In August, 1918, Virginia Woolf spent a quiet stretch at Asheham, the country house that she and her husband, Leonard, rented in rural Sussex. "We've been practically alone, which has a very spiritual effect upon the mind," Woolf wrote to a friend, the socialite Lady Ottoline Morrell. After six months spent in such isolation, Woolf quipped, "I should be a kind of Saint, and Leonard an undoubted prophet. We should shed virtue on people as we walked along the roads." Alas, any pretensions to holiness had been dispelled by the arrival of house guests the previous evening: "I had such a bath of the flesh that I am far from unspotted this morning.

Roe v Wade has now been overturned by the US Supreme Court, removing the country-wide right to an abortion. This will affect access to safe abortions for millions of people in the US . The prospect had already raised questions about the privacy of period-tracking apps. Some apps share data with third parties for advertising or research purposes, causing concern this data could be used as evidence against anyone seeking or obtaining an abortion in states that now outlaw the procedure. Period-tracking apps vary in scope.

The recent leak of a draft opinion from the US Supreme Court suggests that Roe v Wade could be overturned, eliminating the country-wide right to an abortion. The prospect has re-raised questions about the privacy of period-tracking apps. Some apps share data with third parties for advertising or research purposes, causing concern this data could be used as evidence against anyone seeking or obtaining an abortion in states that outlaw the procedure should Roe v Wade be overturned. Period-tracking apps vary in scope. In some, people record simple details, like when their period begins and ends, and the app the makes predictions about when their period will arrive in future and when they are ovulating.

The U.S. Food and Drug Administration recently partnered with Health Canada and the UK's Medicines and Healthcare products Regulatory Agency to issue guiding principles to align efforts and standards for artificial intelligence and machine learning medical device development in health care. "The FDA believes that artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day," said Jim McKinney, public affairs specialist at the FDA, in an email to The Well News. McKinney said the 10 guiding principles grew out of collaborative discussions with Health Canada and MHRA, and learning from several sectors that applied AI and ML technologies for years and have developed good practices that can be readily applied to the medical device industry. Evidence from published information, expert and other public perspectives and review experience was used to develop the guiding principles that will be used by the agency to lay the foundation for the development of Good Machine Learning Practice, which will unify international efforts for medical device development. Over the past decade the FDA has reviewed and authorized a growing number of devices legally marketed with machine learning and expects this trend to continue.

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Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Detroit-area man upset that his parents asked him to turn off his video game while in their bedroom last week so they could go to sleep flew into a rage and stabbed them, with his stepdad succumbing to his injuries days later, authorities said. Christopher McKinney, 29, of Madison Heights, faces several charges related to the Dec. 11 incident, including assault with intent to murder and obstructing and resisting a police officer, according to Oakland County jail records. The charges could be upgraded following his stepfather's death from his injuries four days later.

Note on style - this initial blog post will be divided into three sections. In the first I will review what I understand to be the key points from the first chapter of the fastai book. In the second I will mention how, following the authors’ instructions, I setup a ‘workspace’ for DL. And finally I’ll discuss some open questions I have. Key points from Chapter 1 With this chapter Howard and Gugger provide a useful overview of the subject of Deep Learning, some extremely useful tips on how to setup a development environment for DL coding and analysis (more on that below) and provide a summary of their approach to teaching DL. The authors make great efforts to make the subject approachable, emphasising that neither advanced qualifications nor high level coding ability are necessary to implement Deep Learning techniques. Indeed, they say right from the beginning that they intend to give readers a sense of ‘the complete game’. I can say from experience of other courses or books in this area that this is a refreshingly different approach. They also provide a good overview of the history of the discipline - from McCulloch and Pitts’ notion of an artificial neuron to more contemporary concepts such as Parallel Distributed Processes (PDP). For me the most interesting topic is the difference between Machine Learning and more traditional forms of programming. From what I understand, traditional programming is based on the notion that inputs from the user will go through a function (defined by the programmer) and specific output(s) will be the result. This approach works well when, for example, we want to automate repetitive tasks. However it is not suitable for more complex or conceptual tasks, such as recognising the difference between a cat or dog, imitating a particular author’s writing style or making a good movie recommendation. The reason traditional approaches don’t work here is that a programmer would need to specify every single aspect relevant to the task. A far better approach then is one were the machine itself can ‘learn’ i.e. the programme (or model) has a process inherent to itself that enables it to output a result that is intelligible and accurate to humans. To make this clearer, I briefly touch on the solution that ultimately caught on, which was conceived by Arthur Samuel and called Machine Learning. ML essentially involves taking data, weighting it in some way (through labelling the data for instance) and training the programme (now referred to as a model) to recognise patterns within that data. This process will repeat, with the weights adjusting through each cycle, until the programmer considers the programme sufficiently accurate. Interestingly once the model is trained, it can be used in the manner of a traditional programme. That means novel data can be introduced, without weighting, and the model will then make predictions - again, for example, whether a picture shows a cat or dog. While this training process is based on repetition, training to frequently on the same data set will actually decrease accuracy. This is a situation known as over-fitting where the model makes predications to close to its training data. My summary here may sound very theoretical but Howard and Gugger present their account in quite a practical fashion with lots of coding and real life examples. Setting up a DL coding environment One surprising element of this course is the great advice that Howard and Gugger offer on how to set up your working environment for DL projects. In fact, this may have been the feature that ultimately persuaded me to work through the entire course. The best thing to do is check their site for the details link but just to give a brief summary of what they describe: Haward and Gugger have developed a framework called fastai that enables users to access DL techniques in PyTorch in a much more straightforward manner than is possible than through coding for PyTorch directly. (I’ve not personally used PyTorch but this is my understanding of what fastai does.) One consequence of this is that a GPU is required for fastai to function. I do not have a GPU in my rather cheap Lenovo laptop but not to worry as the authors provide a very useful, and thorough, guide on how to set up a cloud system to run the course exercises, which are written in standard Jupyter Notebooks (although Google Colab versions are also available). I would emphasise that this is in no way an intimidating or difficult process, in fact I’m almost stunned by how easy it is to set up a cloud computer. Personally I’m using a free service called Gradient which is offered by a company known as Paperspace. While there are some restrictions (such as the cloud system shutting down automatically after 6 hours) Paperspace have integrated fastai’s Jupyter Notebooks into their service, which means you can jump right in once you’ve set it up. The only complaint I have, and it’s very minor, is that for Paperspace do give you the option of running the course notebooks on systems without GPUs, which makes no sense since the notebooks won’t work without GPUs. Also a few times, when I’ve started the virtual machine, it seems to have automatically selected the CPU system. Anyway I certainly can’t complain, this is a great service and I think Paperspace should be given some credit for making DL this accessible for free to basically anyone. As a side note, another extremely useful discussion on setting up your coding environment can be found in Wes McKinney’s Python for Data Analysis. McKinney’s text focuses far more on the mechanics of data analysis, personally I see it more as a reference book than something I would read cover to cover. Nevertheless the opening chapter were he discusses the basics modules required for doing data analysis in Python is something I return to every time I setup a new PC. Open questions One point I’m not entirely clear on is how the theoretical notion of artificial neurons can produce intelligible results. Having done some other more mathematical focused courses on this subject I believe the reason that artificial neurons, in particular through layering, can do this is to with the fact they can be thought of as strings of matrix multiplications. This allows the system to compute linear algebra, which enable the computer to predict an answer. I’m not completely happy with my own explanation here. I do think at some point I will have to do a deep dive on the Maths but for now this is my understanding.

It makes sense for large technology companies like Google and Microsoft to open source AI and machine learning solutions because they have overlapping vertical interests in providing vast cloud services. These come into play when a certain machine learning library becomes popular and users deploy it on the cloud and so forth. It is less clear why financial services companies, which play a much more directly correlated zero sum game, would open up code that they paid the engineering team to create. It's interesting that hedge funds, traditionally thought to be the most secretive of financial institutions, have been proactive in pushing an open source software agenda. AQR Capital Management was probably patient zero when it came to opening up their code around data storage – and this move, shepherded by software engineer Wes McKinney, kickstarted the popular Pandas libraries project.