Pick the wrong printer and it could empty your wallet as fast it drains the ink tanks. Though you can buy a decent printer for under £40, the cost of ink to keep it running can end up being far more than this – especially with a wasteful printer.

We run extensive ink consumption tests to expose the thirstiest printers, whether you print every day or just occasionally. And our latest batch has revealed one of the most wasteful printers we’ve ever seen. One new printer could use over 4000% more ink if you only print once in a while, sacrificing  huge amounts to wasteful ink cleaning cycles.

Printer reviews – see printers from HP, Canon and other brands expertly tested

HP PageWide: fast, but may leave you furious

PageWide is a new technology from HP, using thousands of print heads spanning the entire width of the page. It’s claimed to combine inkjet print quality with laser speed. The HP PageWide 377DW, which we’ve recently tested, is it certainly fast. It can knock out an 8×10-inch photo in best quality in just 19 seconds.

It can scan and copy, plus has built in wi-fi. However, our unique tests have found a pretty sizeable downside – eye-watering running costs. These are due to some of the worst ink waste we’ve ever seen from a printer.

The dirty truth of printer cleaning

Over years of testing, we’ve found that many printers automatically clean themselves when you’re not even printing, using precious ink that never reaches the page. You can’t print with it, but this is still ink that you’re paying for.

So, we run ‘occasional printing’ tests over a period of weeks to measure the ink that is used for automatic cleaning cycles. By weighing ink cartridges before and after our printing tests we can work out the amount of ink that’s lost during cleaning.

This HP PageWide uses a staggering 4477% more ink when printing 20 black sheets and 10 in colour over a period of weeks, turning the printer off between uses, compared with doing to the same job in one go. That means your costs skyrocket if you only use the printer now and then.

Another sting in the tail? A full new set of ink cartridges will cost you nearly £250.

Our new batch of printer reviews

You can’t simply rely on the initial price of a printer when it comes to picking a value model. Our tests have identified brilliant Best Buys that won’t waste your ink, and can still churn out top quality prints.

Which? members can log in to see our top printer recommendations. Not a member yet? Sign up to a £1 trial and make sure you don’t throw your money away on a wasteful printer.

Our latest printer results also include full reviews of the popular Canon Pixma TS8050, and it’s more premium sister model, the Canon Pixma TS9050.

We have more than 230 fully-tested inkjet and laser printers to choose from, with Best Buys starting at under £50. These cracking printers will give you top notch print quality and low running costs.

When it comes to laptops, battery life can be a key buying decision. But Which? has discovered that the battery life claimed by laptop manufacturers rarely lives up to reality, with our tests finding it often falls drastically short.

Which? testing has shown that almost all laptop manufacturers overstate their battery claims. In some cases, the battery life estimates were double what we achieved in our lab testing.

Laptop reviews – Read our latest laptop reviews, from brands such as Apple, Dell, HP and Lenovo.

Manufacturer claims vs reality

We’ve compared the stated manufacturer claims against our laptop tests over the past year, comparing 67 models. Overall we found that manufacturers are missing their claims not by minutes, but by hours. The most optimistic laptop manufacturers are overstating their battery life by 50% or more, leaving you searching for the power cable twice as often as you’d expect.

It’s not all bad news, however – our tests found that with Apple MacBooks, you could meet or even exceed the claimed battery life, according to Apple.

Laptop batteries: What we found

We test laptops for battery life while actively browsing the web over wi-fi, running the laptop in this fashion until the battery is fully drained. In the graphic above, we show the average claimed battery life for all the laptops we’ve tested since January 2016, versus the average battery life in our tests. Number of laptops tested: Acer (8); Apple (3), Asus (8), Dell (10), HP (12), Lenovo (20), Toshiba (6).

The Which? battery test

Each laptop we judge goes through our battery tests at least three times. We don’t simply trust battery capacity claims: we actually drain the whole battery from start to finish, several times over, during various tasks. One test involves watching films until the battery finally shuts down, another continually browsing websites over wi-fi.

We believe that that these tests are representative of the real world use that a laptop would get. As the figures we arrived at are often drastically different to the manufacturer claims, we have to wonder how their own estimates are arrived at.

Below are some examples of the sorts of discrepancies our testing unveiled:

Lenovo Yoga 510

• Claimed battery life: 5 hours


• Which? tests: 2 hours, 7 minutes

Apple MacBook Pro 13 

• Claimed battery life: 10 hours


• Which? tests: 12 hours

HP Pavilion 14-al115na

• Claimed battery life: 9 hours


• Which? tests: 4 hours 25 minutes

Dell Inspiron 15 5000

• Claimed battery life: 7 hours


• Which? tests: 3 hours 58 minutes

Acer E15 

• Claimed battery life: 6 hours


• Which? tests: 2 hours 56 minutes

Which? reached out to laptop manufacturers and asked why there were such big differences in their claims and our findings. Dell told us ‘It’s difficult to give a specific battery life expectation that will directly correlate to all customer usage behaviours because every individual uses their PC differently – it’s similar to how different people driving the same car will get different gas mileage depending on how they drive.’

HP said that is battery tests ‘uses real life scripts and runs on real applications like Microsoft office.‘, and that the exact specifications, such as screen resolution, will impact the results for each model.

It’s vital to look past manufacturer claims and dig a little deeper to find out what kind of battery life you can really expect from your laptop. We test like no one else, so check our full laptop reviews for the Which? verdict on laptop battery life.

A billion and a half photos find their way onto Facebook every single day and the company is racing to understand them and their moving counterparts with the hope of increasing engagement. And while machine learning is undoubtedly the map to the treasure, Facebook and it’s competitors are still trying to work out how to deal with the spoils once they find them. Facebook AI Similarity Search (FAISS), released as an open source library last month, began as an internal research project to address bottlenecks slowing the process of identifying similar content once a user’s preferences are understood. Under the leadership of Yann LeCun, Facebook’s AI Research (FAIR) lab is making it possible for everyone to more quickly relate needles within a haystack.

On its own, training a machine learning model is already an incredibly intensive computational process. But a funny thing happens when machine learning models comb over videos, pictures and text  — new information gets created! FAISS is able to efficiently search across billions of dimensions of data to identify similar content.

In an interview with TechCrunch, Jeff Johnson, one of the three FAIR researchers working on the project, emphasized that FAISS isn’t so much a fundamental AI advancement as a fundamental AI enabling technique.

Imagine you wanted to perform object recognition on a public video that a user shared to understand its contents so you could serve up a relevant ad. First you’d have to train and run that algorithm on the video, coming up with a bunch of new data.

From that, let’s say you discover that your target user is a big fan of trucks, the outdoors and adventure. This is helpful, but it’s still hard to say what advertisement you should display — A rugged tent? An ATV? A Ford F-150?

To figure this out, you would want to create a vector representation of the video you analyzed and compare it to your corpus of advertisements with the intent of finding the most similar video. This process would require a similarity search, whereby vectors are compared in multi-dimensional space.

In this representation of a similarity search, the blue vector is the query. The distance between the “arrows” reflects their relative similarity.

In real life, the property of being an adventurous outdoorsy fan of trucks could constitute hundreds or even thousands of dimensions of information. Multiply this by the number of different videos you’re searching across and you can see why the library you implement for similarity search is important.

“At Facebook we have massive amounts of computing power and data and the question is how we can best take advantage of that by combining old and new techniques,” posited Johnson.

Facebook reports that Implementing k-nearest neighbor across GPUs resulted in an 8.5x improvement in processing time. Within the previously explained vector space, nearest neighbor algorithms let us identify the most closely related vectors.

More efficient similarity search opens up possibilities for recommendation engines and  personal assistants alike. Facebook M, its own intelligent assistant, relies on having humans in the loop to assist users. Facebook considers “M” to be a test bed to experiment with the relationship between humans and AI. LeCun noted that there are a number of domains within M where FAISS could be useful.

“An intelligent virtual assistant looking for an answer would need to look through a very long list,” LeCun explained to me. “Finding nearest neighbors is a very important functionality.”

Improved similarity search could support memory networks to help keep track of context and basic factual knowledge, LeCun continued. Short term memory contrasts with learned skills like finding the optimal solution to a puzzle. In the future, a machine might be able to watch a video or read a story and then answer critical follow up questions about it.

More broadly, FAISS could support more dynamic content on the platform. LeCun noted that news and memes change every day and better methods of searching content could drive better user experiences.

A billion and a half new photos a day presents Facebook with a billion and a half opportunities to better understand its users. Each and every fleeting chance at boosting engagement is dependent on being able to quickly and accurately sift through content and that means more than just tethering GPUs.

Featured Image: Bryce Durbin

The race is on to attract as much expertise in artificial intelligence as possible at tech companies large and small, and more than a few Silicon Valley giants are looking inward to convert tech talent they already possess into the AI resources they increasingly need. Facebook has its own AI course, which is oversubscribed, according to a new report by Wired, and which is led by one of the leading AI researchers in the world.

Facebook’s Larry Zitnick, who is a key leader at the social networking company’s Artificial Intelligence Research Lab, as well as a Microsoft Research and CMU Robotics alum, teaches a class on deep learning for Facebook employees that draws over-capacity crowds. Zitnick’s course sparks strong competition among engineers who already rank among the best in the world, each vying to come to grips with and excel at a field outside of their original purview, but one that few fail to recognize is the hottest in tech.

On the other hand, AI and deep learning increasingly touch all aspects of the technology business, so experts with understanding of where the overlap might prove most useful in their own original discipline are also going to be very much in demand. There are external efforts underway to help create more of these polyglot deep learning pros, including at online educational firms like Udacity, but new talent isn’t rolling in fast enough from outside sources, traditional and non-traditional alike.

Facebook also offers an AI immersion program, which embed prospects within the work it’s doing in the field. The goal, again, is to spread expertise across the company, and thread deep learning know-how into the organization’s very DNA. Expect this to be the rule for big tech company behavior for the foreseeable future.

Featured Image: Getty Images/Yuri Khristich/Hemera (modified by TechCrunch)