Saving your trained machine learning models is an important step in the machine learning workflow: it permits you to reuse them in the future. For instance, it's highly likely you'll have to compare models to determine the champion model to take into production -- saving the models when they are trained makes this process easier. The alternative would be to train the model each time it needs to be used, which can significantly affect productivity, especially if the model takes a long time to train. In this post, we will cover 5 different ways you can save your trained models. Pickle is one of the most popular ways to serialize objects in Python; You can use Pickle to serialize your trained machine learning model and save it to a file. At a later time or in another script, you can deserialize the file to access the trained model and use it to make predictions.

A futurology report predicts that SMEs will be run by lookalike avatars of their business owners by 2050 – here's what they'll be able to do UK small businesses will be run by avatars in time for 2050, according to research. The futurology report by Virgin Money says that this will come about thanks to advances in technology in the coming decades. UK futurologist, Dave Coplin, said that these virtual smart clones will be driven by artificial intelligence and machine learning. They'll be able to handle the workload while business owners focus on building their business and achieving a better work-life balance. Avatars will be programmed by business leaders as their persona and they will be fully independent with the ability to operate and make decisions on the person's behalf.

Most developers who grapple with big data are data engineers, data scientists, or machine learning engineers. This book is aimed at those professionals who are looking to use Spark to scale their applications to handle massive amounts of data. In particular, data engineers will learn how to use Spark's Structured APIs to perform complex data exploration and analysis on both batch and streaming data; use Spark SQL for interactive queries; use Spark's built-in and external data sources to read, refine, and write data in different file formats as part of their extract, transform, and load (ETL) tasks; and build reliable data lakes with Spark and the open source Delta Lake table format. For data scientists and machine learning engineers, Spark's MLlib library offers many common algorithms to build distributed machine learning models. We will cover how to build pipelines with MLlib, best practices for distributed machine learning, how to use Spark to scale single-node models, and how to manage and deploy these models using the open source library MLflow.

Artificial intelligence researchers are doubling down on the concept that we will see artificial general intelligence (AGI) -- that's AI that can accomplish anything humans can, and probably many we can't -- within our lifetimes. Responding to a pessimistic op-ed published by TheNextWeb columnist Tristan Greene, Google DeepMind lead researcher Dr. Nando de Freitas boldly declared that "the game is over" and that as we scale AI, so too will we approach AGI. Greene's original column made the relatively mainstream case that, in spite of impressive advances in machine learning over the past few decades, there's no way we're gonna see human-level artificial intelligence within our lifetimes. But it appears that de Freitas, like OpenAI Chief Scientist Ilya Sutskever, believes otherwise. "Solving these scaling challenges is what will deliver AGI," the DeepMind researcher tweeted, later adding that Sutskever "is right" to claim, quite controversially, that some neural networks may already by "slightly conscious."

In the second of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in France. What is France's strategy for Artificial Intelligence? The French president, Emmanuel Macron, announced in March 2018 his ambition for France to become a global leader of the artificial intelligence (AI) ecosystem. The first phase of the National Programme included an initial investment of €1.5 billion into the creation of a network of interdisciplinary institutes dedicated to artificial intelligence (the "3IA" institutes) and the financing of multiple AI projects overseen by Bpifrance. The second phase will provide for €2 billion of private and public funding to attract and train new talent.

How can a blood clot be removed from the brain without any major surgical intervention? How can a drug be delivered precisely into a diseased organ that is difficult to reach? Those are just two examples of the countless innovations envisioned by the researchers in the field of medical microrobotics. Tiny robots promise to fundamentally change future medical treatments: one day, they could move through patient's vasculature to eliminate malignancies, fight infections or provide precise diagnostic information entirely noninvasively. In principle, so the researchers argue, the circulatory system might serve as an ideal delivery route for the microrobots, since it reaches all organs and tissues in the body.

US AI guidelines are everything the EU's AI Act is not: voluntary, non-prescriptive and focused on changing the culture of tech companies. As the EU's Artificial Intelligence (AI) Act fights its way through multiple rounds of revisions at the hands of MEPs, in the US a little-known organisation is quietly working up its own guidelines to help channel the development of such a promising and yet perilous technology. In March, the Maryland-based National Institute of Standards and Technology (NIST) released a first draft of its AI Risk Management Framework, which sets out a very different vision from the EU. The work is being led by Elham Tabassi, a computer vision researcher who joined the organisation just over 20 years ago. Then, "We built [AI] systems just because we could," she said.

Apple's long-awaited Apple Car could have virtual displays on the inside instead of clear windows, according to a new patent. The tech giant has filed a patent for a virtual reality (VR) vehicle system that matches up'virtual views' with the physical motion of a car as it's travelling. For example, if the car was careering down a hill, the system could project a virtual image of a rollercoaster ride. Chairs in the car would move about to match the visuals, the patent suggests, much like an immersive '4DX' cinema experience. But it would mean passing views of the real world – such as a beautiful medieval cathedral or stunning coastal hills – would be entirely replaced with virtual graphics.

This article was published as a part of the Data Science Blogathon. As a consequence of the large quantity of data accessible, particularly in the form of photographs and videos, the need for Deep Learning is growing by the day. Many advanced designs have been observed for diverse objectives, but Convolution Neural Network – Deep Learning techniques are the foundation for everything. So that'll be the topic of today's piece. Deep learning is a machine learning and artificial intelligence (AI) area that mimics how people learn.

But, actually, according to Professor Rose Luckin and Karine George, putting AI at the heart of your classroom can lessen that load and make your life as a teacher easier. In this week's Tes Podagogy podcast, Luckin, a professor of learner centred design at UCL's Knowledge Lab, and George, a former headteacher and active research practitioner, discuss their mission to enlighten teachers and leaders on the power of AI in education. The first thing George talks about is workload: teachers could use AI in creating online quizzes for pupils to complete, which give them instant feedback. George and Luckin also suggest that it can help with safeguarding, flagging pupils who need support, and seeking out data patterns, as well as recruitment and how school trips are planned. And actually, Luckin points out that, according to Programme for International Student Assessment data, the UK has one of the highest rates of technology per pupil in the Organisation for Economic Cooperation and Development countries. But, actually, according to Professor Rose Luckin and Karine George, putting AI at the heart of your classroom can lessen that load and make your life as a teacher easier.