Within a short span of time and with few instructions, people can solve complex problems from scratch, for instance, loading the trunk of a car with seemingly too many objects. This is the core of human intelligence—its rapid and flexible nature. What is the cognitive scheme that allows us to create novel and complex strategies? And do “intelligent” machines use similar or fundamentally different schemes?

To answer these questions, scientists at the Champalimaud Centre for the Unknown in Portugal, and the University of California, Berkeley, created Hexxed. This mobile game consists of a series of fun and challenging puzzles designed to provide unique insight into how intelligence works. This free app has compatible versions for both iPhone and Android.

Taking science out of the lab

“Hexxed joins a global trend of citizen science games in which individuals around the world can contribute to scientific discoveries by simply playing,” says Gautam Agarwal, one of the scientists who developed the game as part of his research project in the lab of Zachary Mainen at Champalimaud.

Why move the experiment outside of the lab? According to Agarwal, this is the best way to gather data sets that are diverse and large enough to tap into difficult questions, such as how age and cultural background shape human thinking. “Experiments in laboratory conditions have a limited number of subjects, and are often repetitive and dull. In contrast, online games can be played by an unrestricted number of people worldwide, and inspire players to participate fully by immersing them in an evolving stream of experiences.”

Human vs. machine intelligence

The diversity of the data goes even further, as the team plans to use the game to learn not only about human intelligence, but about machine intelligence, as well. Mattia Bergomi, a researcher involved in the study, points out that video games are commonly used to test the capacity of artificial intelligence, but often fall short of the mark.

“The majority of games fall into one of two categories,” he explains. “On one extreme, there are challenging games that can only be solved with complex strategies. This results in problem-solving approaches that are difficult to formulate mathematically and therefore difficult to compare across subjects. On the other extreme, you have simple games that can easily be described mathematically. But then, those are not challenging enough to draw out intelligent problem-solving schemes.”

Hexxed was developed to bridge between the two extremes: it’s quite challenging, but it can still be described by simple mathematical constructs. “This unique design will allow us to compare strategies adopted by humans with those generated by machines in a systematic and comprehensive manner,” Agarwal adds.

Game on!

So what is intelligence and how does it vary across humans and machines? Play Hexxed and we may have an answer soon enough.

The post What makes human intelligence exceptional? The answer may be hidden inside this game appeared first on Artificial Intelligence.

Google’s Android Studio IDE team has released the stable version of Android Studio 4.1, featuring machine learning improvements and a database inspector.

With the 4.1 release, Android Studio improves on-device machine learning support via backing for TensorFlow Lite models in Android projects. Android Studio generates classes so models can be run with better type safety and less code.

The database inspector, meanwhile, enables querying of an app’s database, whether the app uses the Jetpack Room library or the Android platform version of SQLite directly. Values can be modified using the database inspector, with changes seen in apps.

Introduced October 12 and accessible from developer.android.com, Android Studio 4.1 also makes it easier to navigate Dagger-related dependency injection code by providing a new gutter action and extending support in the Find Usages Window. For example, clicking on the gutter action next to a method that consumes a given type navigates to where a type is used as a dependency.

Other capabilities in Android Studio 4.1 include templates in the create New Project dialog now use Material Design Components and conform to updated guidance for themes and styles by default. These changes make it easier to recommended material styling patterns and support UI features such as dark themes.

Android Emulator now can also be run directly in Android Studio. This can conserve screen real estate and enable navigation quickly between the emulator and editor window using hotkeys. Also, the emulator now supports foldables, with developers able to configure foldable devices with a variety of designs and configurations.

In addition, symbolification for native crash reports is available; updates to Apply Changes allow for faster builds and the Android Studio Memory Profiler now includes a Native Memory Profiler for apps deployed to physical devices running Android 10 or later.

The Native Memory Profiler tracks allocations and deallocations of objects in native code for a specific time period and offers information about total allocations and remaining heap size.

Rounding off the changes, C/C++ dependencies can be exported from AAR (Android Archive) files; Android Studio Profilers can be accessed in a separate window from the primary Android Studio window, which is useful for game developers; System Trace UI improvements are on offer and 2,370 bugs were fixed and 275 public issues were closed.

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(Nanowerk News) Androids, or robots with humanlike features, are often more appealing to people than those that resemble machines — but only up to a certain point. Many people experience an uneasy feeling in response to robots that are nearly lifelike, and yet somehow not quite “right.” The feeling of affinity can plunge into one of repulsion as a robot’s human likeness increases, a zone known as “the uncanny valley.”

The journal Perception (“The Uncanny Valley Phenomenon and the Temporal Dynamics of Face Animacy Perception “) published new insights by Emory psychologists into the cognitive mechanisms underlying this phenomenon.

Since the uncanny valley was first described, a common hypothesis developed to explain it. Known as the mind-perception theory, it proposes that when people see a robot with human-like features, they automatically add a mind to it. A growing sense that a machine appears to have a mind leads to the creepy feeling, according to this theory.

“We found that the opposite is true,” says Wang Shensheng, first author of the new study, who did the work as a graduate student at Emory and recently received his PhD in psychology. “It’s not the first step of attributing a mind to an android but the next step of ‘dehumanizing’ it by subtracting the idea of it having a mind that leads to the uncanny valley. Instead of just a one-shot process, it’s a dynamic one.”

The findings have implications for both the design of robots and for understanding how we perceive one another as humans.

“Robots are increasingly entering the social domain for everything from education to healthcare,” Wang says. “How we perceive them and relate to them is important both from the standpoint of engineers and psychologists.”

“At the core of this research is the question of what we perceive when we look at a face,” adds Philippe Rochat, Emory professor of psychology and senior author of the study. “It’s probably one of the most important questions in psychology. The ability to perceive the minds of others is the foundation of human relationships. ”

The research may help in unraveling the mechanisms involved in mind-blindness — the inability to distinguish between humans and machines — such as in cases of extreme autism or some psychotic disorders, Rochat says.

Co-authors of the study include Yuk Fai Cheong and Daniel Dilks, both associate professors of psychology at Emory.

Anthropomorphizing, or projecting human qualities onto objects, is common. “We often see faces in a cloud for instance,” Wang says. “We also sometimes anthropomorphize machines that we’re trying to understand, like our cars or a computer.”

Naming one’s car or imagining that a cloud is an animated being, however, is not normally associated with an uncanny feeling, Wang notes. That led him to hypothesize that something other than just anthropomorphizing may occur when viewing an android.

To tease apart the potential roles of mind-perception and dehumanization in the uncanny valley phenomenon the researchers conducted experiments focused on the temporal dynamics of the process. Participants were shown three types of images — human faces, mechanical-looking robot faces and android faces that closely resembled humans — and asked to rate each for perceived animacy or “aliveness.” The exposure times of the images were systematically manipulated, within milliseconds, as the participants rated their animacy.
The results showed that perceived animacy decreased significantly as a function of exposure time for android faces but not for mechanical-looking robot or human faces. And in android faces, the perceived animacy drops at between 100 and 500 milliseconds of viewing time. That timing is consistent with previous research showing that people begin to distinguish between human and artificial faces around 400 milliseconds after stimulus onset.

A second set of experiments manipulated both the exposure time and the amount of detail in the images, ranging from a minimal sketch of the features to a fully blurred image. The results showed that removing details from the images of the android faces decreased the perceived animacy along with the perceived uncanniness.

“The whole process is complicated but it happens within the blink of an eye,” Wang says. “Our results suggest that at first sight we anthropomorphize an android, but within milliseconds we detect deviations and dehumanize it. And that drop in perceived animacy likely contributes to the uncanny feeling.”

The post Why human-like robots elicit uncanny feelings appeared first on Artificial Intelligence.

Google Assistant has introduced a new Read It feature which makes long reads much easier. The feature will especially come handy for those who may have difficulty seeing or reading texts on a small screen and lean towards listening to podcasts.

Google is beginning to roll out the Read It feature starting this month and the company says that the feature will help users listen to long web pages with the help of Google Assistant. This means that users will be able to instruct the AI-powered virtual assistant to read out a 2000-word article to you while you are busy with your work or any other activity.

Here’s how you can enable this feature on Android phones

Follow these simple steps to access the Read It feature on your device:

Step 1: Download Google Assistant from the Play Store if you haven’t already.

Step 2: Once you launch the app, simply utter these four words – “Google Assistant, Read It.” once on a webpage.

As you instruct Google Assistant to read the particular page, the app will start reading it out.

Google Assistant also highlights the text and auto scrolls the page once it starts reading the text, helping users understand how far along they have come within that web page.

The new Read It feature will support 42 languages

The Read It feature will also prove to be quite beneficial for those who do not follow a certain language on web pages and even people suffering from any kind of visual impairment. This is especially because Google Assistant will also allow users with the ability to translate web pages into 42 different languages. It is also worth mentioning that out of the 42 language options, 11 are Indian languages, although the Read It feature only supports English, as of now, and support for other languages will soon be enabled by the company.

The post Google Assistant Rolls Out ‘Read It’ Feature To Read The Entire Web Page For Android Users appeared first on Artificial Intelligence.

As you already know-how Machine learning is playing a crucial role in predicting your upcoming actions. Now it’s time to see how Machine learning is revolutionizing Mobile app development.

What is Machine learning?

Machine learning is the application of Artificial Intelligence which makes computers predict the outcomes automatically without the intervention of human beings.

MACHINE LEARNING ALGORITHMS

Commonly three types of Machine learning algorithms are available:

A supervised Machine Learning algorithm

In Supervised Machine learning, you are having both input and output variable and then the algorithm is used there in order to predict the output variable.

Unsupervised Machine Learning

In unsupervised machine learning, only the input variable is available instead of an output variable. In Unsupervised Machine learning, data is divided into groups in order to get more information.

As per the research conducted by bcc research, the global machine learning market totaled $1.4 billion in 2017 and is estimated to reach $8.8 billion by 2022. Machine learning vs Artificial intelligence also a most debated topic for data analysts.

Le’s have a look at some of the top machine learning applications

Netflix

One of the most famous examples of Machine learning mobile app is Netflix. And in the present age, everyone is aware of that.

The reason behind this is that you want to watch each and everything before the time you think.

This is not magic, but if we talk about some years ago, this might be considered as magic. But this is not at all magic and the ruth behind this is just a Machine learning.

Netflix has covered its journey from a DVD rental website to a global streaming service. And this has become possible only with the help of Machine learning.

Various Machine learning algorithms are used in Netflix such as Linear regression, Logistic regression. These algorithms of Machine learning help Netflix to suggest personalized recommendations.

Netflix’s content is classified by genre, actors, reviews, length, year and more. All these data go into machine learning algorithms.

Tinder

Every youngster is aware of this word Tinder that it is a dating app that helps in finding a soulmate. Tinder helps in finding a perfect match and this becomes possible only with the help of Machine learning. See how

Machine learning helps in making smart photos of the users which increases the chances of finding a true and perfect match. Tinder uses all kinds of love spells and potions, and one of them is machine learning.

But how is it possible?

Machine learning in tinder helps in showing a random order of your profile photos to people and analyzes how often they’re swiped right or left. With the help of this knowledge, it allows Tinder to manage your profile and put the most popular photos on the top of the profile. With the passage of time, it helps constantly in improving your profile better and better.

And finally, you’ll get better results and find your soulmate in no time.

Snapchat

This is one of the most popular platforms where machine learning is playing its important role by giving recommendations to the users.

There could be seen a fantastic combination of augmented reality and machine learning algorithms in Snapchat for the computer versions.

So how do Snapchat filters work?

First of all, Snapchat detects a face. Then the software sees a photo as a set of data for analyzing each part of the face. But the query is that, How does it become possible for the Snapchat to determine which part of the image is facing?

Well, the software analyzes this difference by looking and between the dark and the light pars of the image individually. The software goes on scanning the photo again and again and it helps the software to detect the difference between grayscale pixel values underneath the white boxes and the black boxes and finally, this helps in detecting which part of the image is a face.

Let’s take an example, we can detect the shape of the face by keeping in consideration these facts: the bridge of the nose is usually lighter than the surrounding area on both sides, the eye sockets are darker than the forehead, and the middle of the forehead is lighter than its sides. This type of algorithm will not work in the case if you tilt the face slightly but it’s really accurate for the front face.

Face recognition Snapchat

Apart from detecting a simple face, if you want to apply a crown in the Snapchat, the app needs to do more than detect a face. There is a need for locating facial features.

Final words

So, these are some of the mobile applications where machine learning is playing an important role. The above-given examples could properly meet you with the importance of Machine learning in Mobile application development.

So, if you are also looking for developing Machine learning-based apps, then hire Python developers who can make such kinds of apps comfortably.

The post How to implement Machine learning in an Android app? appeared first on Artificial Intelligence.

Familiarity breeding contempt hits home in the results of a new study into the security threat from apps on Google Play. The research, conducted by the University of Sydney and CSIRO’s Data61, has unearthed thousands of dangerous apps hiding in plain sight in the online store, tricking users by mimicking popular alternatives. The study used artificial intelligence to identify likely counterfeits, before testing them for malware and other vulnerabilities.

The study deployed a neural network to examine both the design of icons and the wording in descriptions, reviewing “1.2 million apps” to identify “potential counterfeits for the top 10,000 apps.” It found “2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top 10,000 popular apps in the Google Play Store.” The research also found “1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third-party advertisement libraries.”

The use of pre-trained AI algorithms to evaluate style and content “outperforms many baseline image retrieval methods for the task of detecting visually similar app icons,” and on the large dataset of more than1.2 million app icons, the study’s methods achieve “8%-12% higher precision” than alternatives.

“Many counterfeits can be identified once installed,” the authors explain, “however even a tech-savvy user may struggle to detect them before installation,” thus the idea to try the “novel approach of combining content embeddings and style embeddings generated from pre-trained convolutional neural networks to detect counterfeit apps.”

The study found that the 2,040 most dangerous counterfeits “were marked by at least five commercial antivirus tools as malware,” although, encouragingly, 6-10 months since we discovered the apps, 27%-46% of the potential counterfeits we identified are not available in Google Play Store, potentially removed due to customer complaints.”

None of this should come as a surprise-the insecurity of apps on both Android and iOS has been very much in the headlines recently.

Last year, Buzzfeed News reported that “eight apps with a total of more than 2 billion downloads in the Google Play store have been exploiting user permissions as part of an ad fraud scheme that could have stolen millions of dollars.” All eight apps were Chinese in origin, with seven from a single developer, Cheetah Mobile. “The companies claim more than 700 million active users per month for their mobile apps.”

And this month alone, Davey Winder reported for Forbes on the threat from mobile applications, leaving “iPhone and iPad users not as secure as they might imagine, [with] their personal data at risk.” ZDNet has reported that “three-quarters of mobile applications have vulnerabilities relating to insecure data storage, leaving both Android and Apple iOS users open to cyber attacks.” And TechCrunch has reported on vulnerabilities even in U.S. mobile banking apps.

Smartphone users cannot claim that they’re not being warned.

Both Google and Apple continue to fight the good fight to keep their ecosystems secure, and on Android Google Play Protect has been designed to guard against just such vulnerabilities. Google has also said that “in 2018, we introduced a series of new policies to protect users from new abuse trends, detected and removed malicious developers faster, and stopped more malicious apps from entering the Google Play Store than ever before. The number of rejected app submissions increased by more than 55%, and we increased app suspensions by more than 66%.”

The use of AI to moderate content and promote internet safety has been catapulted into the news by social media’s woes in the last 12 months. Projects like Google’s Jigsaw are a sign of things to come. This study is a start on applying the same thinking to a different realm, but one that struggles with the same issues of scale and user naivety.

Ultimately, there’s no substitute for common sense and treating apps from unknown sources as potential threats. And that means checking carefully, not clicking casually. We carry all of the most valuable and private information we have on our smartphones, and we gladly give those devices access to the cloud storage where we store the rest. Our phones know where we live and work, and where we bank and spend. That’s worth remembering before inviting strangers into our virtual homes and giving them permission to roam around simply because they ask nicely.

The post Android Warning: Thousands Of Dangerous Copycat Apps On Google Play, Study Finds appeared first on Artificial Intelligence.

 All too often, we search for an app and end up finding what looks to be the best fit for our needs. But that is until one sees the long list of permissions the application thinks it needs to function. Some developers tend to call for permissions for functionality that their app clearly does not need, like an expense tracker needing the RECORD_AUDIO permission, indicating a high possibility of a nefarious motive.

Google does realize that many such applications plague the Google Play Store. While the technologically adept users may keep a close eye on the permissions they grant to any app, the normal user usually just presses on “Accept” till they reach their end result. It then becomes Google’s “responsibility” to figure out a solution that protects the users from such intrusive applications while devising a solution that scales across the entirety of the Google Play Store and all future uploads.

Google’s approach to fighting this problem involves the application of machine learning to scale its solution. Google begins by analyzing privacy and security signals for each app in Google Play, and then compares that app to its functional peers i.e. other apps with similar features. Functional peers help set the baseline of behavior expected out of that group and apps belonging to this group that exceed the boundary of expected behaviors are then easier to identify. For example, a coloring book app does not need to have access to a user’s precise location, and this need can be established by analyzing other coloring book apps. Similarly, a navigation app does need precise location, and looking at other navigation apps would illustrate that the need for location permissions is within expected and accepted behavior.

Google utilizes machine learning to create these peer groups, letting it look beyond other methods like manual curation and fixed categories, methods that have their own drawbacks. Google’s approach uses “deep learning of vector embeddings to identify peer groups of apps with similar functionality”. This uses app metadata such as text descriptions and user metrics like number of installs. Once the peer groups are established, anomalous behaviors are identified  for potentially harmful signals related to privacy and security from each app’s requested permissions and behaviors. The correlation between different peer groups and their security signals helps different teams at Google decide which apps to promote and determine which apps deserve a more careful look.

The results are also used to help app developers improve the privacy and security of their own apps, though Google did not expand on how exactly this is done, or how exactly it segregates apps that have an honest oversight in its security from apps that have malafide intentions. Perhaps this part of the process is done after manual inspection of these apps by Google’s security and privacy experts, but we are left to draw Google’s word on this.

Nonetheless, it is good to see Google tackling the issues of permission at the store level as well, with a solution that works best for the scale of the Android ecosystem.

The post Google Details its Use of Machine Learning to Identify Intrusive Mobile Apps appeared first on Artificial Intelligence.