar curvature on the edges. Well, the iPhone 16 Pro is curved more in that area, but neither phone is close to having sharp edges.

Apple’s handset has a pill-shaped cutout at the top of the display, the so-called Dynamic Island. Samsung’s device has a small display camera hole up there. Both devices do have very thin bezels around the display, which are also uniform. On the right-hand side of the iPhone 16 Pro you’ll find a power/lock key and the Camera Control button. On the left, the volume up and down buttons are located, along with an Action Button. The Galaxy S24, on the other hand, has the power/lock key on the right, along with the volume up and down buttons, and that’s it.

Both smartphones have three cameras on the back, but those setups look considerably different. The iPhone 16 Pro has its recognizable camera island in the top-left corner. The Galaxy S24’s cameras protrude directly from the backplate and are vertically-aligned in the top-left corner. The iPhone 16 Pro does have a slightly bigger display, and it’s taller and wider than the Galaxy S24, while also being thicker and heavier. It’s over 30 grams heavier. Both smartphones offer an IP68 certification for water and dust resistance. They’re both quite slippery too, but very comfortable to hold.

Apple iPhone 16 Pro vs Samsung Galaxy S24: Display

The iPhone 16 Pro features a 6.3-inch 2622 x 1206 LTPO Super Retina XDR OLED display. That panel is flat, and it has a 120Hz refresh rate. HDR10 content is supported, as is Dolby Vision. The maximum brightness here is set at 2,000 nits. The screen-to-body ratio is at around 90%, while the display aspect ratio is 19.5:9. The Ceramic Shield glass is placed on top of this phone’s display.

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Samsung Galaxy S24 series AM AH 044

The Samsung Galaxy S24, on the flip side, has a 6.2-inch 2340 x 1080 Dynamic LTPO AMOLED 2X display. This display has a 120Hz refresh rate and supports HDR10+ content. It also offers a 2,600 nits peak brightness. The screen-to-body ratio is at around 90%, while the display aspect ratio is 19.5:9. The Gorilla Glass Victus 2 from Corning is protecting this phone’s display.

Both of these panels are really good. They’re quite vivid and more than sharp enough. They also have very good viewing angles, and the touch response is very good. These displays do not have a high-frequency PWM dimming, though, so keep that in mind. The blacks are deep on both, and both have a high refresh rate. The Galaxy S24 can technically get brighter, but in practice, the difference is not that big at all. They’re both bright enough.

Apple iPhone 16 Pro vs Samsung Galaxy S24: Performance

The Apple A18 Pro is a 3nm processor which fuels the iPhone 16 Pro. That is Apple’s most powerful chip. The company also included 8GB of RAM here, along with NVMe flash storage. The Galaxy S24 is fueled by the Snapdragon 8 Gen 3 (4nm) or Exynos 2400 (4nm) chips, depending on the market. We used the Snapdragon 8 Gen 3 model. Samsung also included 8GB of LPDDR5X RAM inside the phone, along with UFS 3.1 or UFS 4.0 flash storage. UFS 3.1 flash storage is included in the 128GB storage option only.

Having said that, both smartphones do offer really good performance. In regular, day-to-day tasks, they both perform great. They’re snappy whatever you’re doing, and the high refresh rate helps keep things looking really nice while you’re scrolling around. Getting either phone to slow down is not that easy. They can jump between apps without a problem and are great for browsing, messaging, emailing, multimedia consumption, image editing, video processing, and so on.

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The iPhone 16 Pro technically has more prowess on the gaming side of things. It has a more powerful chip and GPU, but the Galaxy S24 keeps up in terms of performance. No matter what game you throw at these two phones, they’ll do a great job. They will get warm after a while, but neither phone will get visibly affected by that, at all. Neither phone becomes to hot to hold either.

Apple iPhone 16 Pro vs Samsung Galaxy S24: Battery

The iPhone 16 Pro battery capacity has finally been revealed, the phone includes a 3,582mAh battery, so a 9.4% larger battery pack than its predecessor. The Galaxy S24 includes a 4,000mAh battery pack. Apple’s iPhones usually have smaller battery packs than their Android counterparts. In this case the difference is not that big, and the iPhone 16 Pro does offer better battery life in comparison… it’s not even close.

The Galaxy S24 can even struggle to get to the 6-hour screen-on-time mark, it tends to be closer to 5-5.5 hours. The iPhone 16 Pro can go above and beyond that. The iPhone 15 Pro offered really good battery life, and the iPhone 16 Pro flies above that. Getting to the 7-hour screen-on-time mark on this phone does seem doable, but it will depend on a number of factors, of course. Your mileage may vary.

When it comes to charging, the iPhone 16 Pro supports 38W wired, 25W MagSafe wireless, 15W Qi2 wireless, 7.5W Qi wireless, and 5W reverse wired charging. The Galaxy S24 supports 25W wired, 15W wireless, and 4.5W reverse wireless charging. Do note that neither of these two smartphones ships with a charger in the retail box. You’ll have to buy one separately if you don’t already own it.

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Apple iPhone 16 Pro vs Samsung Galaxy S24: Cameras

You’ll find three cameras on the back of both of these phones. The iPhone 16 Pro has a 48-megapixel main camera (1/1.28-inch camera sensor), a 48-megapixel ultrawide unit, and a 12-megapixel periscope telephoto camera (5x optical zoom). The Galaxy S24 includes a 50-megapixel main camera (1/1.56-inch camera sensor), a 12-megapixel ultrawide unit (120-degree FoV), and a 10-megapixel telephoto unit (3x optical zoom).

Samsung Galaxy S24 series AM AH 040(1)

Both of these phones do a good job in the camera department, but the iPhone 16 Pro pulls ahead. It has a more capable main camera, and that shows in the final product. Both phones tend to provide images with warmer tones, but the ones from the iPhone 16 Pro have a better balance overall. The Galaxy S24 can overdo it with sharpening and saturation at times, the photos also don’t look as well-rounded. The iPhone 16 Pro does tend to brighten up the darker portions of images in HDR situations a bit too much, which makes the images look flatter than it should. They both do a very good job in low light, but once again, the iPhone 16 Pro is better most of the time.

The iPhone 16 Pro has a telephoto camera that offers more versatility in comparison, and the shots from it mostly look a bit better. Its ultrawide camera also tends to provide more detail than Samsung’s, but both do a good job of keeping the color profile similar to what their main shooters provide.

Audio

Stereo speakers are included on both smartphones, and they both offer good performance. The sound output is well-balanced, and not too sharp or anything. They’re both loud enough and similar in that regard.

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There is no audio jack on either one of these two smartphones, though. You’ll need to use their Type-C ports if you want to hook up your wired headphones. Alternatively, Bluetooth 5.3 is on offer for wireless connectivity.

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Meta Quest 3S will be affordable, reveals price leak

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Meta Quest 3S will be affordable, reveals price leak

Meta has been working on a VR headset for the better part of the year. The brand’s upcoming VR headset is expected to be called the Meta Quest 3S. The official name of the device was recently confirmed by an official store. Now, the price of the Meta Quest 3S has leaked ahead of the launch. The leaked pricing suggests that Meta’s next VR offering will be a highly affordable one.

The leaked price suggests that the Meta Quest 3S will be an affordable offering.

The price of the Meta Quest 3S VR headset has appeared in an Amazon advert on the streaming platform Peacock. One Reddit user of the OTT platform saw the advertisement and managed to record it. He then shared the ad clip carrying Meta Quest 3S’ price on Reddit. The Meta Quest 3S VR headset will cost just $299. It will be one of the most affordable VR headsets on offer. Notably, the rumor mill had also suggested a similar pricing earlier.

The $299 Quest 3S variant will have 128GB of storage

The advert on Peacock has leaked the pricing of the 128GB storage variant of the VR headset. This will put it at the same price as the 64GB Oculus Quest 2 from 2020. The company will likely offer other variants of the VR headset with varying storage as well. Notably, the advertisement corroborates recent images of the device that leaked online. It shows full-color passthrough tech at a lower resolution than the Quest 3.

Meta Quest 3S will offer the Snapdragon XR2 Gen 2 SoC, just like the Quest 3

A Snapdragon XR2 Gen 2 chip will power the Meta Quest 3S.. The same chipset powers the Quest 3. The device will have the old Fresnel lenses from the Quest 2 to achieve the $299 affordable price tag. It could also have downwards-facing side cameras in the same positions as the Quest 3 model.

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Furthermore, the reports indicate that the Meta Quest 3S will offer a slightly lower battery capacity than the Quest 3. It will also miss a headset jack. The brand is expected to unveil the new VR headset at its Connect event on September 25.

Meta Quest 3s price leaked

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News of The Week: eBay’s New Background Enhancing AI Tool

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News of The Week: eBay’s New Background Enhancing AI Tool

Discover how eBay’s new background enhancement tool can help sellers create stunning, professional-grade photos effortlessly.

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GenAI demands greater emphasis on data quality

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GenAI demands greater emphasis on data quality

Data quality has perhaps never been more important. And a year from now, then a year beyond that, it will likely be even more important than it is now.

The reason: AI, and in particular, generative AI.

Given its potential benefits, including exponentially increased efficiency and more widespread use of data to inform decisions, enterprise interest in generative AI is exploding. But for enterprises to benefit from generative AI, the data used to inform models and applications needs to be high-quality. The data must be accurate for the generative AI outputs to be accurate.

Meanwhile, generative AI models and applications require massive amounts of data to understand how to respond to a user’s query. Their outputs aren’t based on individual data points, but instead on aggregations of data. So, even if the data used to train a model or application is high-quality, if there’s not enough of it, the model or application will be prone to deliver an incorrect output called an AI hallucination.

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With so much data needed to reduce the likelihood of hallucinations, data pipelines need to be automated. Therefore, with data pipelines automated and humans unable to monitor every data point or data set at every step of the pipeline, it’s imperative that the data be high-quality from the start and there be checks on outputs at the end, according to David Menninger, an analyst at ISG’s Ventana Research.

Otherwise, not only inaccuracies, but also biased and potentially offensive outputs could result.

As we’re deploying more and more generative AI, if you’re not paying attention to data quality, you run the risks of toxicity, of bias. You’ve got to curate your data before training the models, and you have to do some postprocessing to ensure the quality of the results.
David MenningerAnalyst, ISG’s Ventana Research

“Data quality affects all types of analytics, but now, as we’re deploying more and more generative AI, if you’re not paying attention to data quality, you run the risks of toxicity, of bias,” Menninger said. “You’ve got to curate your data before training the models, and you have to do some postprocessing to ensure the quality of the results.”

In response, enterprises are placing greater emphasis on data quality than in the past, according to Saurabh Abhyankar, chief product officer at longtime independent analytics vendor MicroStrategy.

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“We’re actually seeing it more than expected,” he said.

Likewise, Madhukar Kumar, chief marketing officer at data platform provider SingleStore, said he is seeing increased emphasis on data quality. And it goes beyond just accuracy, he noted. Security is an important aspect of data quality. So is the ability to explain decisions and outcomes.

“The reason you need clean data is because GenAI has become so common that it’s everywhere,” Kumar said. “That is why it has become supremely important.”

However, ensuring data quality to get the benefits of AI isn’t simple. Nor are the consequences of bad data quality.

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The rise of GenAI

The reason interest in generative AI is exploding — the “why” behind generative AI being everywhere and requiring that data quality become a priority — is that it has transformative potential in the enterprise.

Data-driven decisions have proven to be more effective than those not informed by data. As a result, organizations have long wanted to get data in the hands of more employees to enable them to get in on the decision-making process.

But despite the desire to broaden analytics use, only about a quarter of employees within most organizations use data and analytics as part of their workflow. And that has been the case for years, perhaps dating back to the start of the 21st century.

The culprit is complexity. Analytics and data management platforms are intricate. They largely require coding to prepare and query data, and data literacy training to analyze and interpret it.

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Vendors have attempted to simplify the use of their tools with low-code/no-code capabilities and natural language processing features, but to little avail. Low-code/no-code capabilities don’t enable deep exploration, and the NLP capabilities developed by data management and analytics vendors have limited vocabularies and still require data literacy training to use.

Generative AI lowers the barriers that have held back wider analytics use. Large language models have vocabularies as large as any dictionary and therefore enable true natural language interactions that reduce the need for coding skills. In addition, LLMs can infer intent, further enabling NLP.

When generative AI is combined with an enterprise’s proprietary data, suddenly any employee with a smartphone and proper clearance can work with data and use analytics to inform decisions.

“With generative AI, for the first time, we have the opportunity to use natural language processing broadly in various software applications,” Menninger said. “That … makes technology available to a larger portion of the enterprise. Not everybody knows how to use a piece of software. You don’t have to know how to use the software; you just have to know how to ask a question.”

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Generative AI chatbots — tools that enable users to ask questions using natural language and get responses in natural language — are not foolproof, Menninger added.

“But they’re a huge improvement,” he said. “Software becomes easier to use. More people use it. You get more value from it.”

Meanwhile, data management and analytics processes — integrating and preparing data to make it consumable; developing data pipelines; building reports, dashboards and models — require tedious, time-consuming work by data experts. Even more tedious is documenting all that work.

Generative AI changes that as well. NLP reduces coding requirements by enabling developers to write commands in natural language that generative AI can translate to code. In addition, generative AI can be trained to carry out certain repetitive tasks on its own, such as writing code, creating data pipelines and documenting work.

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“There are a lot of tasks humans do,” Abhyankar said. “People are overworked, and if you ask them what they are able to do versus what they’d like to be able to do, most will say they want to do five or 10 times more. One benefit of good data with AI on top of it is that it becomes a lever and a tool to help the human being be potentially multiple times more efficient than they are.”

Eventually, generative AI could wind up being as transformational for knowledge workers as the industrial revolution was for manual laborers, he said. Just as an excavator is multiple times more efficient at digging a hole than a construction worker with a shovel, AI-powered tools have the potential to make knowledge workers multiple times more efficient.

Donald Farmer, founder and principal of TreeHive Strategy, likewise noted that one of the main potential benefits of effective AI is efficiency.

“It enables enterprises to scale their processes with greater confidence,” he said.

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However, the data used to train the AI applications that enable almost anyone within an organization to ask questions of their data and use the responses to inform decisions had better be right. Similarly, the data used to train the applications that take on time-consuming, repetitive tasks that dominate data experts’ time had better be right.

The need for data quality

Data quality has always been important. It didn’t just become important in November 2022 when OpenAI’s launch of ChatGPT — which represented a significant improvement in LLM capabilities — initiated an explosion of interest in developing AI models and applications.

Bad data has long led to misinformed decisions, while good data has always led to informed decisions.

A graphic lists six elements of data quality: accuracy, completeness, consistency, timeliness, uniqueness and validity.

But the scale and speed of decision-making were different before generative AI. So were the checks and balances. As a result, both the benefits of good data quality and consequences of bad data quality were different.

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Until the onset of self-service analytics spurred by vendors such as Tableau and Qlik some 15 years ago, data management and analytics were isolated to teams of IT professionals working in concert with data analysts. Consumers — the analysts — usually had to submit a request to data stewards, who would then take the request and develop a report or dashboard that could be analyzed to inform a decision.

The process could often take months and at least took days. And even when the report or dashboard was developed, it often had to be redone multiple times as the end user realized the question they asked wasn’t quite right or the resulting data product led to follow-up questions.

During the development process, IT teams worked closely with the data used to inform the reports and dashboards they built. They were hands-on, and they had time to make sure the data was accurate.

Self-service analytics altered the paradigm, removing some of the control from centralized IT departments and enabling end users with the proper skills and training to work with data on their own. In response, enterprises developed data governance frameworks to both set limits on what self-service users could do with data — to protect against self-service users going too far — and also give the business users freedom to explore within certain parameters.

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The speed and scale of data management and analytics-based decision-making increased, but it was still limited to a group of trained users who, with their expertise, were usually able to recognize when something seemed off in the data and not hastily take actions.

Now, just as generative AI changes who within an organization can work with data and what experts can do with it, it changes the speed and scale of data-informed decisions and actions. To feed that speed and scale with good data, automated processes — overseen by humans who can intervene when necessary — are required, according to Farmer.

“It puts an emphasis on processes that can be automated, identifying data-cleaning processes that require less expertise than before,” Farmer said. “That’s where it’s changing. We’re trying to do things at much greater scale, and you just can’t have a human in the loop at that scale. Whether the process can be audited is very important.”

Abhyankar compared the past and present to the difference between a small, Michelin-starred gourmet restaurant and a fast-food chain.

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The chef at the small restaurant, each day, can shop for the ingredients of every dish and then oversee the kitchen as each dish gets made. At a chain, the scale of what needs to be bought and the speed with which the food needs to be made make it impossible for a chef to oversee every detail. Instead, a process ensures no bad meat or produce makes it into meals served to consumers.

“[Data quality] is really important in a world where you’re going from hand-created dashboards and reports to a world where you want AI to do [analysis] at scale,” Abhyankar said. “But you can’t scale unless you have a system in place so [the AI application] can be precise and personalized to serve many more people with many more insights on the fly. To do that, the data quality simply has to be there.”

Benefits and consequences

The whole reason enterprise interest is rising in developing AI models and applications and using AI to inform decisions and automate processes — all of which need high-quality data as a foundation — is the potential benefits.

The construction worker who now has an excavator to dig a hole rather than a shovel can be multiple times more efficient. And in concert with a few others at the controls of excavators, they can dig the foundation for a new building perhaps a hundred times faster than they could by hand.

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A construction worker with a cement mixer can follow up and pour the foundation multiple times faster than if they had to mix the cement and pour it by hand. Next, the girders can be moved into place by cranes rather than carried by humans, and so on.

It adds up to an exponentially more efficient construction process.

The same is true of AI in the enterprise. Just as construction teams can rely on the engines and controls in excavators, cement mixers, cranes and other vehicles that scale the construction process, if the data fueling AI models and applications is trustworthy, organizations can confidently scale business processes with AI, according to Farmer.

And scale in the business world — being able to do exponentially more without having to expand staff — means growth.

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“Data quality enables enterprises to scale their processes with greater confidence,” he said. “It enables them to build fine-grained processes like hyperpersonalization with greater confidence. Next-best offers, recommendation engines, things that can be highly optimized for an individual — that sort of thing becomes very possible.”

Beyond retail, another common example is fraud detection, according to Menninger. Detecting fraud amid millions of transactions can be nearly impossible. AI models can check all those transactions, while not even teams of humans have the capacity to look at them all, much less find patterns and relationships between them.

“If accurate data is being fed into the models to detect fraud, and you can improve the detection even just slightly, that ends up having a large impact,” Menninger said.

But just as the potential benefits of good-quality data at the core of AI are greater than good data without AI, the consequences of bad data at the core of AI are greater than the consequences of bad data without AI. The speed and scale that AI models and applications enable result in the broader and faster spread of fallout from poor decisions and actions.

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Back when IT teams controlled their organizations’ data and when a limited number of self-service users contributed to decisions, the main risk of bad data was lack of trust in data-informed decisions and the resulting loss of efficiencies, according to MicroStrategy’s Abhyankar. In rare cases, it could lead to something more severe, but there was usually time for someone to step in and stop something from happening before it spread.

Now, the potential exists to not only scale previous problems, but also create new ones.

If AI models and applications are running processes and making decisions without someone checking them before actions are taken, it could lead to significant ethical problems such as baselessly denying an applicant a credit card or mortgage. Similarly, if a human uses AI outputs to make decisions, but the output is misinformed, it could result in serious ethical issues.

“You scale the previous problems,” Abhyankar said. “But it’s actually worse than that. In scenarios where the AI is making decisions, you’re making bad decisions at scale. If you run into ethical problems, it’s catastrophically bad for an organization. But even when AI is just delivering information to a human being, you’re scaling the problems.”

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Farmer noted that AI doesn’t deliver outputs based on single data points. AI models and applications are statistical, looking at broad swaths of data to inform their actions. As long as most of the data used to train a model or application is correct, the model or application will be useful.

“If a data set is poor quality, you’ll get poor results,” Farmer said. “But if one piece of data is wrong, it’s not going to make much difference to the AI because it’s looking at statistics as a whole.”

That is, unless it’s that fine-grained decision about an individual such as whether to approve a mortgage application. In that case, if the data is wrong, it can lead to serious ethical consequences. Even more catastrophically, in a healthcare scenario, bad data could lead to the difference between life and death.

“If we’re using AI to make decisions about individuals — are we going to give someone a mortgage — then having high-quality individual data becomes extremely important, because then we have given this system over,” Farmer said. “If we’re talking about AI making fine-grained decisions, then the data has to be very high-quality.”

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Ensuring data quality

With data quality so critical to the success of AI, as well as reaping the benefits of broader use of technologies and exponentially increased efficiency, the obvious question is how enterprises can ensure good data goes into models and applications so that good outputs result.

There is, unfortunately, no simple solution — no fail-safe.

Data quality is difficult. Enterprises have always struggled to ensure only good-quality data is used to inform decisions. In the era of AI, including generative AI, that’s no different.

“The problem is still hard,” Abhyankar said.

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But there are steps that organizations can take to lessen the likelihood of bad data slipping through the cracks and affecting the accuracy of models and applications. There are technologies they can use and processes they can implement.

Ironically, many of the technologies that can detect bad data use AI to do so.

Vendors such as Informatica and Oracle offer tools designed specifically to monitor data quality. These tools can look at data characteristics such as metadata and data lineage, sometimes have master data management capabilities, and in general are built to detect problematic data. Other vendors such as Alation and Collibra provide data catalogs that help enterprises organize and govern data, including descriptions of data, to provide users with information before they operationalize any data.

Still other vendors including Acceldata and Monte Carlo offer data observability platforms that use AI to monitor data as it moves through data pipelines, detecting irregularities as they occur and automatically alerting customers to potential problems. But unlike data quality tools and data catalogs that address data quality while data is at rest before being used to train AI models and applications, observability tools monitor data while it is in motion on its way to a model or application.

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“Increasingly, AI is actually in a sense running its own data quality,” Farmer said. “Many of those tools work on inferences, work on discovering patterns of the data. It turns out that AI is very good at that and doing it at scale.”

More important than any tooling, however, is that humans always remain involved and check any output before it is used to take action.

Just as a hybrid approach emerged as ideal for cloud computing — including on-premises, private cloud and public cloud — a hybrid approach that uses technology to augment humans is emerging as the ideal approach to working with the data used to train AI, according to SingleStore’s Kumar.

“First and foremost is to allow humans to have control,” he said.

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Humans simply know more about their organization’s data than machines and can better spot when something seems off. Humans have been working with their organization’s data from their organization’s founding, which in some cases means there are decades’ worth of code used to develop and inform dashboards and reports that humans can perfectly replicate, but a machine might not know.

Humans, in a simple example, know whether their company’s fiscal year starts on Jan. 1 or some other date, while a model might assume it starts on Jan. 1.

“Hybrid means human plus AI,” Kumar said. “There are things AI is really good at, like repetition and automation, but when it comes to quality, there’s still the fact that humans are a lot better because they have a lot more context about their data.”

If there’s a human at the end of the process to check outputs, organizations can better ensure actions taken will have their intended results, and some potentially damaging actions can be avoided.

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If there’s a person to make sure a mortgage application should be rejected or approved, it will benefit their organization’s bottom line. The approved mortgage will result in profits, as well as avoid the serious consequences of mistakenly declining someone’s application based on biased data, while the declined mortgage will avoid potential losses related to a default.

If there’s a healthcare worker to check whether a patient is allergic to a recommended medication or that medication might interact badly with another medication the patient is taking, it could save a life.

The AI models and applications, fueled by data, can be left to do their work. They can automate repetitive processes, generate code to develop applications, write summaries and documentation, respond to user questions in natural language and so on. They’re good at those tasks, when informed by good-quality data.

But they’re not perfect, even when the data used to train them is as good as possible.

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“There always has to be human intervention,” Menninger said.

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.

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Cards Against Humanity is suing SpaceX for trespassing and filling its property with ‘space garbage’

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Cards Against Humanity is suing SpaceX for trespassing and filling its property with ‘space garbage’

Cards Against Humanity is the latest entity to take on Elon Musk in court. The irreverent party game company filed a $15 million lawsuit against SpaceX for trespassing on property it owns in Texas, which happens to sit near SpaceX facilities.

According to filed in a federal court in Texas, Musk’s rocket company began using its land without permission for the last six months. SpaceX took what was previously a “pristine” plot of land “and completely fucked that land with gravel, tractors, and space garbage,” CAH wrote in a .

As you might expect from the card game company known for its raunchy sense of humor and headline-grabbing stunts, there’s an amusing backstory to how it became neighbors with SpaceX in Texas in the first place. , the company bought land along the US-Mexico border as part of a crowdfunded effort to protest then President Donald Trump’s plan to build a border wall. Since then, the company writes, it has maintained the land with regular mowing, fencing and “no trespassing” signs.

SpaceX later purchased adjacent land and, earlier this year, allegedly began using CAH’s land amid some kind of construction project. From the lawsuit (emphasis theirs):

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The site was cleared of vegetation, and the soil was compacted with gravel or other substance to allow SpaceX and its contractors to run and park its vehicles all over the Property. Generators were brought in to run equipment and lights while work was being performed before and after daylight. An enormous mound of gravel was unloaded onto the Property; the gravel is being stored and used for the construction of buildings by SpaceX’s contractors along the road. Large pieces of construction equipment and numerous construction-related vehicles are utilized and stored on the Property continuously. And, of course, workers are present performing construction work and staging materials and vehicles for work to be performed on other tracts. In short, SpaceX has treated the Property as its own for at least six (6) months without regard for CAH’s property rights nor the safety of anyone entering what has become a worksite that is presumably governed by OSHA safety requirements.

SpaceX, according to the filing, “never asked for permission” to use the land and “and hasnever reached out to CAH to explain or apologize for the damage.” The rocket company did, however, give “a 12-hour ultimatum to accept a lowball offer for less than half our land’s value,” according to a statement posted online. A spokesperson for CAH said the land in question is “about an acre” in size.

What CAH's Texas land looked like prior to SpaceX's alleged trespassing.

What CAH’s Texas land looked like prior to SpaceX’s alleged trespassing. (Christopher Markos / Cards Against Humanity)

In response to the ultimatum, CAH filed a $15 million lawsuit against SpaceX for trespassing and damaging its property. The game company, which originally was funded via a Kickstarter campaign, says that if it’s successful in court it will share the proceeds with the 150,000 fans who helped originally purchase the land in 2017. It created where subscribers can sign-up for a chance to get up to $150 of the potential $15 million payout should their lawsuit succeed. (A disclaimer notes that “Elon Musk has way more money and lawyers than Cards Against Humanity, and while CAH will try its hardest to get me $100, they will probably only be able to get me like $2 or most likely nothing.)

SpaceX didn’t immediately respond to a request for comment. But CAH isn’t the only Texas landowner that’s raised questions about the company’s tactics. SpaceX has been aggressively growing its footprint in Southern Texas in recent years. The expansion, which has resulted in many locals selling their land to SpaceX, has rankled some longtime residents, according to an investigation Reuters.

CAH says that Musk’s past behavior makes SpaceX’s actions “particularly offensive” to the company known for taking a stance on social issues.

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“The 2017 holiday campaign that resulted in the purchase of the Property was based upon CAH undertaking efforts to fight against ‘injustice, lies, [and] racism,” it states. “Thus, it is particularly offensive that these egregious acts against the Property have been committed by the company run by Elon Musk. As is widely known, Musk has been accused of tolerating racism and sexism at Tesla and of amplifying the antisemitic ‘Great Replacement Theory.’ Allowing Musk’s company to abuse the Property that CAH’s supporters contributed money to purchase for the sole purpose of stopping such behavior is totally contrary to both the reason for the contribution and the tenets on which CAH is based.”

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What Hamster Kombat is teaching us about game marketing | The DeanBeat

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What Hamster Kombat is teaching us about game marketing | The DeanBeat

Game marketing is changing, thanks to Hamster Kombat, a tapping mini-game on Telegram that has been downloaded more than 300 million times since March. It took only 73 days for Hamster Kombat to reach its first 100 million users.

Traditional marketing tactics are losing their power when it comes to attracting the attention of target audiences, said Tavia Wong, chief marketing officer at Credbull, a small private credit company in Asia with a dozen employees. The age of the viral game is back, at least on one platform. And many are starting to copy the formula like PiP World, Bondex, Gamee and Liithos.

In an interview with GamesBeat, she said that Web3 tap-and-earn games like Hamster Kombat are the unexpected inspiration for marketing professionals, and she believes every business can learn from their success, as well as how to leverage it for translating hype into revenue. Will it be a lifeline for Web3 games, which have struggled to get mainstream acceptance?

“When I first saw Hamster Kombat, I thought it was quite silly. It was going viral on Telegram and I thought it probably wasn’t going to go anywhere,” Wong said. “”But low and behold, the community has been growing really, really quickly. It’s one of the most successful games on Telegram, where there’s a cute hamster that a lot of people can just tap away on.”

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She added, “As a marketer, it really makes you sit up and go, ‘Okay, what’s there? Why is the community going so quickly?’ And it keeps growing.”

Hamster Kombat

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Hamster Kombat is a tap-to-earn crypto game on Telegram, where you click or tap on the screen. Players take the role of a CEO at a cryptocurrency exchange. The creators said in May 2024 that they would launch a token on The Open Network (TON), a Layer-1 blockchain originally created by Telegram. Now the development is being handled externally by the community.

In the game, players start as a bald hamster under contract to be a CEO of a cryptocurrency exchange. Users can tap the hamster avatar to generate in-game coins, but the main gameplay mechanic involves purchasing exchange upgrades to increase the hourly profit. You can earn coins by referring friends to play the game on Telegram or by finishing harder in-game tasks like solving a daily Morse code cipher.

It’s popular for the moment. But things can change. Telegram’s founder Pavel Durov was arrested in France and is being held on charges that the platform doesn’t do enough to protect users from fraud, terror and other negative influences. It’s not clear how this will affect Telegram.

Marketing savvy

Tavia Wong of Credbull.

At a marketing agency, Wong ran ad campaigns for more than eight years and she sold the agency to a Fortune 500 company. She then joined an AI firm and later looked into crypto. Her current company Credbull is looking to engage with the retail community — the enthusiasts who will pay attention to such “tap and earn” games. So she really wanted to find out what was driving this game forward.

“One of the main reasons behind it is incentives (check out our recent story on the Benjamin app), and it’s almost like going to a casino. It’s a little bit of a gambling effect because people tap. They get more points when they refer their friends to the game, and they also get points to rank on the leaderboard. So they have to do it like a daily streak,” Wong said.

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It could be viewed as addictive like gambling. Or like other more benign habits.

“Think about it like Duolingo. So every day you have to kind of log in and they are cool sound effects,” Wong said. “There are things that really keep you engaged within the app. And there are leaderboards to show you where you rank, so that you really don’t want to go down because it affects how many points you’re going to get. And it becomes this crazy, crazy game. And you would think that a user is probably going to get a huge amount of tokens or rewards — a financial incentive for being so active. But the fact is that most of this project has not even launched or released yet. So everybody is playing in anticipation of a future reward.”

It’s like any other speculative bubble in that respect. There’s a lot of word of mouth, and people are just really engaged within those communities, Wong said.

This bodes well for marketers because they lost virality after Facebook shutdown the game spam and after Apple hobbled targeted ads in favor of user privacy.

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“It’s a very good thing for marketers because these games, as we get more sophisticated, can also introduce the idea of clans. And these clans compete against each other to see who gets the most,” Wong said. “That makes you feel a sense of belonging to a bigger club.” And the wining gets bigger.

Soon enough, there’s a domino effect that fuels the game’s momentum.

Growth hacking

Hamsters are popular on Telegram.

Wong believes that Hamster Kombat leverages growth hacking. She said the game’s explosive growth isn’t a coincidence. It’s a winning formula fueled by social media buzz, word-of-mouth, and clever referral programs that create a self-sustaining wave of new users. Every victory, every success story, and every shared experience attract more players to the action. It’s a domino effect fueling the game’s momentum.

It’s also tapping into gamification on Telegram, a platform that doesn’t have a ton of hardcore games. She thinks Web3 games like Hamster Kombat are exploding with easy-to-play, gamified experiences that seamlessly integrate with crypto.

Telegram happens to have a lot of crypto fans, particularly those who are concerned about traditional government that they’re willing to put their money into cryptocurrency. Telegram also has a lot of people who therefore have their own cryptocurrency wallets — something that Hamster Kombat leverages.

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Wong believes this is the future of user engagement, and it’s not just for gamers anymore. Think gamified loyalty programs or interactive marketing campaigns. Tap into reward-seeking behavior, a natural competitive spirit, and the classic fear of missing out to incentivize potential customers to engage with your communications. 

Good old days of Facebook

Developers can still leverage network effects on Telegram, as it’s like the old Facebook before it cracked down on viral messaging. Being strategic about choosing a crypto native messaging app to launch a relevant tap and earn game or other gamified experiences is key to leverage the large user bases of apps like Telegram, Wong said.

For the moment, players are embracing the quirkiness of Hamster Kombat. These mini-games are fun, quirky, and full of surprises. Forget dry press releases. Think interactive challenges, gamified product features, or even a mascot that embodies your brand’s personality.

And community is everything, Wong said. Look at Hamster Kombat’s massive social media following, consisting of 300 million users; 13 million followers on Twitter/X, and their YouTube account of tens of millions subscribers. Web3 games foster strong communities through shared gameplay and rewards. Can your marketing strategy create a similar level of engagement? 

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Harnessing traffic

A hyperrealistic hamster.

It’s important not to measure engagement just for the sake of reporting on success. Marketers need to ask themselves how they can harness the traffic and attention they’re receiving from successful campaigns to translate it into users, she said.

By making gamified experiences highly relevant to the end solution, companies can not only educate users on their solution, for instance as Monzo did by providing insights on spending habits, but by directly pushing communications aimed at customer onboarding within the experience itself and continually rewarding them with bonus points for following the user journey.  

Each day, millions of new users are joining Hamster Kombat, making it one of the fastest-growing digital services in the world, according to Telegram. We’ll see what happens once the Hamster Kombat team mints its token on TON.

Loyalty

What kind of gamers like Hamster Kombat?

I asked Wong what’s the difference between this and regular loyalty programs that people are creating.

“With regular loyalty programs, you come up with a program, you try to engage your existing community, but people don’t really care much about your loyalty program unless you’re a really big brand,” she said. “So how do you build something like that from scratch? So I think the key is to go where the traffic is. Go out on Telegram, for example, where these tap to earn games are so popular. You can tap into the millions of people already using Telegram.”

Hamster Kombat is a hypercasual game where everybody can play. Brands can start moving in on the action to reach the players with various kinds of ads. The people are already there. Credbull launched its own tap-and-earn game that got off the ground.

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“We’re leveraging on the hype of this, and then putting in all the psychology of good marketing and selling, where it’s like social rush,” she said. “You have speculation and future rewards and then create surprises within the game in a very simple way. It leads to really explosive in growth.”

“What’s cool is that the the benefits of this go past marketing. So our product team has also been loving tis as they can launch small product features and test them within the game. You can use it to test offers, product features and see how the crowd responds to it,” she said. “It becomes your focus group for your product team. You get immediate feedback on whether users love it or not.”

Wong compares this time, where the number of games is in the hundreds, to the early days of Facebook. She predicts the brands will come in. Credbull has experimented with its own Telegram game in various ways.

“I think there is a parallel with the game and metaverse brands, like how they want to engage with retail consumers and so they created shops on the metaverse,” she said.

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These brands go where someone is succeeding in getting attention.

“I wouldn’t underestimate how effective Telegram has been at onboading users,” she said. “There are so many messaging apps out there. But Telegram is one of the top in the world.”

Leveraging popularity

Nothing lasts forever. But when you can leverage popularity for a purpose, you can grow.

When you’re planning loyalty campaigns, you’re trying to get people interested in the first place and then to retain that interest in the long run, she said. Big social media companies have studied what it takes to make people play. The details get very granular. Games like Angry Birds had memorable sounds that could serve as psychological triggers within your brain to keep playing, she said. A huge team comes together to understand the psychology, game mechanics, the tokenomics and more to start social engagement. In the case of Telegram, it’s so brainless that you create a game to tap.

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The good question is at what point does the game with weak gameplay give way to a more sophisticated game with much better gameplay. The game can be simple, but it has enough sophistication to keep people playing. Gamification apps focus on motivating the player to stay engaged and use social techniques to retain them.

I noted one triple-A game company, Liithos, wanted to make an open world game. It couldn’t raise money in the current environment, so it took one piece of the intellectual property and made a character out of it. Then it launched it as a viral clicker game. It’s called Clickbait, a satirical game called Clickbait as part of its No One Is Safe franchise.

It’s trying to draw attention to its bigger mission and games through the Clickbait game, which revolves around mischievous chatbot called RantCPU. and it focuses on the anxiety around AI and a world where humanity has destroyed itself. It’s a new transmedia property set to launch as a game on Steam, a comic book series from Scout, and trading cards.

Wong noted some game companies raise money directly from the community. With NFTs, sometimes that worked and sometimes it went horribly wrong, with scammers stealing money.

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“When I saw this tap and earn game, as a CMO, to be honest, I dismissed it because I was like, it’s so silly. I played so much better production games from big gaming houses. Why am I spending my time playing on this? But when I looked at the metrics, they were growing like crazy,” she said.

Wong added, “I understand why it’s so popular now, because it’s using all the effects of the psychology.”


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The 25 battery tech startups that just got a piece of $3B in federal funds  

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MESC Batteries Map

The federal government is handing out another $3 billion to startups in the buzzy battery tech sector. 

The investment, which the Biden administration announced Friday, is the latest injection of capital to come from a $16 billion pot that the Department of Energy set aside to build out local battery manufacturing, processing, and recycling facilities. It’s part of the broader Inflation Reduction Act, which passed into law in August 2022 and includes incentives to boost the domestic battery industry and reduce reliance on the world’s battery incumbent, China.

This tranche of funding went to startups across 14 states, but there were certain winners that will see the bulk of the expected 18,000 jobs to be created as a result of this funding. South Carolina companies secured the most funding, with five projects being awarded $850 million. For example, Cirba Solutions grabbed a $200 million bag to build, own, and operate a facility to process large-scale battery-grade salts to support the electric vehicle market. 

Four Michigan companies snagged a total $355 million in grant money. General Motors-backed Mitra Chem got $100 million from the DOE and another $25 million from the state of Michigan’s Competitiveness Fund. The company will partner with Sun Chemical to build a facility that will develop and manufacture lithium-iron phosphate materials for electric vehicles and battery storage systems.

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The DOE also awarded venture-backed battery recycling startup Ascend Elements $125 million, alongside chemical manufacturing company Orbia, to build a recycled graphite production facility in Kentucky that converts graphite residue from lithium-ion battery recycling and cathode material production into battery-grade graphite.

The loans and grants will go to companies working across the battery supply chain, from critical mineral extraction to production of cathode and anode materials, from electrolyte salt production to battery recycling. 

Here’s a list of all of the startups that have secured funds:

The DOE awarded $3 billion to 25 battery startups as part of the Battery Materials Processing and Battery Manufacturing and Recycling Programs.
Image Credits: U.S. Department of Energy

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