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A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noises in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.

Today, a lot of high-profile deep machine learning projects in the cloud are powered by GPUs; specifically NVIDIA GPUs. Even Facebook uses them for its own machine learning work behind-the-scenes. GPUs are able to handle the massive amounts of computing power required to train deep neural networks that facilitate these projects. But Huawei deputy chairman and rotating CEO Eric Xu believes the future of machine learning lies in dedicated processors. Read on to find out more. In the past few years, NVIDIA has made a big push to dethrone CPUs in the deep machine learning space.

Apple's Siri upgrades will let you dictate LinkedIn messages, verbally search for shoe photos on Pinterest, and hail an Uber, hands-free. When Apple unveils its new iPhone Wednesday, it's expected to push hard on software improvements available for its next mobile operating system, iOS 10, available for current iPhones and a predicted new iPhone model. The iPhone's voice-activated digital assistant Siri will be a key part of this upgrade, say tech analysts and developers. Siri, primarily used for voice commands to dial phone numbers, set reminders and get traffic information, has been opened to third-party developers after five years of existence. Apple, via a spokeswoman, said app developers are creating "completely new ways for users to interact" within apps.

Today on In Case You Missed It: Google's Tensor Flow machine learning technology helped create a device to sort through massive amounts of cucumbers at a farm in Japan, sorting the vegetables by quality grade so that humans don't have to do it manually. Meanwhile, an Australian scientist created an ink that changes colors when exposed to sunlight, which could theoretically help people from getting a sunburn. We also touched on the new internet-connected pet toy from Acer and rounded up the biggest headlines of the week for you in TL;DR. Be sure to check out IBM Watson's movie trailer and read about SpaceX's rocket explosion. As always, please share any interesting tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

A computer's victory over a human go master this past March reminds us of the pending "singularity" -- the rapidly approaching moment in time when artificial intelligence overtakes human intelligence. Machines will learn, and we won't be their teachers. Are we prepared for it? Can we prepare for it? Many futurists declare it inevitable, probably within a generation, maybe less.

US Transportation Secretary Anthony Foxx delivers an announcement in Washington, DC, in 2014. Automakers and ride-hail companies are racing to put self-driving cars on the road. In a few weeks, Uber passengers in Pittsburgh will be able to hail self-driving Volvos. Last month, Tesla announced its hopes to build an autonomous ride-hailing fleet. And this month, Ford said it plans to mass-produce autonomous vehicles by 2021.

Technology has greatly affected our lives over the years. Now, a study from Stanford University has revealed how artificial intelligence (A.I.) may change people's lifestyle -- from transportation, employment, health and household chores -- by the year 2030. Machine learning is a subfield of computer science, that grew out of the quest for artificial intelligence. It is so pervasive in today's world that you probably use it often in daily life, without even realising it. Machine learning has given us self-driving cars, effective web search, recommendations that you get when you visit web sites or social media sites, face detection in a digital photo album, stock trading etc. Mark Zuckerberg made it his new year's goal to create Jarvis-like artificial intelligence for his home -- at this point it lets him manage his house and toasts his bread.

Machine learning is a subfield of computer science, that grew out of the quest for artificial intelligence. It is so pervasive in today's world that you probably use it often in daily life, without even realising it. Machine learning has given us self-driving cars, effective web search, recommendations that you get when you visit web sites or social media sites, face detection in a digital photo album, stock trading etc. Machine learning enables computers to analyze vast amounts of data and automatically detect patterns and features, or make predictions regarding certain conditions. In a dynamic disease like cancer, gauging and diagnosing such a complex heterogeneity is the biggest challenge. After decades of cancer research, it has become increasingly clear that no two patients' cancers are exactly the same, and even within one person's tumor there is a wild diversity of cells.

Artificial intelligence is very interesting topic of research for many modern scientists. The concept of machine intelligence is really fascinating. It gives human a power to design something that can live on its own. The AI technology has become really advanced and its only matter of time when the machines will be able to learn almost anything. The machine learning algorithms are already very smart, however the processing power has been a challenge in last decade.