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Why Uganda does not need car digital monitoring and tracking technologies to fight crime – Daily Monitor

September 1, 2021 | Author: admin

By Guest Writer

In the 1990s, the UK government procured tacit consent from her citizens to use Closed Circuit Televisions (CCTVs) dubbed as the miracle solution to criminality. A document titled CCTV-Looking Out for You was published in 1994 to reinforce support for the use of video surveillance as a faultless crime-fighting machine. With over 5.2 million cameras spread across the country (one camera for every 13 Britons), the crime rate per capita in the UK is still high although UK citizens are the most virtually herded people on planet earth.

For Ugandans now, imagine that one day you woke up and your freedom of movement (forget Covid-19 movement restrictions) has a price on it, and the price is that every time you leave your home using your car or motorcycle, the government will be able to know all your whereabouts, daily routines, capture and keep all these records in a database for a given period, not to mention predict your future movement using complex algorithms and data mining tools. This is exactly what the proposed car digital monitoring and tracking system will do, disguised as an Intelligent Transport Management System (ITMS). How will this be done? Simple! Have all automobiles fitted with Automatic Number Plate Recognition (ANPR) devices and install ANPR CCTV cameras on roads that will capture all details about the automobile like direction and a web of travel routes, timestamp, drivers details, make and color of automobile among others, all done in the mighty name of fighting crime. But do we need all these Big Brother intrusive technological surveillance systems to curb crime?

Crime is such a complex social phenomenon caused by a multivariate of factors whose prevention measures or models cannot simply be thought of, or assumed to be monolithic. To think or make such assumptions is to have a nave understanding of crime as a social construct. And to worship a system of hi-tech surveillance cameras as crime saviours is a mistaken belief in the powers of video surveillance known as the CCTV myth. No empirical research to date proves that hi-tech video surveillance systems have any general impact on crime reduction. Studies in the US and several commissioned by the UK Home Office to study the impact of video surveillance systems on crime found no statistically significant relationship between the two. A 2009 House of Lords Committee Report on video surveillance also noted that CCTV cameras were not as effective in preventing crime as earlier believed. CCTVs were instead found to be most effective in dissuading car thefts but not preventing them as such. Overall, studies show that areas manned with cameras do not outperform those without them in terms of crime prevention.

But why do governments still insist on justifying the use of hi-tech surveillance technologies as crime-fighting tools yet evidence shows that they arent? The always quick and blunt answer from government officials is if you are not doing anything wrong, you have nothing to worry about. On 8th June 1949, George Orwell, published a novel titled 1984. In the book, he warned that that societies would be doomed if they left unchecked the kind of totalitarian thinking taking root in the minds of policy maker and intellectuals. Many citizenspolicymakersworld are currently in this Orwellian state of affairs, where their governments unjustifiably seek measures of social control which instead restrict citizens fundamental rights and freedoms such as freedom of movement, privacy, and autonomy.

The ANPR technology was first developed in the UK in the 1970s by the Home Offices Scientific Development Branch and tested in the 1980s to aid investigations into allegedly stolen cars. This was initially done by comparing digital film shots of a vehicle number plate to a database of allegedly stolen vehicles. This technology was further developed into the ANPR network found in the UK today, with over 10 billion peoples data recorded and stored in the database. Londons transport system has over 1,400 cameras collecting number plate data of vehicles to keep tabs on traffic congestion and carbon emission. This is an ITMS led transportation system commonly found in heavy industrial complexes and smart cities to control traffic and reduce accidents. How feasible and sustainable is an ITMS in Uganda, or specifically for Kampala citys chaotic transport management system? And how will it help curb crime, since its part of the main justifications for the Russian deal?

On 24th July 2013, the Information Commissioners Office (ICO), UKs data protection authority issued an Enforcement Notice to the Hertfordshire Constabulary Police instructing it to halt its use of the vehicle number plate tracking system in Royston town it considered illegal and unlawful. This followed complaints by those concerned about the Polices use of ANPR to track all cars entering and leaving the city and that yet were installed devoid of any public debate or legal framework. The ICO held that the use of ANPR cameras and other forms of surveillance systems must be justified and proportionate to the problems it sought to address with a prior comprehensive assessment of their impact on the privacy of road users. However, despite being a much welcomed and fair ruling, the problem here is is at least twofold. Firstly, the ICO is a quasi-judicial body whose decisions are only advisory and not binding in law. Secondly, some activists think that calls for justification and proportionality in using surveillance systems only rubber stamps and legitimizes state intrusion of citizens privacy with no apparent overarching value.

So, do we as Ugandans need all these hi-tech intrusive surveillance systems to prevent crime? Well, someones misguided and ill-informed contention despite reading this article thus far may still assume that we do. Numerous evidence suggests that it is through communities, and not video technological surveillance that crimes can be reduced or even prevented. Such can be achieved through the creation and reinforcement of social communal bonds that enhance social cohesion and produce social capital to tackle crime. How will a system of digital car monitoring and tracking solve serious crimes such as corruption, land grabbing, defilement, murder and an array of cybercrimes Ugandans are currently facing? Instead, they can pose serious cyber (national) security issues if the system is not fully secured. Video surveillance has limited utility. Firstly, it can induce fear in the mind of a criminally motivated offender but does not in itself prevent him from committing a crime like the CCTV crusaders would like us to believe. If so was the case, there would be no crime in London and New York or other cities littered with surveillance cameras. Secondly, it can be used for evidentiary purposes in court. But how many have been adduced in court as evidence to support the prosecution of crimes in Uganda? We need a study on this too, otherwise the overall efficacy and effectiveness of video surveillance in crime control, prevention, and reduction should not be glorified and overstated beyond their capacity. To believe that digital number plates will meaningfully solve crime is utterly unfounded and grossly mistaken.

ByDaniel Adyera

Director, Centre for Criminology and Criminal Justice Policyemail: [emailprotected]

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Basic Concepts in Machine Learning

September 1, 2021 | Author: admin

Last Updated on August 15, 2020

What are the basic concepts in machine learning?

I found that the best way to discover and get a handle on the basic concepts in machine learning is to review the introduction chapters tomachine learning textbooks and to watch the videos from the first model inonlinecourses.

Pedro Domingos is a lecturer and professor on machine learning at the University of Washing and author of a new book titled The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.

Domingos has a free course on machine learning online at courser titled appropriately Machine Learning. The videos for each module can be previewedon Coursera any time.

In this post you will discover the basic concepts of machine learning summarized from Week One of Domingos Machine Learning course.

Basic Concepts in Machine LearningPhoto by Travis Wise, some rights reserved.

The first half of the lecture is on the general topic of machine learning.

Why do we need to care about machine learning?

A breakthrough in machine learning would be worth ten Microsofts.

Bill Gates, Former Chairman, Microsoft

Machine Learning is getting computers to program themselves. If programming is automation, then machine learning is automating the process of automation.

Writing software is the bottleneck, we dont have enough good developers. Let the data do the work instead of people. Machine learning is the way to make programming scalable.

Machine learning is like farming or gardening. Seeds is the algorithms, nutrientsis the data, thegardneris you and plants is the programs.

Traditional Programming vs Machine Learning

Sample applications of machine learning:

What is your domain of interest and how could you use machine learning in that domain?

There are tens of thousands of machine learning algorithms and hundreds of new algorithms are developed every year.

Every machine learning algorithm has three components:

All machine learning algorithms are combinations of these three components. A framework for understanding all algorithms.

There are four types of machine learning:

Supervised learning is the most mature, the most studied and the type of learning used bymost machine learning algorithms. Learning with supervision is much easier than learning without supervision.

Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x).

Machine learning algorithms are only a very small part of using machine learning in practice as a data analyst or data scientist. In practice, the process often looks like:

It is not a one-shot process, it is a cycle. You need to run the loop until you get a result that you can use in practice. Also, the data can change, requiring a new loop.

The second part of the lecture is on the topic of inductive learning. This is the general theory behind supervised learning.

From the perspective of inductive learning, we are given input samples (x) and output samples (f(x)) and the problem is to estimate the function (f). Specifically, the problem is to generalize from the samples and the mapping to be useful to estimate the output fornew samples in the future.

In practice it is almost always too hard to estimate the function, so we are looking for very good approximations of the function.

Some practical examples of induction are:

There are problems where inductive learning is not a good idea. It is important when to use and when not to use supervised machine learning.

4 problems where inductive learning might be a good idea:

We can write a program that works perfectly for the data that we have. This function will be maximally overfit. But we have no idea how well it will work on new data, it will likely be very badly because we may never see the same examples again.

The data is not enough. You canpredictanything you like. And this would be naive assume nothing about the problem.

In practice we are not naive. There is an underlying problem and we areinterested inan accurate approximation of the function. There is a double exponential number of possible classifiers in the number of input states. Finding a good approximate for the function is verydifficult.

There are classes of hypotheses that we can try. That is the form that the solution may take or the representation. We cannot know which is most suitable for our problem before hand. We have to use experimentation to discover what works on the problem.

Two perspectives on inductive learning:

You could be wrong.

In practice we start with a small hypothesis class and slowly grow the hypothesis class until we get a good result.

Terminology used inmachine learning:

Key issues in machine learning:

There are 3concerns for a choosing a hypothesis spacespace:

There are 3properties by which you could choose an algorithm:

In this post you discovered the basic concepts in machine learning.

In summary, these were:

These are the basic concepts that are covered in the introduction to most machine learning courses and in the opening chapters of any good textbook on the topic.

Although targeted at academics,as a practitioner, it is useful to have a firm footingin these concepts in order to better understand how machine learning algorithmsbehave in the general sense.

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Machine Learning Helps Clarify the Risk Connected to Age-Related Blood Condition – On Cancer – Memorial Sloan Kettering

September 1, 2021 | Author: admin

Artificial intelligence (AI) and machine learning allow researchers to study databases that otherwise would be too large and complex. In a recent study, Sloan Kettering Institute computational biologist Quaid Morris and collaborators used models to study an aging-related blood condition called clonal hematopoiesis (CH).

Their research showed how evolution and natural selection influence CH and the effects that it may have on health outcomes. CH is relatively common in older people, affecting up to 10% of the population by age 80. The condition raises the risk of developing blood disorders including some blood cancers and cardiovascular disease.

One of the issues that we face in studying something complicated like CH is the interplay of many different factors, says Dr. Morris, who is co-senior author of a paper on CH published August 13, 2021, in Nature Communications. AI could eventually give us the tools to guide clinical decisions.

Hematopoietic stem cells are cells that will eventually develop into different types of blood cells. In people with CH, some of these hematopoietic stem cells instead form a group of cells that is genetically distinct from the rest of their counterparts.Some of these subsets of cells, or clones, may contain mutations linked to cancer. This process can eventually lead to problems.

The presence of these mutations in the blood doesnt mean that the person carrying them has or will definitely develop cancer, but studies have shown that people with CH are at higher risk of developing certain blood cancers, especiallymyelodysplastic syndromeandacute myeloid leukemia (AML). They are also at increased risk for cardiovascular disease, heart attacks, and strokes.

In January 2018, Memorial Sloan Kettering Cancer Center launched a clinic for cancer patients found to have CH. The clinic provides these patients with regular monitoring for signs of blood cancer and regular screening for cardiovascular disease risk. Early detection of cancer or heart disease allows doctors to step in right away with a treatment plan. The clinic also has an important forward-looking research component: trying to understand which patients with CH are at highest risk of future health problems.

In the current study, the researchers looked at how different CH-related mutations interact with each other to increase or decrease the chances that a cancer-causing clone will eventually rise to dominance and progress become to cancer.

This type of research requires complex statistical models, says Dr. Morris, a member of the Computational and Systems Biology Program. Deep learning and neural network techniques are AI methods that can help us to make inferences about whats going on in this population of hematopoietic cells and study the interplay of different subsets of cells.

The hope is that AI can help us make sense of patterns that are so complex that we'd never be able to see them on our own.

The researchers used blood samples collected as part of the European Prospective Investigation into Cancer and Nutrition, anongoing, multicenter study that has medical information on about 65,000 people spanning almost three decades. The analysis of blood samples with CH included 92 samples from people who eventually developed AML and 385 controls (people who did not have AML).

This research was done in collaboration with scientists at the Ontario Institute for Cancer Research (OICR) and the University of Toronto, where Dr. Morris worked before coming to MSK. The co-senior author, Philip Awadalla of OICR, is an expert in population genetics, a field that focuses on how genes change in response to evolution and natural selection.

Dr. Morris says data collected through MSKs CH clinic will make this kind of analysis much more precise and potentially more useful going forward. The data we used in the current study was retrospective and taken from a single snapshot in time, he explains. In contrast, he notes, the CH clinic is collecting multiple samples from the same patients over months or years. This means that models we build with this data will be more informed and more effective at studying patterns over time and help us to make better predictions, he adds.

CH research is an important component of calculating and understanding cancer risk, a major goal of MSKs Precision Interception and Prevention Program. The objective of this approach is to either prevent cancer from occurring or stop it at the earliest stages, when its easier to treat.

The hope is that AI can help us make sense of patterns that are so complex that wed never be able to see them on our own, Dr. Morris says.

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Industry VoicesWhy the COVID-19 pandemic was a watershed moment for machine learning – FierceHealthcare

September 1, 2021 | Author: admin

Times of crisis spark innovation and creativity, as evidenced in the way organizations have come together to innovate for the greater good during the COVID-19 pandemic.

Liquor distilleries started producing hand sanitizer, 3D printing companies made face shields and nasal swabs to meet massive demandsand auto companies shifted gears to make ventilators.

Machine learning (ML)computer systems that learn and adapt autonomously by using algorithms and statistical models to analyze and draw inferences from patterns in data to inform and automate processeshas also played an important role, supporting practically every aspect of healthcare. Amazon Web Services has supported customers as they enable remote patient care, develop predictive surge planning to help manage inpatient/ICU bed capacityand tackle the unprecedented feat of developing an messenger ribonucleic acid (mRNA)-based COVID-19 vaccine in under a year.

We now have the opportunity to build on our lessons from the past year to apply ML to help address several underlying problems that plague the healthcare and life sciences communities.

Telehealth was on the rise before COVID-19, but it revealed its true potential during the pandemic. Telehealth is often viewed simply as patients and providers interacting online via video platforms but has proven capable of doing much more. Applying ML to telehealth provides a unique opportunity to innovate, scale and offer more personalized experiences for patients and ensure they have access to the resources and care they need, no matter where they're located.

ML-based telehealth tools such as patient service chatbots, call center interactions to better triage and direct patients to the information and care they requireand online self-service prescreenings are helping optimize patient experiences and streamline provider assessments and diagnostics.

RELATED:Global investment in telehealth, artificial intelligence hits a new high in Q1 2021

For example, GovChat, South Africa's largest citizen engagement platform, launched a COVID-19 chatbot in less than two weeks using an artificial intelligence (AI) service for building conversational interfaces into any application using voice and text. The chatbot provides health advice and recommendations on whether to get a test for COVID-19, information on the nearest COVID-19 testing facility, the ability to receive test resultsand the option for citizens to report COVID-19 symptoms for themselves, their family membersor other household members.

In addition, early in the COVID-19 crisis, New York City-based MetroPlusHealth identified approximately 85,000 at-risk individuals (e.g., comorbid heart or lung disease, or immunocompromised) who would require additional support services while sheltering in place. In order to engage and address the needs of this high-risk population, MetroPlusHealth developed ML-enabled solutions including an SMS-based chatbot that guides people through self-screening and registration processes, SMS notification campaigns to provide alerts and updated pandemic informationand a community-based organizations referral platform, called Now Pow, to connect each individual with the right resource to ensure their specific needs were met.

By providing an easy way for patients to access the care, recommendationsand support they need, ML has given providers the ability to innovate and scale their telehealth platforms to support diverse and continuously changing community needs. Agile, scalableand accessible telehealth continues to be important as providers look for ways to reach and engage patients in hard-to-reach or rural areas and those with mobility issues. Organizations and policymakers globally need to make telehealth and easy access to care a priority now and going forward in order to close critical gaps in care.

Beyond the unprecedented shifts in the approach to engaging, supporting and treating patients, COVID-19 has dictated clear direction for the future of patient care: precision medicine.

Guidelines for patient care planning care have shifted from statistically significant outcomes gathered from a general population to outcomes based on the individual. This gives clinicians the ability to understand what type of patient is most prone to have a disease, not just what sort of disease a specific patient has. Being able to predict the probability of contracting a disease far in advance of its onset is important to determining and initiating preventative, intervening, and corrective measures that can be tailored to each individual's characteristics.

RELATED:What's on the horizon for healthcare beyond COVID-19? Cerner, Epic and Meditech executives share their takes

One of the best examples of how ML is enabling precision medicine is biotech company Modernas ability to accelerate every step of the process in developing an mRNA vaccine for COVID-19. Moderna began work on its vaccine the moment the novel coronaviruss genetic sequence was published. Within days, the company had finalized the sequence for its mRNA vaccine in partnership with the National Institutes of Health.

Moderna was able to begin manufacturing the first clinical-grade batch of the vaccine within two months of completing the sequencinga process that historically has taken up to 10 years.

Personalized health isn't only about treating disease, it's about providing access to resources and information specific to a patient's needs. ML is playing a key role in curating content that can help to educate and support patients, caregivers and their families.

Breastcancer.org allows individuals with breast cancer to upload their pathology report to a private and secure personal account. The organization uses ML-based natural language processing to analyze and understand the report and create personalized information for the patient based on their specific pathology.

RELATED:Healthcare AI investment will shift to these 5 areas in the next 2 years: survey

For the last decade, organizations have focused on digitizing healthcare. Today, making sense of the data being captured will provide the biggest opportunity to transform care. Successful transformation will depend on enabling data to flow where it needs to be at the right time while ensuring that all data exchange is secure.

Interoperability is by far one of the most important topics in this discussion. Today, most healthcare data is stored in disparate formats (e.g., medical histories, physician notes and medical imaging reports), which makes extracting information challenging. ML models trained to support healthcare and life sciences organizations help solve this problem by automatically normalizing, indexing, structuring and analyzing data.

ML has the potential to bring data together in a way that creates a more complete view of a patient's medical history, making it easier for providers to understand relationships in the data and compare specific data to the rest of the population. Better data management and analysis leads to better insights, which lead to smarter decisions. The net result is increased operational efficiency for improved care delivery and management, and most importantly, improved patient experiences and health outcomes.

Looking ahead, imagine a time when our pernicious medical conditions like cancer and diabetes can be treated with tailored medicines and care plans enabled by AI and ML. The pandemic was a turning point for how ML can be applied to tackle some of the toughest challenges in the healthcare industry, though we've only just scratched the surface of what it can accomplish.

Taha Kass-Hout is the director of machine learning for Amazon Web Services.

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How to upskill your team to tackle AI and machine learning – VentureBeat

September 1, 2021 | Author: admin

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience and to shine a light on some of these leaders. In this series, publishing Fridays, were diving deeper into conversations with this years winners, whom we honored recently at Transform 2021. Check out last weeks interview with a winner of our AI rising star award.

No one got more nominations for a VentureBeat AI award this year than Katia Walsh, a reflection of her career-long effort to mentor women in AI and data science across the globe.

For example, Mark Minevich, chair of AI Policy at International Research Center of AI under UNESCO, said, Katia is an impressive, values-driven leader [who has] been a diversity champion and mentor of women, LGBTQ, and youth at Levi Strauss & Co, Vodafone, Prudential, Fidelity, Forrester, and in academia over many years. And Inna Saboshchuk, a current colleague of Walshs at Levi Strauss & Co, said, a single conversation with her will show you how much she cares for the people around her, especially young professionals within AI.

In particular, these nominators and many others highlighted Walshs efforts to upskill team members. Most recently, she launched a machine learning bootcamp that allowed people with no prior experience to not only learn the skills, but apply them every day in their current roles.

VentureBeat is thrilled to present Walsh with this much-deserved AI mentorship award. We recently caught up with her to learn more about the early success of her latest bootcamp, the power of everyday mentorship, and the role it can play in humanizing AI.

This interview has been edited for brevity and clarity.

VentureBeat: You received a ton of nominations for this award, so clearly youre making a real impact. How would you describe your approach to AI mentorship?

Katia Walsh: My approach is not specific to AI mentorship, but rather overall leadership. I consider myself to be a servant leader, and I see my job as serving the people on my teams, my partners teams, and at the companies that I have the privilege to work for. My job is to remove barriers to help them grow, learn, engage, and mobilize others to succeed. So that extends to AI, but its not limited to that alone.

VentureBeat: Can you tell us about some of the specific initiatives youve launched? I know at Levi Strauss & Co, for example, you recently created a machine learning bootcamp to train more than 100 employees who had no prior machine learning experience, most of them women. Thats amazing.

Walsh: Absolutely. So we are still in the process. We just started our first cohort between April and May, where we took people with absolutely no experience in coding or statistics from all areas of the company including warehouses, distribution centers, and retail stores and sought to make sure we gave people across geographies and across the company the opportunity to learn machine learning and practice that in their day job, regardless of what that day job was.

So we trained the first cohort with 43 people, 63% of whom were women in 14 different locations around the world. And thats very important because diversity comes in so many different ways, including cultural and geographic diversity. And so that was very successful; every single one of those employees completed the bootcamp. And now were about to start our second cohort with 60 people, which will start in September and complete in November.

VentureBeat: Im glad you mentioned those different aspects of diversity, because the industry is full of conversations around diversity, inclusion efforts, and ethical AI some of them more genuine than others. So how does AI mentorship ladder up to all that?

Walsh: I see it as just yet another platform to make an impact. AI is such an exciting field, but it can also be seen as intimidating. Some people dont know if its technology or business, but the answer is both. In fact, AI is actually part of our personal lives as well. One of my goals is to humanize the field of AI so that everyone understands the benefits and feels the freedom and the power to contribute to it. And by feeling that, they will in turn help make it even more diverse. At the end of the day at this point, at least AI is the product of human beings, with all of human beings mindsets, capabilities, and limitations. And so, its also imperative to ensure that when we create algorithms, use data, and deliver digital products, we do our very best to really reflect the world we live in.

VentureBeat: We talked about initiatives, but of course mentorship is also about those everyday mentorship-like interactions, such as with ones manager or an industry connection. How important are these not just for personal development, but also running a business and being part of a team?

Walsh: Thats actually probably the most important stage. Our daily lives revolve around what might be considered the mundane meetings, tasks, assignments, deadlines and thats actually where we can make the most impact. Mentorship is really not about doing something special and extra, but rather making sure that as part of our daily lives and daily responsibilities and jobs, we ensure we think about if were being equitable, fair, and doing everything we can to bring diversity. But it cant be a box to check; it has to become a part of how we think and act every hour in every single day.

VentureBeat: Are there any misconceptions about mentorship you think are important to clear up, or often overlooked aspects of mentorship you think everyone should know about?

Walsh: One thing that comes to mind is this idea that women can only be mentored by other women. Thats actually not the case. And in my own experience, Ive had the great privilege of working with men who have themselves taken the chance on me, given me opportunities, and given me responsibilities even before I felt ready. And I really appreciate that. So everyone can be a mentor to women and all genders including fluid genders regardless of their own gender, job, or role.

VentureBeat: And do you have any advice for everyone, but especially business leaders, about how they can be better mentors? Or what about advice for people looking to be mentored about how to make the most out of those relationships and everyday interactions?

Walsh: Ill address the mentee question first. Ive really been impressed with people who, even at a very young age, have had the courage, incentive, and initiative to reach out and say, I want to learn from you. Can you spend a few minutes with me? I always take the call. So I really encourage people to feel that strength and to take that initiative to reach out to people they think they can learn from. And I encourage those who are mentors to also take that call and to proactively encourage others to stay connected with them. One of the things I did was actually give my cell phone number to everyone in my company. Its not commonly done, but Ive put it in our own town hall chat because I want people to feel that connection. I dont want anyone to feel intimidated by a title or where someone sits in a company. AI, data, and digital are truly transversal. Theyre horizontal and cut across everything in a company. So its part of what I do in my function, but its also part of really wanting to contribute to diversity and mentorship.

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Benefits of Pursuing a Career in Machine Learning In 2022 – Analytics Insight

September 1, 2021 | Author: admin

Machine learning is one of the fastest-growing fields in the world right now. As per the reports, machine learning engineers are in high demand. All ventures currently have a huge number of utilizations in artificial intelligence, which is the essential motivation behind why there is an appeal for jobs in that field. If you are still confused with so many career options, this is high time for you to think about seeking a career in machine learning.

Along with AI, machine learning is the fuel required to power robots. With machine learning, you can control programs that can easily be updated and modified to adjust with new conditions and assignments to finish things rapidly and productively.

Here are a few benefits of pursuing a career in machine learning:

Regardless of the remarkable development in machine learning, the field faces expertise deficiency. If you can fulfill the needs of enormous organizations by acquiring the necessary machine learning skills, you will have a safe future in an innovation that is on the rise.

Machine learning promises to solve issues faced by businesses every day. As a machine learning engineer, you will deal with many challenges and foster arrangements that profoundly affect how organizations and individuals flourish. A job that allows you to work and address various challenges gives the highest satisfaction. Every day, you will get new opportunities to learn and grow in this field. You can observe trends firsthand that will help you boost your relevance in the marketplace, thus augmenting your value to the employer.

Machine learning is still in its early stage, and as the innovation develops and propels, you will have the insight and skill to follow a successful career and build the future for yourself. The average salary of a machine learning engineer is one of the top reasons why machine learning appears to be a worthwhile career to young minds.

Machine learning skills assist you with extending roads in your career. You can also start your career as a data scientist if you acquire relevant machine learning skills (that means you hit the two birds with one stone). Become a valuable asset by acquiring aptitude in the two fields and set out on an astonishing journey loaded up with difficulties, endless opportunities, and knowledge.

What are the other jobs you can get if you pursue a career in machine learning?

Machine learning engineers develop applications and arrangements that mechanize tasks. The majority of these are redundant assignments dependent on condition and activity sets that machines can perform without mistakes, effectively.

A couple of different jobs accessible in the field are ML data scientist, ML computer programmer, senior planner, ML architect, etc. A computer programmer with enough information on Python and the center ML libraries can switch careers into machine learning. Machine learning professional has an upper hand in the field if he knows tech areas like Probability and statistics, system design, ML algorithms and libraries, data modeling, programming languages, and more.

In conclusion, pursuing a career in machine learning is the best idea to become a part of the digital revolution happening in sectors like healthcare, hospitality, banking, logistics, manufacturing, and many more. Having machine learning skills allows you to become the first pick in any sector, which opens the door to various opportunities.

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Research Fellow in Adversarial Machine Learning for Transportation (EPSRC MACRO) job with CRANFIELD UNIVERSITY | 264376 – Times Higher Education (THE)

September 1, 2021 | Author: admin

School/Department School of Aerospace, Transport and ManufacturingBased at Cranfield Campus, Cranfield, BedfordshireHours of work 37 hours per week, normally worked Monday to Friday. Flexible working will be considered.Contract type Fixed term contractFixed Term Period 15 MonthsSalary Full time starting salary is normally in the range of 33,809 to 37,684 per annum, with potential progression up to 47,105 per annumApply by 03/10/2021

Role Description

Cranfield Universitys world-class expertise, large-scale facilities and unrivalled industry partnerships is creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here.

We welcome applications from prospective Research Fellows in Adversarial Machine Learning for Transportation. This exciting role is part of a larger project is funded by EPSRC.

About the School of Aerospace, Transport and Manufacturing

The School of Aerospace, Transport and Manufacturing (SATM) is a leading provider of postgraduate level engineering education, research and technology support to individuals and organisations. At the forefront of aerospace, manufacturing and transport systems technology and management for over 70 years, we deliver multi-disciplinary solutions to the complex challenges facing industry.

About the Role

Our reputation for leading in the field of digital systems: sensor data, communications, machine learning, and reasoning - has been established through more than thirty years of research into this field. We are primarily focused in this project on secure AI/ML for transportation and mobility as a service (MaaS). Our work covers academic provision (MSc and PhD) and research. Research works span from fundamental research and development to single client contract research and development.

As Research Fellow you will contribute to the research activities of the Centre for Autonomous and Cyberphysical Systems, especially concerning the specific activities of: (1) machine learning for transportation sector (especially in mobility as a service), (2) adversarial attack modelling in AI/ML, and (3) co-designing secure AI systems in mobility as a service sector.

About You

You will be expected to collaborate with the existing staff working in the same EPSRC project and the area and have communications and meetings with our collaborators within the university, the industrial / government partners, or in other universities.

You will be educated to doctoral level in a relevant subject and have experience of management research using both qualitative and quantitative methods. With excellent communication skills, you will have expertise in social network analysis and a background in Health & Safety would be an advantage. In return, the successful applicant will have exciting opportunities for career development in this key position, and to be at the forefront of world leading research and education, joining a supportive team and environment.

Our Values

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here. We aim to create and maintain a culture in which everyone can work and study together and realise their full potential.

Diversity and Inclusion

Our equal opportunities and diversity monitoring has shown that women are currently underrepresented within the university and so we actively encourage applications from eligible female candidates. To further demonstrate our commitment to progressing gender diversity in STEM, we are members of WES & Working Families, and sponsors of International Women in Engineering Day.

Flexible Working

We actively consider flexible working options such as part-time, compressed or flexible hours and/or an element of homeworking, and commit to exploring the possibilities for each role. Find out more here.

How to Apply

Please do not hesitate to contact us for further details on E: hr@cranfield.ac.uk. Please quote reference number 3744.

Closing date for receipt of applications: 3 October 2021

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Research Fellow in Adversarial Machine Learning for Transportation (EPSRC MACRO) job with CRANFIELD UNIVERSITY | 264376 - Times Higher Education (THE)

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COVID-19 showed why the military must do more to accelerate machine learning for its toughest challenges – C4ISRNet

September 1, 2021 | Author: admin

As recent events have shown, military decision-making is one of the highest-stakes challenges in the world: Diplomatic relations are at stake; billions of dollars of tax-funded budgets are in the balance; the safety and well-being of thousands of military and civilian personnel around the globe are on the line; and above all, the freedom and liberty of the United States and its more than 330 million citizens must be protected. But with such immense stakes comes an almost unfathomably large amount of related data that must be taken into account. Whether it is managing population health in an increasingly complex and connected world, or managing decisions on the network-centric battlefield, standalone humans are proving insufficient to harness the data, analyze it, and make timely and correct decisions.

Spanning six branches and upward of 1.3 million active duty military personnel on all seven continents, how can all of the data points from dictates from the commander-in-chief to handwritten notes on the deck of an aircraft carrier be taken into account? In matters of national security, speed and reliability in decision-making and avoiding technological surprises or being caught off guard by the nations political rivals require massive real-time analysis and first and second order thinking that includes the complexities of human behavior.

Consider all of the stakes and moving parts facing the leadership at a large domestic military base during the recent COVID-19 pandemic. Concerns of COVID-19 did not just need to consider the base personnel, but also the behavior of the civilians in the surrounding counties, as people from throughout the region, military and civilian contractors alike, were coming and going daily. The information necessary to consider starts with infection and hospitalization rates, but also includes behavior monitoring (and influencing) as well as staying up to date with steps being taken by local, regional and state officials to monitor the virus and limit its spread. With so many moving parts, it is very difficult to stay up to the minute on everything and to determine the right decision with any degree of certainty.

The answer to this guesswork and analysis paralysis lies in the capabilities of artificial intelligence and machine learning. If the military continues to waste too much time with human hours of effort and analysis that could be handled by machines, that could lead to danger and even death of military personnel or civilians. At the heart of complex systems, such as the U.S. military, there is a critical tipping point where the systems are so complex that humans can no longer track them. But AI solutions are capable of delivering up-to-the-minute data modeling, considering all factors at play and second and third order consequences, that can present tangible, data-driven intelligence that takes actions far beyond the limitations of linear human minds. Perhaps the biggest benefit is the confidence to avoid the negative publicity from the podium moment, when asked to justify decisions. Decision-makers can confidently move beyond relying on hunches and instead identify data based on sub-indexes, models from experts, and simulations specific to that day and the circumstances specific to each facility.

When President Biden was recently called onto the carpet to explain the rapid fall of Afghanistan in nine days, he should have had an AI that could at least explain the data, the models and weights that fed the analysis, conclusions and decisions based on the belief that the 300,000 strong Afghan army would be able to hold off the 60,000 Taliban fighters long enough for an orderly withdrawal. Journalists would then be free to question the data sources, the models or the weightings, but not the president, who would be relying on these systems for his judgment. But more importantly, such a system would have certainly predicted this rapid fall in its Monte Carlo distribution of potential outcomes, and would have generated counter measures and cautions.

Without a deeper commitment to AI, the military risks missing out on intelligence that transcends classified, siloed and otherwise restricted information without compromising security. One of the biggest challenges to high-stakes decision-making in the military is silos of classified information, making it difficult or impossible for every party to know every factor that is shaping the situation.

Using AI and machine learning solves this challenge safely. Rather than dumping disparate data from various branches of the military and clearance level into one gigantic data lake, it is possible to leave all the data safely and securely where it is, and train a machine to know and inform the human decision-makers that the data exists. AI is capable of processing not only all of the information in the corpus, but it is also able to know which parties do and do not have clearance to each individual piece of data. In matters of classified information, it can tell different personnel that the information exists, and direct these individuals to the authority qualified to disclose it.

Capabilities like these can be readily applied to large, complex military undertakings, featuring processes, decisions and volumes of information. For instance, when a new aircraft carrier is being built, management requires information in hand-written reports. It is difficult for the naked eye to tell if the project is on time or on budget because of the heavy reliance on human judgment. If any human assessment is just a fraction off, it can massively impact the whole project.

Recent challenges that factor in the vagaries of human behavior illustrated starkly by COVID-19 and the withdrawal from Afghanistan, beg for the rapid analysis and creative input of machine learning systems. From digestion and quantification of countless data points to absorbing and cataloging knowledge of experts who will not always be around to help with predictive modeling of circumstances with dozens of variables, this amplified intelligence is the key to better outcomes.

Richard Boyd is CEO at Tanjo, a machine learning company.

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Podcast: NYU’s Kolm on transaction costs and machine learning – Risk.net

September 1, 2021 | Author: admin

Most forms of post-trade transaction cost analysis only consider the price impact of completed orders. But ignoring partially filled orders which are all too common when trading produces a distorted measure of execution quality.

Depending on what methodologies are used, you might be off by 20% to 30%, relative to the true transaction cost, says Petter Kolm, professor of finance and director of the Mathematics in Finance masters program at NYUs Courant Institute of Mathematical Sciences, and our guest for this episode of Quantcast.

Kolms latest paperwith Nicholas Westray, a visiting researcher in financial machine learning at the Courant Institute, explores the so-called clean-up costs of trades, which they define as the opportunity cost attributed to the part of the order that is unfilled.

Most trading firms use ad hoc techniques to measure the cost of partial fills. The paper proposes a streamlined way to quantify clean-up costs that can be consistently applied to different trading strategies. The setup assumes the market behaves like a propagator model. This allows for the transaction costs of partially filled orders to be modelled as if they were fully executed, capturing the effects on drift of the security as well as the market impact of the trade.

In this podcast, Kolm also discusses his other research interests, including the applications of machine learning and its various branches in finance, one of whichis natural language processing (NLP). Kolm and his team have used NLP to gauge investor sentiment on individual stocks by harvesting signals from financial news. Their research has shown that there is indeed a connection between sentiment and the successive behaviour of the stock.

Kolm is also working on various applications of reinforcement learning, which is becomingincreasingly popular. He is, however, more cautious than other quants about it applicationin finance. While the technique is promising, he warns that prior applications such as the Alpha Go system developed by Googles Deepmind benefited from a large and stable database for training. In finance, quants only have a limited history of prices to work with. Reinforcement learning has had a bit of hype all the cool kids are doing it these days, but I think people are starting to understand and separate hype from reality, he says.

Kolm says his future projects will focus on the application of deep learning and reinforcement learning to optimal execution and the trading of American options, as well as the use of NLP to generate trading signals.

Index

00:00 Intro and transaction cost analysis

02:30 How costly are clean-up costs?

05:10 The problem of quantifying clean-up costs

10:32 Reading financial news with NLP

16:50 Reinforcement learning

22:40 Deep learning and limit order books

26:20 Teaching machine learning techniques

30:20 Future research projects

To hear the full interview, listen in the player above, or download. Future podcasts in our Quantcast series will be uploaded to Risk.net. You can also visit the main page here to access all tracks, or go to the iTunes store orGoogle Podcaststo listen and subscribe.

Now also available on Spotify.

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Playing Catch-Up To The Early Adopters Of Analytics, AI And Machine Learning – Africa.com

September 1, 2021 | Author: admin

By Sarthak Rohal, VP IT services at AlphaCodes

Early adopters of data analytics, Artificial Intelligence (AI) and Machine Learning (ML) tools have found themselves in a position of favour in todays rapidly accelerating digital world. Far from being a point of differentiation, these technologies have become imperative for survival. As businesses that have lagged behind struggle to play catch up, a trusted IT partner becomes a critical business asset, helping organisations adapt and thrive in a new world.

Better decisions, faster

The Covid-19 pandemic has affected all businesses around the world, and recovering from its effects will be a top priority for the remainder of 2021 and beyond. While some businesses struggle with the new reality, others have seen it as an opportunity to improve their data and analytical assets, operationalise, and update their processes.

The key to business success today is the ability to make better decisions faster. This all hinges on the ability to analyse data, put the analytics to work through AI, and then leverage technology to train algorithms that enhance the decision-making process using ML.

With the sheer volume of data available today, it is beyond human ability to gather, analyse and deliver insight in any meaningful timeframe. Crucially, adoption of AI/ML should not be seen as a replacement for human resources, but rather an augmentation of human ability.

The goal should be to use data and analytics to increase revenue, improve efficiency, and respond to customer/market trends, driving better decisions that create a competitive advantage.

Adapt or risk irrelevance

According toGartner, by the end of 2024, 75% of enterprises will operationalise AI, driving a fivefold increase in streaming data and analytics infrastructures.Grand View Researchstates that the global AI market size is expected to grow at a Compound Annual Growth Rate (CAGR) of 42.2% from 2020 to 2027. AMcKinseysurvey reports that, for financial year 2019, 66% of respondents agreed that adoption of AI/ML in their business has helped increase revenue, while 40% cited a decrease in costs with the adoption of AI/ML.

What this all means is that AI/ML is no longer a competitive advantage, but is necessary simply to keep pace with global business. However, it can be challenging to get right, as highlighted by aDeloitte reportthat states that somewhere in the region of 94% of enterprises face problems when it comes to implementing AI.

Getting the foundations right

Before implementing AI in data analytics, organisations need to look at their data and make sure that they have sufficient data points for the AI to process. Without enough data points, AI will inevitably be biased toward a certain outcome, which means it will not provide meaningful analytical insight.

Quality data is essential in reducing noise and bias in the data, which in turn is essential for more accurate outcomes. It also reduces the computational power required by analytics, and speeds the model training process for ML, if data is clean and relevant from the outset.

It is also important to implement AI in the right place. Not everything needs AI to solve a problem, and an indiscriminate approach will reduce both value and impact. Additionally, organisations need to manage the change to maximise adoption and reduce the amount of confusion that may occur.

Partnerships to success

The biggest issue is that using AI in data analytics is not just about applying AI models to the data. It also needs an understanding of the data being captured for the analytics purpose while understanding which models would yield the best results. These are not skills that many enterprises necessarily possess in-house, which is why partnering with a reputable technology provider is key.

Maximising value from AI requires enterprises to focus their efforts on the right business lines with the right AI models. An experienced partner can help organisations understand the nuances of data and assist with gaining meaningful insights to drive business capabilities to a competitive advantage. In addition, a technology partner can help organisations understand which areas of business could optimally benefit from the use of AI.

AI/ML-related solutions will always provide an edge to business decision-makers, as they can simulate thousands of models and iterations and understand the risk and returns from each iteration, which is practically impossible without these next-generation technologies. Playing catch-up is now vital, as organisations that have not yet jumped on the AI/ML bandwagon will find it increasingly difficult to remain competitive.

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Playing Catch-Up To The Early Adopters Of Analytics, AI And Machine Learning - Africa.com

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