Sleep is a complicated biological process and plays an essential role in health. Sleep occurs in cycle and involves different sleep stages, helping restore functions of body and mind, such as immune, nervous, skeletal, and muscular systems [1]. Unhealthy lifestyles and work-related stress may lead to sleep disturbances, which has become one of the serious issues in modern societies. Sleep disorders not only cause a reduction in physical performance during the day, but have negative effects on cognitive functions [2]. Moreover, some psychological and neurological diseases can also deteriorate normal sleep patterns [3].

Deep learning is used in agriculture for several tasks such as quality assessment of crop and vegetation, autonomous fruit picking, and the classification and detection of different species. We will focus on classification in this webinar where we will learn to utilise the capability of a deep learning model to automate identification of flowers. From preparing the images to training and evaluating an existing deep neural network, we will explore how efficiently MATLAB handles these assignments. Besides exploring the features of deep neural network, we will deploy the newly trained model in MATLAB to classify more flower images.

Spell, founded by Serkan Piantino, is making machine learning as easy as ABC. Piantino, CEO of the New York-based startup and former director of engineering for Facebook AI Research, explained to AI Podcast host Noah Kravitz how he's bringing compute power to those that don't have easy access to GPU clusters. Spell provides access to hardware as well as a software interface that accelerates execution. Piantino reported that a wide variety of industries has shown interest in Spell, from healthcare to retail, as well as researchers and academia. "You know there's some upfront cost to running an experiment, but if you get that cost down low enough, it disappears mentally" -- Serkan Piantino [11:52] "Providing access to hardware and making things easier -- giving everybody the same sort of beautiful compute cluster that giant research organizations work on -- was a really powerful idea" -- Serkan Piantino [18:36] Deep learning icon and NVIDIA Chief Scientist Bill Dally reflects on his career in AI and offers insight into the AI revolution made possible by GPU-driven deep learning.

This would be a typical design of a \(ConvNet \). All of the work and designing a convolutional neural net is selecting its \(hyperparameters \): deciding what's the filter size, what's the stride, what's the padding and how many filters to use. We will give some suggestions later and some guidelines for how to make these choices. At the moment, one thing to take away from this is that as we go deeper in the neural network, typically we start off with larger images \(39\times 39 \), and then the height and width will stay the same for a while and gradually trend down as we go deeper in the neural network. That is, the size has gone from \(39 \) to \(37 \) to \(17 \) to \(7 \), whereas the number of channels generally increases (from \(3 \) to \(10 \) to \(20 \) to \(40 \)).

Creating agents that resemble the cognitive abilities of the human brain has been one of the most elusive goals of the artificial intelligence(AI) space. Recently, I've been spending time on a couple of scenarios that relate to imagination in deep learning systems which reminded me of a very influential paper Alphabet's subsidiary DeepMind published last year in this subject. Imagination is one of those magical features of the human mind that differentiate us from other species. From the neuroscience standpoint, imagination is the ability of the brain to form images or sensations without any immediate sensorial input. Imagination is a key element of our learning process as it enable us to apply knowledge to specific problems and better plan for future outcomes.

With the recent rise in interest in artificial intelligence for computer vision applications, a lot of attention has been given to the potential benefits that AI can bring – promises of more accurate quality control inspection with fewer false alarms and lower cost. However, when deployed, these goals often aren't met – in fact, 85 per cent of AI projects fail. Some of the reasons behind these numbers are that most of the efforts to deploy AI to date assume the availability of large amounts of data to train the model, ignore the importance of being able to explain how the algorithm arrived at its conclusion, and lack consideration for compute resource requirements. Often, in today's complex manufacturing processes, there aren't enough examples of defects to create an accurate model. Furthermore, and perhaps more importantly, commonly used methods such as deep learning are a black box, only able to provide results but not show the method used to reach a conclusion.

Machine learning--combined with radiologists' intuitive, or "gist" processing--is more accurate in assessing breast cancer risk than either approach alone. Deep learning models, such as convolutional neural networks (CNNs), enable automatic screening of life-threatening breast cancers at an earlier, more curable stage. However, CNNs rely on large annotated datasets for clinical diagnosis as input. Gist is the memory representation of the bottom-line meaning of an experience. Gist-based intuition is a human visual ability, an advanced form of reasoning that is mainly unconscious and develops with experience.

The Matthias Mann lab at the Max Planck Institute of Biochemistry is a leader in the field of mass spectrometry-based proteomics and has pushed the development and application of this technology for over two decades. The Fabian Theis lab at the Helmholtz Center Munich has a long-standing reputation for pioneering machine learning and AI methods in molecular biology, in particular on single-cell genomics and microscopy. They have recently joined forces in a project to develop novel deep learning techniques for peptide analysis and predictions on multiple levels, which potentially revolutionizes proteomic workflows in terms of accuracy and efficiency. Together the Theis and Mann labs are looking for two highly motivated postdoc candidates for working in a team that will combine newest developments in both Machine Learning and proteomics. This technology will be applied to the diagnosis and prognosis of disease on the basis of MS-based proteomics.

AILabPage defines – Artificial neural networks (ANNs) as "Biologically inspired computing code with the number of simple, highly interconnected processing elements for simulating (only an attempt) human brain working & to process information model".

Say you've been driving on the roads of Phoenix, Arizona, all your life, and then you move to New York. Do you need to learn driving all over again? You just have to drive a bit more cautiously and adapt yourself to the new environment. The same can't be said about deep learning algorithms, the cutting edge of artificial intelligence, which are also one of the main components of autonomous driving. Despite having propelled the field of AI forward in recent years, deep learning, and its underlying technology, deep neural networks, suffer from fundamental problems that prevent them from replicating some of the most basic functions of the human brain.