If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Sometimes these notebooks find their way into production, but their code and structure are often far from ideal. In this session, we cover some best practices around creating and operationalising notebooks. We will talk about structure, code style, refactoring in notebooks, unit testing, reproducibility and more. Nikolay Manchev is a machine learning enthusiast and speaker. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks with emphasis on biological plausibility. Nikolay was a Senior Data Scientist and Developer Advocate at IBM [masked]) and currently acts as the Principal Data Scientist for EMEA at Domino Data Lab. Talk 3: Generative Deep Learning - The Key To Unlocking Artificial General Intelligence by David Foster Generative modelling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavours such as painting, writing, and composing music. In this talk, we will cover: - A general introduction to Generative Modelling - A walkthrough of one of the most utilised generative deep learning models - the Variational Autoencoder (VAE) - Examples of state-of-the-art output from Generative Adversarial Networks (GANs) and Transformer based architectures.
I recently made a set of explorable animations that takes an in-depth look at the humble perceptron. Aside from the typically shown linearly separable case, you can also explore convergence with an adjustable margin, as well as the Maxover algorithm and Voted Perceptron variants, which offer good performance, even when the data isn't linearly separable. There was very little information online about the Maxover algorithm, save for the one paragraph on the Wikipedia page. And the paper itself was pretty annoying to read through. I'm pretty glad that I was able to get the messy pseudocode from the paper into something visual you can run in your browser.
If you have a good FPGA compiler, don't worry too much about writing efficient mat-mults or convolutions. The downside of having a good FPGA compiler is that it'll optimise the pants off your code -- once lost three days of productivity chasing down why the compiler wired all my output pins to Vcc or Gnd. The day before, I was testing something on the other end of the pipeline, and changed an auto-incrementing register to increment by zero so I could step-through things. I forgot that I hadn't changed it back. The compiler (correctly) deduced that no matter what the input pins were, the output would always be the same.
Deeptech solves profound challenges by enabling revolutionary solutions in a wide range of domains, impacting our lives, societies and industries at a fundamental level that will reverberate through future generations to come. Together with THINGS, we invite corporates to a matchmaking event where you can meet disruptive startups in areas such as AI, robotics, security, radar, sensors, IoT, new materials, energy and biotechnology. These companies, born from science and engineering discoveries, with their new technology have the potential to create new sources of competitive advantage. By joining Ignite Deeptech, you will have the opportunity to explore the possibilities to collaborate and innovate with these startups and potentially impact society and industry on a massive scale. After the matchmaking, you can join THINGS Executive Summit, starting at 14:00.
Unless you've been living under a rock for the past few years, you will be well aware of the trend towards more conscious consumption. In part recently, this is because of the Greta Thunberg effect, the rise Elon Musk's Tesla electric vehicles and more sustainable forms of transportation such as Bird and Uber JUMP. The future of our planet is very much front of mind for Gen-Z, and our cities (especially our large ones) are some of the most polluted places on earth. Just a few days ago, the UK government announced that the petrol and diesel ban will be brought forward 5 years to 2035 rather than 2040. To add to this, the UK government announced that to accelerate the shift to zero emission cars, all company cars will pay no company car tax in 2020–2021.
A chat-bot is, a robotic self learning and talking bot which imitate human conversation through text chats and voice commands (a good example being Siri or Amazon Alexa). Task Handling Chat-bot where you ask something and it execute that task in more easy manner. For example if you ask to book a table at a restaurant, or open website than it will perform the operation on your mobile, laptop and lands you at the page you ask, order the pizza for you A.I. based chat bots (learn over a period of time using Machine Learning techniques) -- dialog flow is an example of that Chat bots are mostly used for businesses will only increase as time goes by. No programming prior experience is required because Google Dialogflow is the platform where all the Machine learning algorithm get trained in back-end. Go to the Dialogflow Console.
LINK "An overview of near-infrared spectroscopy (NIRS) for the detection of insect pests in stored grains" LINK "A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy" LINK The latest generation of near-infrared (NIR) spectroscopy systems designed for on-line measurement of properties opens up new possibilities for measuring product properties. LINK "In situ ripening stages monitoring of Lamuyo pepper using a new generation NIRS sensor" LINK "Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectra" LINK " Estimation of soil phosphorus availability via visible and near-infrared spectroscopy" LINK "Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy." LINK "Detection of Dibutyl Phthalate (DBP) Content in Liquor Based on Near Infrared Technology" LINK "Analysis of incensole acetate in Boswellia species by near infrared ...
Hopsworks 1.x series brings many new features and improvements, ranging from services such as the Feature Store and Experiments, to enhanced support for distributed stream processing and analytics with Apache Flink and Apache Beam, to building Deep Learning pipelines with TensorFlow Extended (TFX), to code versioning support for Jupyter notebooks with Git, to all-new provenance/lineage of data across all steps of a data engineering and data science. We are also excited that Hopsworks 1.x is the back-bone of the all new Managed Hopsworks platform for AWS, Hopsworks.ai Hopsworks 1.x brings significant Feature Store improvements ranging from updated UI components to connectivity with external systems and feature discovery. Users of Hopsworks Enterprise can now easily connect to the Feature Store from their Databricks notebooks and Amazon Sagemaker. Documentation for connecting with these two platforms can be found at hopsworks.readthedocs.io