A team of scientists at the Max Planck Society recently published astounding new research indicating they've pushed the field of "organoids," to a bold new frontier. Up front: Organoids are synthetic biological constructs that mimic human organs to various degrees. Scientists grow organoids by programming stem cell clusters. Essentially, they feed the clusters the necessary components to spark their growth, and then they employ a series of limiters to guide them into becoming the kind of organoid we want. The ultimate hope is that we'll be able to grow organ analogs -- synthetic liver or brain organoids, for example -- that we can use to advance our scientific and medical knowledge.

But artificial intelligence is gradually becoming a content marketer's best friend – or at least more than an acquaintance. It may turn into a love story yet and I believe we're in for a "happily ever after" scenario. If you don't agree to the romance, I hear you. The use of AI in marketing shows a 186% YoY growth. Statistics show only 29% of marketing leaders used AI in 2018; by 2020, it was 84%.

AI-powered synthetic brains will allow humans to operate 500 versions of themselves at once, according to the man behind Amazon's voice assistant. Igor Jablokov believes artificial intelligence will become so advanced we will be unable to distinguish between a real or "synthetic" mind. The CEO of Pryon previously founded Yap, a fully-automated cloud platform for voice recognition, which was snapped up by Amazon before being used for the popular Alexa. The device uses a non-human voice to communicate with users, but Igor warns such technology could change with terrifying consequences. He told the Financial Times: "People will not be able to tell if they are interacting with you or your AI proxy. "Right now, you could be doing two interviews at once.

Since ancient times, humans have been interested in finding systematic approaches to reasoning and logical thinking. Now, we want to make machines "think" like us and endow them with the reasoning ability that, unfortunately, we don't quite understand ourselves. But, why do we need machines that can deconstruct truths and validate reasons like we do? One of our most recent AI-related posts discusses the story of an AI system that can detect skin cancer more accurately than dermatologists. No doubt, this is big a deal in that an early diagnosis is one of the most effective methods for providing successful cancer treatments.

The IEEE Spectrum this month has a story on synthetic brains. In this article I will review the story and comment on the status of the quest: replicating the human brain in synthetic systems. This article is about neuroscience, neuromorphic, artificial neural networks, deep learning, computing hardware in biology and synthetic, and how all of these come together in the the human grand challenge of creating a synthetic brain at or above human level. Why We Should Copy the Brain: we should do this because we want to create intelligent machines that can do the work for us. In order to do our work, machine will have to live in our environment, have senses similar to our own, and be able to accomplish the same kind of tasks. It does not stop here: machine can do more and better than we can most tasks, simply as we do better than other life forms. And we would like them to do things we cannot do, and do better the things we can do.

Creating an artificial intelligence similar to the structure of the human brain carries a range of benefits that drastically outweigh any potential drawbacks. Apart from copying a human's ability to think creatively, learn rapidly from inconsistent data, and utilise the evolutionary benefits that many traditional artificial intelligences lack, artificial copies of human brains will also allow us to study brain diseases and disorders without the use of an actual, organic patient. It is most useful for tasks which require visual and auditory signals to complete fully and safely, such as driving a car or holding a realistic face-to-face conversation. A synthetic brain which uses synchronised'spikes' of electricity in a neural network, like an organic one, will be far more able to carry out these tasks than a standard computer. However, this also means that it will suffer the same drawbacks as an organic brain, such as fading memories and a lesser ability to read and utilise data in a logical instead of hierarchal fashion.