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Category Archives: Machine Learning
Python, the general-purpose coding language has gained much popularity over the years. Speaking of web development, app designing, scientific computing or machine learning, Python has it all. Due to this favourability of Python in the market, python developers are also in high demand. They are required to be competent and out of the box thinkers- undoubtedly a race to win.
Are you one of those python developers? Do you find yourself lagging behind in proving your reliability? Maybe you are going wrong with some of your
skills. Never mind!
Im here to tell you of those 8 trendsetting skills you need to hone. Implement them and prove your expertise in the programming world. Come, lets take a look!
Being able to use the Python Library in its full potential also decides your expertise with this programming language. Python libraries like Panda, Matplotlib, Requests, Pyglet and more consist of reusable codes that youd wish to add to your programs. These libraries are boon to you as a developer. They will increase workflow and make task execution way easier. Nothing saves more time from having to write the whole code every time.
You might know how Python omits repeated code by using pre-developed frameworks. As a developer using a Python framework, you typically write code which conforms to some kind of conventions. Because of which it becomes easy to delegate responsibilities for the communications, infrastructure and low-level stuff to the framework. You can, therefore, concentrate on the logic of the application in your own code. If you have a good knack of these Python frameworks it can be a blessing, as it allows smooth flow of development. You may not know them all, but its advisable to keep up with some popular ones like Flask, Django and CherryPy.
Not sure of Python frameworks? You can seek help from Python Training Courses.
Object-relational mapping (ORM) is a programming method used to access a database. It exposes your database into a series of objects without writing commands to insert or retrieve data. It may sound complex, but can save you a lot of time, and help to control access to your database. ORM tools can also be customised by a Python developer.
A good Python developer should have sharp analytical skills. You are expected to observe and critically come up with complex ideas, solutions or decisions about coding. Talking of the analytical skills in Python you need to have:
Analytical skills are a mark of your additional knowledge in the field. Building your analytical skills also make you a better problem solver.
Python Developers have a bright future in Data Science. Companies on the run will prefer developers with Data Science knowledge to create innovative tech solutions. Knowing Python will also gain your knowledge of probability, statistics, data wrangling and SQL. All of these are significant aspects of Data Science.
Python is the right choice to grow in the Artificial Intelligence and Machine learning domain. It is an intuitive and minimalistic language with a full-featured library line (also called frameworks) which considerably reduces the time required to get your first results.
However, to master artificial intelligence and machine learning with Python you need to have a strong command over Python syntax. A fair grounding with calculus, data science and statistics can make you a pro. If you are a beginner, you can gain expertise in these areas by brushing up your math skills for Python Mathematical Libraries. Gradually, you can acquire your adequate Machine Learning skills by building simple neutral networks.
In the coming years, deep learning professionals will be well-positioned as there is a huge possibility awaiting in this field. With Python, you should be able to easily develop and evaluate deep learning models. Since deep learning is the advanced model of Machine Learning, to be able to bring it into complete functionality you should first get hands-on:
A good python developer is also a mixture of several soft skills like proactivity, communication and time management. Most of all, a career as a Python Developer is challenging, but at the same time interesting. Empowering yourself with these skill sets is sure to take you a long way. Push yourself from the comfort zone and work hard from today!
See the rest here:
8 Trending skills you need to be a good Python Developer - iLounge
Riverside Research Welcomes Dr. William Casebeer, Director of Artificial Intelligence and Machine Learning – PRNewswire
Dr. Casebeer's career began with the United States Air Force from which he retired from duty as a Lieutenant Colonel and intelligence analyst in 2011. He brings two decades of experience leading and growing research programs from within the Department of Defense and as a contractor. Dr. Casebeer held leadership roles at Scientific Systems, Beyond Conflict, Lockheed Martin, and Defense Advanced Research Projects Agency (DARPA).
"We are so happy to have Dr. Casebeer join our team," said Dr. Steve Omick, President and CEO. "His wealth of knowledge will be extremely valuable to not only the growth of our research and development in AI/ML but also to our other business units."
As a key member of the company's OIC, Dr. Casebeer will lead the advancement of neuromorphic computing, adversarial artificial intelligence, human-machine teaming, virtual reality for training and insight,and object and activity recognition. He will also pursue and grow opportunities with government research organizations and the intelligence community.
About Riverside Research
Riverside Research is a not-for-profit organization chartered to advance scientific research for the benefit of the US government and in the public interest. Through the company's open innovation concept, it invests in multi-disciplinary research and development and encourages collaboration to accelerate innovation and advance science. Riverside Research conducts independent research in machine learning, trusted and resilient systems, optics and photonics, electromagnetics, plasma physics, and acoustics. Learn more at http://www.riversideresearch.org.
SOURCE Riverside Research
Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk – Healthcare IT News
Since the earliest days of the COVID-19 pandemic, one of the biggest challenges for health systems has been to gain an understanding of the community spread of this virus and to determine how likely is it that a person walking through the doors of a facility is at a higher risk of being COVID-19 positive.
Without adequate access to testing data, health systems early-on were often forced to rely on individuals to answer questions such as whether they had traveled to certain high-risk regions. Even that unreliable method of assessing risk started becoming meaningless as local community spread took hold.
Parkland Health & Hospital System, the safety net health system for Dallas County, Texas, and PCCI, a Dallas-based non-profit with expertise in the practical applications of advanced data science and social determinants of health, had a better idea.
Community spread of an infectious disease is made possible through physical proximity and density of active carriers and non-infected individuals. Thus, to understand the risk of an individual contracting the disease (exposure risk), it was necessary to assess their proximity to confirmed COVID-19 cases based on their address and population density of those locations.
If an "exposure risk" index could be created, then Parkland could use it to minimize exposure for their patients and health workers and provide targeted educational outreach in highly vulnerable zip codes.
PCCIs data science and clinical team worked diligently in collaboration with the Parkland Informatics team to develop an innovative machine learning driven predictive model called Proximity Index. Proximity Index predicts for an individuals COVID-19 exposure risk, based on their proximity to test positive cases and the population density.
This model was put into action at Parkland through PCCIs cloud-based advanced analytics and machine learning platform called Isthmus. PCCIs machine learning engineering team generated geospatial analysis for the model and, with support from the Parkland IT team, integrated it with their electronic health record system.
Since April 22, Parklands population health team has utilized the Proximity Index for four key system-wide initiatives to triage more than 100,000 patient encounters and to assess needs, proactively:
In the future, PCCI is planning on offering Proximity Index to other organizations in the community schools, employers, etc., as well as to individuals to provide them with a data driven tool to help in decision making around reopening the economy and society in a safe, thoughtful manner.
Many teams across the Parkland family collaborated on this project, including the IT team led by Brett Moran, MD, Senior Vice President, Associate Chief Medical Officer and Chief Medical Information Officer at Parkland Health and Hospital System.
Current and future regulatory landscape for AI and machine learning in the investment management sector – Lexology
On Tuesday this week, Mark Lewis, senior consultant in IT, fintech and outsourcing at Macfarlanes, took part in an event hosted by The Investment Association covering some of the use cases, successes and challenges faced when implementing AI and machine learning (AIML) in the investment management industry.
Mark led the conversation on the current regulatory landscape for AIML and on the future direction of travel for the regulation of AIML in the investment management sector. He identified several challenges posed by the current regulatory framework, including those caused by the lack of a standard definition of AI generally and for regulatory purposes. This creates the risk of a fragmented regulatory landscape (an expression used recently by the World Federation of Exchanges in the context of lack of a standard taxonomy for fintech globally) as different regulators tend to use different definitions of AIML. This results in the risk of over- or under-regulating AIML and is thought to be inhibiting firms adopting new AI systems. While the UK Financial Conduct Authority (FCA) and the Bank of England seem to have settled, at least for now, on a working definition of AI as the use of a machine to perform tasks normally requiring human intelligence, and of ML as a subset of AI where a machine teaches itself to perform tasks without being explicitly programmed these working definitions are too generic to be of serious practical use in approaching regulation.
The current raft of legislation and other regulation that can apply to AI systems is uncertain, vast and complex, particularly within the scope of regulated financial services. Part of the challenge is that, for now, there is very little specific regulation directly applicable to AIML (exceptions include GDPR and, for algorithmic high-frequency trading, MiFID II). The lack of understanding of new AIML systems, combined with an uncertain and complex regulatory environment, also has an impact internally within businesses as they attempt to implement these systems. Those responsible for compliance are reluctant to engage where sufficient evidence is not available on how the systems will operate and how great the compliance burden will be. Improvements in explanations from technologists may go some way to assisting in this area. Overall, this means that regulated firms are concerned that their current systems and governance processes for technology, digitisation and related services deployments remain fit-for-purpose when extended to AIML. They are seeking reassurance from their regulators that this is the case. Firms are also looking for informal, discretionary regulatory advice on specific AIML concerns, such as required disclosures to customers about the use of chatbots.
Aside from the sheer volume of regulation that could apply to AIML development and deployment, there is complexity in the sources of regulation. For example, firms must also have regard to AIML ethics and ethical standards and policies. In this context, Mark noted that, this year, the FCA and The Alan Turing Institute launched a collaboration on transparency and explainability of AI in the UK financial services sector, which will lead to the publication of ethical standards and expectations for firms deploying AIML. He also referred to the role of the UK governments Centre for Data Ethics and Innovation (CDEI) in the UKs regulatory framework for AI and, in particular to the CDEIs AI Barometer Report (June 2020), which has clearly identified several key areas that will most likely require regulatory attention, and some with significant urgency. These include:
In the absence of significant guidance, Mark provided a practical, 10-point, governance plan to assist firms in developing and deploying AI in the current regulatory environment, which is set out below. He highlighted the importance of firms keeping watch on regulatory developments, including what regulators and their representatives say about AI, as this may provide an indication of direction in the absence of formal advice. He also advised that firms ignore ethics considerations at their peril, as these will be central to any regulation going forward. In particular, for the reasons given above, he advised keeping up to date with reports from the CDEI. Other topics discussed in the session included lessons learnt for best practice in the fintech industry and how AI has been used to solve business challenges in financial markets.
Mischief can happen when AI is let loose in the world, just like any technology. The examples of AI gone wrong are numerous, the most vivid in recent memory being the disastrously bad performance of Amazon's facial recognition technology, Rekognition, which had a propensity to erroneously match members of some ethnic groups with criminal mugshots to a disproportionate extent.
Given the risk, how can society know if a technology has been adequately refined to a level where it is safe to deploy?
"This is a really good question, and one we are actively working on, "Sergey Levine, assistant professor with the University of California at Berkeley's department of electrical engineering and computer science, told ZDNet by email this week.
Levine and colleagues have been working on an approach to machine learning where the decisions of a software program are subjected to a critique by another algorithm within the same program that acts adversarially. The approach is known as conservative Q-Learning, and it was described in a paper posted on the arXiv preprint server last month.
ZDNet reached out to Levine this week after he posted an essay on Medium describing the problem of how to safely train AI systems to make real-world decisions.
Levine has spent years at Berkeley's robotic artificial intelligence and learning lab developing AI software that to direct how a robotic arm moves within carefully designed experiments-- carefully designed because you don't want something to get out of control when a robotic arm can do actual, physical damage.
Robotics often relies on a form of machine learning called reinforcement learning. Reinforcement learning algorithms are trained by testing the effect of decisions and continually revising a policy of action depending on how well the action affects the state of affairs.
But there's the danger: Do you want a self-driving car to be learning on the road, in real traffic?
In his Medium post, Levine proposes developing "offline" versions of RL. In the offline world, RL could be trained using vast amounts of data, like any conventional supervised learning AI system, to refine the system before it is ever sent out into the world to make decisions.
Also: A Berkeley mash-up of AI approaches promises continuous learning
"An autonomous vehicle could be trained on millions of videos depicting real-world driving," he writes. "An HVAC controller could be trained using logged data from every single building in which that HVAC system was ever deployed."
To boost the value of reinforcement learning, Levine proposes moving from the strictly "online" scenario, exemplified by the diagram on the right, to an "offline" period of training, whereby algorithms are input with masses of labeled data more like traditional supervised machine learning.
Levine uses the analogy of childhood development. Children receive many more signals from the environment than just the immediate results of actions.
"In the first few years of your life, your brain processed a broad array of sights, sounds, smells, and motor commands that rival the size and diversity of the largest datasets used in machine learning," Levine writes.
Which comes back to the original question, to wit, after all that offline development, how does one know when an RL program is sufficiently refined to go "online," to be used in the real world?
That's where conservative Q-learning comes in. Conservative Q-learning builds on the widely studied Q-learning, which is itself a form of reinforcement learning. The idea is to "provide theoretical guarantees on the performance of policies learned via offline RL," Levine explained to ZDNet. Those guarantees will block the RL system from carrying out bad decisions.
Imagine you had a long, long history kept in persistent memory of what actions are good actions that prevent chaos. And imagine your AI algorithm had to develop decisions that didn't violate that long collective memory.
"This seems like a promising path for us toward methods with safety and reliability guarantees in offline RL," says UC Berkeley assistant professor Sergey Levine, of the work he and colleagues are doing with "conservative Q-learning."
In a typical RL system, a value function is computed based on how much a certain choice of action will contribute to reaching a goal. That informs a policy of actions.
In the conservative version, the value function places a higher value on that past data in persistent memory about what should be done. In technical terms, everything a policy wants to do is discounted, so that there's an extra burden of proof to say that the policy has achieved its optimal state.
A struggle ensues, Levine told ZDNet, making an analogy to generative adversarial networks, or GANs, a type of machine learning.
"The value function (critic) 'fights' the policy (actor), trying to assign the actor low values, but assign the data high values." The interplay of the two functions makes the critic better and better at vetoing bad choices. "The actor tries to maximize the critic," is how Levine puts it.
Through the struggle, a consensus emerges within the program. "The result is that the actor only does those things for which the critic 'can't deny' that they are good (because there is too much data that supports the goodness of those actions)."
Also: MIT finally gives a name to the sum of all AI fears
There are still some major areas that need refinement, Levine told ZDNet. The program at the moment has some hyperparameters that have to be designed by hand rather than being arrived at from the data, he noted.
"But so far this seems like a promising path for us toward methods with safety and reliability guarantees in offline RL," said Levine.
In fact, conservative Q-learning suggests there are ways to incorporate practical considerations into the design of AI from the start, rather than waiting till after such systems are built and deployed.
Also: To Catch a Fake: Machine learning sniffs out its own machine-written propaganda
The fact that it is Levine carrying out this inquiry should give the approach of conservative Q-learning added significance. With a firm grounding in real-world applications of robotics, Levine and his team are in a position to validate the actor-critic in direct experiments.
Indeed, the conservative Q-Learning paper, which is lead-authored by Aviral Kumar of Berkeley, and was done with the collaboration of Google Brain, contains numerous examples of robotics tests in which the approach showed improvements over other kinds of offline RL.
There is also a blog post authored by Google if you want to learn more about the effort.
Of course, any system that relies on amassed data offline for its development will be relying on the integrity of that data. A successful critique of the kind Levine envisions will necessarily involve broader questions about where that data comes from, and what parts of it represent good decisions.
Some aspects of what is good and bad may be a discussion society has to have that cannot be automated.
See the article here:
How do we know AI is ready to be in the wild? Maybe a critic is needed - ZDNet
Panalgo Brings the Power of Machine-Learning to the Healthcare Industry Via Its Instant Health Data (IHD) Software – PRNewswire
BOSTON, Sept. 15, 2020 /PRNewswire/ -- Panalgo, a leading healthcare analytics company, today announced the launch of its new Data Sciencemodule for Instant Health Data (IHD), which allows data scientists and researchers to leverage machine-learning to uncover novel insights from the growing volume of healthcare data.
Panalgo's flagship IHD Analytics softwarestreamlines the analytics process by removing complex programming from the equation and allows users to focus on what matters most--turning data into insights. IHD Analytics supports the rapid analysis of a wide range of healthcare data sources, including administrative claims, electronic health records, registry data and more. The software, which is purpose-built for healthcare, includes the most extensive library of customizable algorithms and automates documentation and reporting for transparent, easy collaboration.
Panalgo's new IHD Data Science module is fully integrated with IHD Analytics, and allows for analysis of large, complex healthcare datasets using a wide variety of machine-learning techniques. The IHD Data Science module provides an environment to easily train, validate and test models against multiple datasets.
"Healthcare organizations are increasingly using machine-learning techniques as part of their everyday workflow. Developing datasets and applying machine-learning methods can be quite time-consuming," said Jordan Menzin, Chief Technology Officer of Panalgo. "We created the Data Science module as a way for users to leverage IHD for all of the work necessary to apply the latest machine-learning methods, and to do so using a single system."
"Our new IHD Data Science product release is part of our mission to leverage our deep domain knowledge to build flexible, intuitive software for the healthcare industry," said Joseph Menzin, PhD, Chief Executive Officer of Panalgo. "We are excited to empower our customers to answer their most pressing questions faster, more conveniently, and with higher quality."
The IHD Data Science module provides advanced analytics to better predict patient outcomes, uncover reasons for medication non-adherence, identify diseases earlier, and much more. The results from these analyses can be used by healthcare stakeholders to improve patient care.
Research abstracts using Panalgo's IHD Data Science module are being presented at this week's International Conference on Pharmacoepidemiology and Therapeutic Risk Management, including: "Identifying Comorbidity-based Subtypes of Type 2 Diabetes: An Unsupervised Machine Learning Approach," and "Identifying Predictors of a Composite Cardiovascular Outcome Among Diabetes Patients Using Machine Learning."
About Panalgo Panalgo, formerly BHE, provides software that streamlines healthcare data analytics by removing complex programming from the equation. Our Instant Health Data (IHD) software empowers teams to generate and share trustworthy results faster,enabling more impactful decisions. To learn more, visit us athttps://www.panalgo.com. To request a demo of our IHD software, please contact us at [emailprotected].