Machine learning's (ML) ability to make robots behave like humans was itself a pathbreaking innovation, and now it can read your mind too! The rise in technological innovation has led to countless advancements in various industries. Machine learning is a subset of artificial intelligence. Machine learning can accurately translate thoughts into sentences, at least for a limited vocabulary of 250 words. The ability of machine learning to make machines act like humans - machine learning that enables machines to learn and adapt to their surroundings over time, as well as, deep learning techniques - have left humans stunned. Along with offering great, exciting, and fantastic innovations, now machine learning can read your mind too.

Chinese scientists claim to have developed a wearable robot that can read a person's mind by monitoring brain waves and muscle activity. The machine, according to its creators, was able to "recognize human intention" with 96 percent accuracy. According to the Hong Kong-based newspaper South China Morning Post, the robot was tested at an assembly factory by developers at China Three Gorges University's Intelligent Manufacturing Innovation Technology Centre. According to the developers, the industrial robot not only monitored the worker's brain waves but also collected electric signals from muscles as it worked together to assemble a complex product. According to the developers, the robot was able to read a coworker's brain waves without the latter saying anything and pick up a tool and place it on the workstation. "In modern industrial manufacturing, assembly work accounts for 45 per cent of the total workload, and 20-30 per cent of the total production cost," project lead scientist Dong Yuanfa and his co-researchers said in a paper published in domestic peer-reviewed journal China Mechanical Engineering.

Recruitment has been changing rapidly. At the root of all this change is technology advancement as the ultimate change maker. Some recruiters wonder about the future of recruitment and what role is left for them to fulfil. What if you could articulate yourself without saying a word? The truth is, we already can.

Sign in to report inappropriate content. As part of the NeuroNet NTI Assistive Neurotechnology project, employees of the Neurobotics Group of Companies and the Moscow Institute of Physics and Technology have trained neural networks to recreate images of the electrical activity of the brain. Earlier, no such experiments were performed on EEG material (other scientists used fMRI or analyzed signals directly from neurons). In the future, this discovery will create a new type of device for post-stroke rehabilitation.

Imagine a reality where computers can visualise what you are thinking. In late December, Guohua Shen, Tomoyasu Horikawa, Kei Majima and Yukiyasu Kamitani released the results of their recent research on using artificial intelligence to decode thoughts on the scientific platform, BioRxiv. Machine learning has previously been used to study brain scans (MRIs, or magnetic resonance imaging) and generate visualisations of what a person is thinking when referring to simple, binary images like black and white letters or simple geographic shapes (as shown in Figure 2 here). But the scientists from Kyoto developed new techniques of "decoding" thoughts using deep neural networks (artificial intelligence). The new technique allows the scientists to decode more sophisticated "hierarchical" images, which have multiple layers of colour and structure, like a picture of a bird or a man wearing a cowboy hat, for example.

Imagine a reality where computers can visualize what you are thinking. In late December, Guohua Shen, Tomoyasu Horikawa, Kei Majima and Yukiyasu Kamitani released the results of their recent research on using artificial intelligence to decode thoughts on the scientific platform, BioRxiv. Machine learning has previously been used to study brain scans (MRIs, or magnetic resonance imaging) and generate visualizations of what a person is thinking when referring to simple, binary images like black and white letters or simple geographic shapes (as shown in Figure 2 here). But the scientists from Kyoto developed new techniques of "decoding" thoughts using deep neural networks (artificial intelligence). The new technique allows the scientists to decode more sophisticated "hierarchical" images, which have multiple layers of color and structure, like a picture of a bird or a man wearing a cowboy hat, for example.

Whether it's using AI to help organize a Lego collection or relying on an algorithm to protect our cities, deep learning neural networks seemingly become more impressive and complex each day. Now, however, some scientists are pushing the capabilities of these algorithms to a whole new level - they're trying to use them to read minds. By reverse-engineering signals sent by the brain, researchers at Carnegie Mellon University have been working on an AI that can read complex thoughts simply by looking at brain scans. Using data collected from a functional magnetic resonance imaging (fMRI) machine, the CMU scientists feed that data into their machine learning algorithms, which then locate the building blocks that the brain uses to create complex thoughts. Impressively, the study showed that the team were able to demonstrate where and how the brain was being triggered while processing 240 complex events, covering everything from individuals to places and even various physical actions or aspects of social interaction. It's by understanding these triggers that the algorithm can use the brain scans to predict what is being thought about at the time, connecting these thoughts into a coherent sentence.

Most English dictionaries list words alphabetically, but how do we store them in our head? Finding out could have an unexpected pay-off: being able to tell what someone is thinking from their brain activity. Although neuroscientists can already do this to a limited extent, the brain's internal filing system for words and concepts – an important step towards accurately reading a person's thoughts – remains murky. Now Jack Gallant at the University of California, Berkeley, and his team have charted the "semantic system" of the human brain. The resulting map reveals that we organise words according to their deeper meaning, in subcategories based around numbers, places, and other common themes. Previous "mind-reading" studies have shown that certain parts of the brain respond to particular words.