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The new study, published today inScientific Reports, has found that machine learning has the potential to enhance and improve a veterinarian's ability to accurately diagnose herd mastitis origin and reduce mastitis levels on dairy farms.

Mastitis is an extremely costly endemic disease of dairy cattle, costing around 170 million in the UK. A crucial first step in the control of mastitis is identifying where mastitis causing pathogens originate; does the bacteria come from the cows' environment or is it contagiously spread through the milking parlour?

This diagnosis is usually performed by a veterinarian by analysing data from the dairy farm and is a cornerstone of the widely used Agriculture and Horticulture Development Board (AHDB) mastitis control plan, however this requires both time and specialist veterinary training.

Machine learning algorithms are widely used, from filtering spam emails and the suggestion of Netflix movies to the accurate classification of skin cancer. These algorithms approach diagnostic problems as a student doctor or veterinarian might; learning rules from data and applying them to new patients.

This study, which was led by veterinarian and researcher Robert Hyde from the School of Veterinary Medicine and Science at the University of Nottingham, aims to create an automated diagnostic support tool for the diagnosis of herd level mastitis origin, an essential first step of the AHDB mastitis control plan.

Mastitis data from 1,000 herds' was inputted for several three-month periods. Machine learning algorithms were used to classify herd mastitis origin and compared with expert diagnosis by a specialist vet.

The machine learning algorithms were able to achieve a classification accuracy of 98% for environmental vs contagious mastitis, and 78% accuracy was achieved for the classification of lactation vs dry period environmental mastitis when compared with expert veterinary diagnosis.

Dr Hyde said: "Mastitis is a huge problem for dairy farmers, both economically and in welfare terms. In our study we have shown that machine learning algorithms can accurately diagnose the origin of this condition on dairy farms. A diagnostic tool of this kind has great potential in the industry to tackle this condition and to assist veterinary clinicians in making a rapid diagnosis of mastitis origin at herd level in order to promptly implement control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use."

Excerpt from:
Machine learning could improve the diagnosis of mastitis infections in cows - Jill Lopez

New research published by TABB Group says the sell-side is in a survival of the fittest race for the top spot in buy-side clients algo wheels.

According to New York-based senior equity analyst Michael Mollemans the author of AI in Sell-Side Equity Algorithms: Survival of the Fittest, the sell-sides artificial intelligence (AI) equity algorithm ecosystem has expanded after years of development work to a point where significant AI-attributable excess returns have finally begun to be realised in the past two years.

As automated performance measurement applications like algo wheels are driving broker selection decisions, competition to build better, faster, smarter algorithms has become a war of attrition. Now more than ever, says Mollemans, not keeping up means youre going backwards, which is why we believe consolidation in the algorithmic trading space will continue, just as the sell-side overall continues to consolidate.

TABB Group interviewed 50 AI algorithm experts from the buy side, sell side, and fintech vendors and produced AI algo ecosystem case studies on US, European, and Asian banks. The 27 page, nine-exhibit report, created to help traders gain depth and breadth of insight and a better understanding about whats happening under the hood in their AI algorithms, covers eight key areas:

How sell-side firms must stay ahead of rapidly evolving, AI-algo data science

Improvements in performance attributable to AI models

Leveraging economies of scale and development budgets to support advanced AI ecosystems

AI applications focusing on scheduling, price and volume prediction, spread capture, strategy and parameter selection and venue-routing decisions

Explainable AI

Turning a black box algo into a clear box

Utilising proprietary data unavailable to competitors

How oversight and governance procedures will become more sophisticated

Moving forward, Mollemans believes that only a few banks will dominate the global algorithmic trading space in the next five years.

The most significant challenge is the changing science of AI and the growing investment needed to transition from traditional algos to AI," he says. "Some of these AI-based techniques, like t-SNE, were not even in existence 10 years ago. In fact, 41 per cent of sell-side firms interviewed launched their client-based AI algos only last year.

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Banks' machine learning/AI-based algorithms are gaining traction and generating alpha - Institutional Asset Manager

Holdyour head up high! The rise of artificial intelligence (AI) and machinelearning (ML) are poised to bring a new era of civilization and not destroythem.

Yet,theres fear that technology will displace the current workers or tasks, andthats partly true. As predicted by researches, the speed at which AI isreplacing jobs is bound to skyrocket, impacting the jobs of several workerssuch as factory workers, accountants, radiologists, paralegal, and truckers.

Shufflingand transformation of jobs around the workforce are being witnessed, thanks tothe technological epoch.

Buthey, were still far from Terminator.

What can be the odds?

The fear is good, perhaps it is only a matter of time before AI and automation will replace the jobs of millions of tech professionals. A 2018 report by the World Economic Forum suggested that around 75 million jobs will be displaced due to automation and AI in the next five years. The good news is, despite these many jobs will be replaced, at the same time, there will also be a creation of 133 million newer job roles for AI engineers and AI experts.

Simplysaid, within the next five years, there will be near about 58 million newer jobroles in the field of AI.

Insteadof worrying about AI and automation stealing your job, you should beconsidering how you need to reshape your career.

AI and ML in theworkplace: How prepared are you for the impact?

AIand machine learning projects are now leading every industry and sector intothe future of technological advancements. The question is, what are the bestways for you to bring these experiences into reality? What are the programminglanguages that can be used for machine learning and AI?

Thinkahead, you can start by considering Python for machine learning and AI.

But why Python?

Python is the foundational language for AI. However, the projects do differ from a traditional software project, thus, it is necessary to dive deeper into the subject. The crux of building an AI career is by learning Python a programming language that is loved by all because it is both stable and flexible. It is now widely used for machine learning applications and why not, it has become one of the best choices across industries.

Over here, we will list down why Python is the most preferred programming language by AI experts today:

Huge bundle of libraries/frameworks

Itis often a tricky task to choose what best fits while running an ML or an AIalgorithm. It is crucial to have the right set of libraries, a well-structuredenvironment for developers to come up with the best coding solution.

Toease their development timings, most developers rely on Python libraries andframeworks. In a software library, there are already pre-written codes that thedevelopers look up to solve programming challenges. This is where Pythonspre-existing extensive set of libraries play a major role in providing themwith the set of libraries and frameworks to choose from. To name a few are:

With these solutions, it gets easier for the developer to develop your product faster. Even so, the development team needs to waste time finding the libraries that will best suit their project. They can always use an existing library for the implementation of further changes.

Holds a strong community and wide popularity

Accordingto a developer survey Stack Overflow (2018), Python was seen to be among thetop most popular programming language amongst developers. This simply means,for every job that you seek in the job market, AI will always be one of theskillsets that they will look to hire for.

Itis also seen that there are nearly more than 140,000 online repositories thathave custom-built software packages of Python. For instance, Python librariessuch as SciPy, NumPy, and Matplotlib can easily be installed in a program thatruns on Python.

Pythonwas pointed out to be 2019s 8th fastest growing programminglanguage with a growth rate of 151% year on year.

Now, these packages used in machine learning helps AI engineers detect patterns from a large dataset. Pythons popularity is widely known that even Google uses this language to crawl web pages. Pixar, an animation studio uses it to produce movies. Surprisingly, even Spotify uses Python for song recommendation.

Within the past years, Python has managed to grow its community worldwide. You can find multiple platforms and forums where machine learning solutions are shared. For every problem, youve faced youll always find theres already someone who has been through with the same problem. Thus, it is easy to find solutions and guidance through this community.

Platform-independent

This simply means, a programming language or a framework allows developers to implement things on a single machine learning, and the same can be used on another machine learning without further changing anything. The best factor about Python is that it is a language that is platform-independent and is supported by several other platforms such as Windows, macOS, and Linux.

Python code can itself create a standalone program that is executable in most operating systems without even needing a Python interpreter.

Simple and most loved programming language

Python is said to be the simplest and the most consistent programming language offering readable code. While there are complex algorithms that stand along with machine learning, Pythons concise and easy readability allows AI professionals to write easy systems that are reliable. This allows the developers to solve complex machine learning problems instead of dealing with technical issues of the language.

Sofar Python is projected to be the only language that is easy for developers tolearn. Some say Python is intuitive as compared to other programming languages.While others believe, it is due to the number of libraries Python offers thatmakes it suitable for all developers to use.

In conclusion

Pythons power and ease of use has catapulted it to become one of the core languages to provide machine learning solutions. Moreover, AI and ML have been the biggest innovation so far ever since the launch of microchip, developing a career in this realm will pave a way toward the future of tomorrow.

About the Author

Michael Lyam is a writer, AI researcher, business strategist, and top contributor on Medium. He is passionate about technology and is inspired to find new ways to create captivating content. Michaels areas of expertise are: AI, machine learning, data science, and business strategy.

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The Impact of Python: How It Could Rule the AI World? - insideBIGDATA

More News13 Mar 2020 | 8:41 PM

Guwahati, Mar 13 (UNI) The Assam Government has increased its precautionary measures to ensure no outbreak of Coronavirus infection in the state.

Kolkata, Mar 13 (UNI) EEPC India chairman Ravi Sehgal today said though exports from India in February 2020 managed to achieve a positive growth, the outlook looked grim going forward due to pandemic Coronavirus which has affected almost all countries in the world.

Kendrapara, Mar 13 (UNI) Two youths were drowned in Chitroptala river while bathing near Patkura bridge.

Giridih, Mar 13 (UNI) A suspected patient of Coronavirus has been referred from the Sadar Hospital to RIMS Ranchi.

Shillong, Mar 13 (UNI) Meghalaya Governor Tathagata Roy on Friday praised the State Police for effectively handling the agitations against Citizenship (Amendment) Act, 2019 (CAA) and demand for Inner Line Permit (ILP).

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TRAI organises a seminar on 'Emergence of Artificial Intelligence and Machine Learning' - United News of India

John Leyden09 March 2020 at 15:50 UTC Updated: 09 March 2020 at 16:04 UTC

Forget crowdsourcing, heres crooksourcing

Computer scientists in the US are working to apply machine learning techniques in order to develop more effective honeypot-style cyber defenses.

So-called deception technology refers to traps or decoy systems that are strategically placed around networks.

These decoy systems are designed to act as a honeypot so that once an attacker has penetrated a network, they will attempt to attack them setting off security alerts in the process.

Deception technology is not a new concept. Companies including Illusive Networks and Attivo have been working in the field for several years.

Now, however, researchers from the University of Texas at Dallas (UT Dallas) are aiming to take the concept one step further.

The DeepDig (DEcEPtion DIGging) technique plants traps and decoys onto real systems before applying machine learning techniques in order to gain a deeper understanding of attackers behavior.

The technique is designed to use cyber-attacks as free sources of live training data for machine learning-based intrusion detection systems.

Somewhat ironically, the prototype technology enlists attackers as free penetration testers.

Dr Kevin Hamlen, endowed professor of computer science at UT Dallas, explained: Companies like Illusive Networks, Attivo, and many others create network topologies intended to be confusing to adversaries, making it harder for them to find real assets to attack.

The shortcoming of existing approaches, Dr Hamlen, told The Daily Swig is that such deceptions do not learn from attacks.

While the defense remains relatively static, the adversary learns over time how to distinguish honeypots from a real asset, leading to an asymmetric game that the adversary eventually wins with high probability, he said.

In contrast, DeepDig turns real assets into traps that learn from attacks using artificial intelligence and data mining.

Turning real assets into a form of honeypot has numerous advantages, according to Dr Hamlen.

Even the most skilled adversary cannot avoid interacting with the trap because the trap is within the real asset that is the adversary's target, not a separate machine or software process, he said.

This leads to a symmetric game in which the defense continually learns and gets better at stopping even the most stealthy adversaries.

The research which has applications in the field of web security was presented in a paper (PDF) entitled Improving Intrusion Detectors by Crook-Sourcing, at the recent Computer Security Applications Conference in Puerto Rico.

The research was funded by the US federal government. The algorithms and evaluation data developed so far have been publicly released to accompany the research paper.

Its hoped that the research might eventually find its way into commercially available products, but this is still some time off and the technology is still only at the prototype stage.

In practice, companies typically partner with a university that conducted the research theyre interested in to build a full product, a UT Dallas spokesman explained. Dr Hamlens project is not yet at that stage.

RELATED Gold-nuggeting: Machine learning tool simplifies target discovery for pen testers

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AI-powered honeypots: Machine learning may help improve intrusion detection - The Daily Swig