We often equate machine learning to fictional scenarios such as those presented in films including the Terminator franchise and 2001: A Space Odyssey. While these are all entertaining stories, the fact of the matter is that this type of artificial intelligence is not nearly as threatening. On the contrary, it has helped to dramatically enhance the overall user experience (UX) and to streamline many online functions (such as common search results) that we take for granted. Machine learning is also making its presence known within the digital gaming community. Without becoming overly technical, what transformations can we expect to witness and how will these impact the experience of the average gaming enthusiast?

Although games such as Pong and Super Mario Bros. were entertaining for their time, they were also quite predictable. This is why so many users have uploaded speed runs onto websites such as YouTube. However, what if a game actually learned from your previous actions? It is obvious that the platform itself would be much more challenging. This concept is now becoming a reality.

Machine learning can also apply to numerous scenarios. It may be used to provide a greater sense of realism with interacting with a role-playing game. It could be employed to offer speech recognition and to recognise voice commands. Machine learning may also be implemented to create more realistic non-playable characters (NPCs).

Whether referring to fast-paced MMORPGs to traditional forms of entertainment including slot games offered by websites such as scandicasino.vip, there is no doubt that machine learning will soon make its presence known.

We can clearly see that the technical benefits associated with machine learning will certainly be leveraged by game developers. However, it is just as important to mention that this very same technology will have a pronounced impact upon the players themselves. This is largely due to how games can be personalised based around the needs of the player.

We are not only referring to common options such as the ability to modify avatars and skins in this case. Instead, games are evolving to the point that they will base their recommendations off of the behaviours of the players themselves. For example, a plot may change as a result of how a player interacts with other characters. The difficulty of a specific level may be automatically adjusted in accordance with the skill of the player. As machine learning and AI both have the ability to model extremely complex systems, the sheer attention to graphical detail within the games (such as character features and backgrounds) will also become vastly enhanced.

We can see that the future of gaming looks extremely bright thanks to the presence of machine learning. While such systems might appear to have little impact upon traditional platforms such as solitaire, there is no doubt that they will still be felt across numerous other genres. So, get ready for a truly amazing experience in the months and years to come!

See the rest here:

How Machine Learning is Set to Transform the Online Gaming Community - Techiexpert.com - TechiExpert.com

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.

See the original post here:

Current and future regulatory landscape for AI and machine learning in the investment management sector - Lexology

Strategic growth, latest insights, developmental trends in Global & Regional Machine Learning Courses Market with post-pandemic situations are reflected in this study. End to end Industry analysis from the definition, product specifications, demand till forecast prospects are presented. The complete industry developmental factors, historical performance from 2015-2027 is stated. The market size estimation, Machine Learning Courses maturity analysis, risk analysis, and competitive edge is offered. The segmental market view by types of products, applications, end-users, and top vendors is stated. Market drivers, restraints, opportunities in Machine Learning Courses industry with the innovative and strategic approach is offered. Machine Learning Courses product demand across regions like North America, Europe, Asia-Pacific, South and Central America, Middle East, and Africa is analyzed. The emerging segments, CAGR, revenue accumulation, feasibility check is specified.

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Global Machine Learning Courses Market Research Report 2015-2027 of Major Types, Applications and Competitive Vendors in Top Regions and Countries -...

Artificial intelligence can be used to diagnose cancer, predict suicide, and assist in surgery. In all these cases, studies suggest AI outperforms human doctors in set tasks. But when something does go wrong, who is responsible?

Theres no easy answer, says Patrick Lin, director of Ethics and Emerging Sciences Group at California Polytechnic State University. At any point in the process of implementing AI in healthcare, from design to data and delivery, errors are possible. This is a big mess, says Lin. Its not clear who would be responsible because the details of why an error or accident happens matters. That event could happen anywhere along the value chain.

Design includes creation of both hardware and software, plus testing the product. Data encompasses the mass of problems that can occur when machine learning is trained on biased data, while deployment involves how the product is used in practice. AI applications in healthcare often involve robots working with humans, which further blurs the line of responsibility.

Responsibility can be divided according to where and how the AI system failed, says Wendall Wallace, lecturer at Yale Universitys Interdisciplinary Center for Bioethics and the author of several books on robot ethics. If the system fails to perform as designed or does something idiosyncratic, that probably goes back to the corporation that marketed the device, he says. If it hasnt failed, if its being misused in the hospital context, liability would fall on who authorized that usage.

Surgical Inc., the company behind the Da Vinci Surgical system, has settled thousands of lawsuits over the past decade. Da Vinci robots always work in conjunction with a human surgeon, but the company has faced allegations of clear error, including machines burning patients and broken parts of machines falling into patients.

Some cases, though, are less clear-cut. If diagnostic AI trained on data that over-represents white patients then misdiagnoses a Black patient, its unclear whether the culprit is the machine-learning company, those who collected the biased data, or the doctor who chose to listen to the recommendation. If an AI program is a black box, it will make predictions and decisions as humans do, but without being able to communicate its reasons for doing so, writes attorney Yavar Bathaee in a paper outlining why the legal principles that apply to humans dont necessarily work for AI. This also means that little can be inferred about the intent or conduct of the humans that created or deployed the AI, since even they may not be able to foresee what solutions the AI will reach or what decisions it will make.

The difficulty in pinning the blame on machines lies in the impenetrability of the AI decision-making process, according to a paper on tort liability and AI published in the AMA Journal of Ethics last year. For example, if the designers of AI cannot foresee how it will act after it is released in the world, how can they be held tortiously liable?, write the authors. And if the legal system absolves designers from liability because AI actions are unforeseeable, then injured patients may be left with fewer opportunities for redress.

AI, as with all technology, often works very differently in the lab than in a real-world setting. Earlier this year, researchers from Google Health found that a deep-learning system capable of identifying symptoms of diabetic retinopathy with 90% accuracy in the lab caused considerable delays and frustrations when deployed in real life.

Despite the complexities, clear responsibility is essential for artificial intelligence in healthcare, both because individual patients deserve accountability, and because lack of responsibility allows mistakes to flourish. If its unclear whos responsible, that creates a gap, it could be no one is responsible, says Lin. If thats the case, theres no incentive to fix the problem. One potential response, suggested by Georgetown legal scholar David Vladeck, is to hold everyone involved in the use and implementation of the AI system accountable.

AI and healthcare often work well together, with artificial intelligence augmenting the decisions made by human professionals. Even as AI develops, these systems arent expected to replace nurses or automate human doctors entirely. But as AI improves, it gets harder for humans to go against machines decisions. If a robot is right 99% of the time, then a doctor could face serious liability if they make a different choice. Its a lot easier for doctors to go along with what that robot says, says Lin.

Ultimately, this means humans are ceding some authority to robots. There are many instances where AI outperforms humans, and so doctors should defer to machine learning. But patient wariness of AI in healthcare is still justified when theres no clear accountability for mistakes. Medicine is still evolving. Its part art and part science, says Lin. You need both technology and humans to respond effectively.

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When AI in healthcare goes wrong, who is responsible? - Quartz

According to a new report by PMMI Business Intelligence, artificial intelligence (AI) and machine learning is the area of automation technology with the greatest capacity for expansion. This technology can optimize individual processes and functions of the operation; manage production and maintenance schedules; and, expand and improve the functionality of existing technology such as vision inspection.

While AI is typically aimed at improving operation-wide efficiency, machine learning is directed more toward the actions of individual machines; learning during operation, identifying inefficiencies in areas such as rotation and movement, and then adjusting processes to correct for inefficiencies.

The advantages to be gained through the use of AI and machine learning are significant. One study released by Accenture and Frontier Economics found that by 2035, AI-empowered technology could increase labor productivity by up to 40%, creating an additional $3.8 trillion in direct value added (DVA) to the manufacturing sector.

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However, only 1% of all manufacturers, both large and small, are currently utilizing some form of AI or machine learning in their operations. Most manufacturers interviewed said that they are trying to gain a better understanding of how to utilize this technology in their operations, and 45% of leading CPGs interviewed predict they will incorporate AI and/or machine learning within ten years.

A plant manager at a private label SME reiterates AI technology is still being explored, stating: We are only now talking about how to use AI and predict it will impact nearly half of our lines in the next 10 years.

While CPGs forecast that machine learning will gain momentum in the next decade, the near-future applications are likely to come in vision and inspection systems. Manufacturers can utilize both AI and machine learning in tandem, such as deploying sensors to key areas of the operation to gather continuous, real-time data on efficiency, which can then be analyzed by an AI program to identify potential tweaks and adjustments to improve the overall process.

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And, the report states, that while these may appear to be expensive investments best left for the future, these technologies are increasingly affordable and offer solutions that can bring measurable efficiencies to smart manufacturing. In the days of COVID-19, gains to labor productivity and operational efficiency may be even more timely.

To access this FREE report and learn more about automation in operations, download below.

Source: PMMI Business Intelligence, Automation Timeline: The Drive Toward 4.0 Connectivity in Packaging and Processing

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Is Wide-Spread Use of AI & Machine Intelligence in Manufacturing Still Years Away? - Automation World

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.

Excerpt from:

How do we know AI is ready to be in the wild? Maybe a critic is needed - ZDNet

In its latest keynote address headed by CEO Tim Cook, Apple its new A14 bionic chip, a 5 nm ARM based chipset.

This System on a Chip (SoC) from Apple is expected to power iPhone 12 and iPad Air (2020) models. The chipset integrates around 11.8 billion transistors.

For over a decade, Apples world-class silicon design team has been building and refining Apple SoCs. Using these designs Apple has been able to develop the latest iPhone, iPad and Apple Watch that are the industry leaders in terms of class and performance. In June of 2020, Apple announced that it will transition the Mac to its custom silicon to offer better technological performance.

Now, Apple Silicon is basically a processor made in-house akin to what is powering the iPhone and iPad family of devices. This ARM move will result in ditching their reliance on Intel chipsets for Future Macs. This transition to silicon will also establish a common architecture across all Apple products, making it far easier for developers to write and optimize their apps for the entire ecosystem. In fact, developers can now start focusing on updating their applications to take advantage of the enhanced capabilities of the Apple silicon.

Along with this Apple also introduced mac0S Big Sur earlier this year, which will be the next major macOS release (version 11.0) and includes technologies that will facilitate a smooth transition to the Apple silicon experience. This will be the first time where developers will be able to make their iOS and iPad OS apps available on the Mac without modifications. The Apple silicon powered Macs will offer industry leading performance per watt and higher performance GPUs. To help developers get accustomed to the new transition, Apple is also launching the Universal App QuickStart Program to guide developers through the entire transition.

Apple plans to ship the new Mac by the end of the year and complete the transition in about two years. This being said Apple will continue to release new versions for Intel-based Mac for years to come.

Apple has been explicit about how serious they are about machine learning-based SoC. Apple A14 includes second-generation machine learning accelerators in the CPU for 10 times faster machine learning calculations. The combination of the new Neural Engine, machine learning accelerators, advanced power management, unified memory architecture and the Apple high-performance GPU enables powerful on-device experiences for image recognition, natural language learning, analysing motion, and maybe a machine learning enabled GPS!

According to a recent patent application by Apple , they have been working on a technology that implements a system for estimating the device location based on a global positioning system consisting of a Global Navigation Satellite System (GNSS) satellite, and receives a set of parameters associated with the estimated position. The processor is further configured to apply the set of parameters and the estimated position to a machine learning model that has been trained on a position relative to the satellite. The estimated position and output of the model is then provided to a Kalman filter for more accurate location.

This technology may be significantly better than what a mobile device alone can perform in most non-aided mode(s) of operation. Apples patent to improve GPS in the upcoming 5G era might give them an advantage over existing resources.

Apples move to its own ARM chips comes just as the company unveils macOS version 11.0 (Big Sur). That means ARM based Mac computers will continue to run macOS instead of switching to iOS 14, similar to the approach taken with existing Windows laptops that use Qualcomm ARM based processors. Apple apparently has its hardware and software team working together, given that they have found a way for all their applications functioning seamless from day one of the launch, through Rosetta 2 acting as an emulator and a translator that will allow Intel-made apps to run on Silicon-powered devices.

Moreover, the Apple ecosystem acts as the catalyst for innovation in the company and is not limited to the hardware and software products, but also around its services.

Putting a foot forward in that direction is the Apple One Subscription.

Apple with its calm dignity, diligent market study and unflinching courage to innovate has taken its own time to come up with their strategic silicon move. Apple stayed focused on its long term goals instead of following the hype, trends and gimmicks set out by its competitors to gain customer attention. This ability to think differently is a driving force behind their success.

And owing to the current state of affairs Apple has played it relatively safe this year, sticking to their core offerings. We can expect an exciting iPhone, iMac and MacOS launch later this year.

Lets gear up for another round of innovation sponsored by Apple.

Continued here:

Solving the crux behind Apple's Silicon Strategy - Medium

The uses of artificial intelligence and machine learning continue to expand, with one of the more recent implementations being video processing. A new method can fill in frames to smooth out the appearance of the video, which [LegoEddy] was able to use this in one of his animated LEGO movies with some astonishing results.

His original animation of LEGO figures and sets was created at 15 frames per second. As an animator, he notes that its orders of magnitude more difficult to get more frames than this with traditional methods, at least in his studio. This is where the artificial intelligence comes in. The program is able to interpolate between frames and create more frames to fill the spaces between the original. This allowed [LegoEddy] to increase his frame rate from 15 fps to 60 fps without having to actually create the additional frames.

While weve seen AI create art before, the improvement on traditionally produced video is a dramatic advancement. Especially since the AI is aware of depth and preserves information about the distance of objects from the camera. The software is also free, runs on any computer with an appropriate graphics card, and is available on GitHub.

Thanks to [BaldPower] for the tip!

See the original post:

Boost Your Animation To 60 FPS Using AI - Hackaday

Despite the pandemic, data scientists remain to be one of the most in-demand jobs. Here we list down 50 latest job openings for data science and analyst positions in cities such as Bangalore, Mumbai, Hyderabad, Pune and more, from last week.

(The jobs are sorted according to the years of experience required).

Location: Hyderabad

Skills Required: Machine learning and statistical models, big data processing technologies such as Hadoop, Hive, Pig and Spark, SQL, etc.

Apply here.

Location: Bangalore

Skills Required: Mathematical modelling using biological datasets, statistical and advanced data analytics preferably using R, Python and/or JMP, hands-on experience in data modelling, data analysis and visualisation, database systems like Postgres, MySQL, SQLServer, etc.

Apply here.

Location: Bangalore

Skills Required: Quantitative analytics or data modelling, predictive modelling, machine learning, clustering and classification techniques, Python, C, C++, Java, SQL, Big Data frameworks and visualisation tools like Cassandra, Hadoop, Spark, Tableau, etc.

Apply here.

Location: Bangalore

Skills Required: Advanced analytics, machine learning, AI techniques, cloud-based Big Data technology, Python, R, SQL, etc.

Apply here.

Location: Thiruvananthapuram, Kerala

Skills Required: Data mining techniques, statistical analysis, building high-quality prediction systems, etc.

Apply here.

Location: Bangalore

Skills Required: Advanced ML, DL, AI, and mathematical modelling and optimisation techniques, Python, NLP, TensorFlow, PyTorch, Keras, etc.

Apply here.

Location: Bangalore

Skills Required: Java, Python, R, C++, machine learning, data mining, mathematical optimisation, simulations, experience in e-commerce or supply chain, computational, programming, data management skills, etc.

Apply here.

Location: Bangalore

Skills Required: Statistics, Machine Learning, programming skills in various languages such as R, Python, etc., NLP, Matlab, linear algebra, optimisation, probability theory, etc.

Apply here.

Location: Bangalore

Skills Required: Knowledge of industry trends, R&D areas and computationally intensive processes (e.g. optimisation), Qiskit, classical approaches to machine learning, etc.

Apply here.

Location: Bangalore

Skills Required: Java, C++, Python, natural language processing systems, C/C++, Java, Perl or Python, statistical language modelling, etc.

Apply here.

Location: Khed, Maharashtra

Skills Required: Statistical computer languages like R, Python, SQL, machine learning techniques, advanced statistical techniques and concepts, etc.

Apply here.

Location: Bangalore

Skills Required: Foundational algorithms in either machine learning, computer vision or deep learning, NLP, Python, etc.

Apply here.

Location: Hyderabad

Skills Required: SQL CQL, MQL, Hive, NoSQL database concepts & applications, data modelling techniques (3NF, Dimensional), Python or R or Java, statistical models and machine learning algorithms, etc.

Apply here.

Location: Anekal, Karnataka

Skills Required: Machine Learning, deep learning-based techniques, OpenCV, DLib, Computer Vision techniques, TensorFlow, Caffe, Pytorch, Keras, MXNet, Theano, etc.

Apply here.

Location: Vadodara, Gujarat

Skills Required: Large and complex data assets, design and build explorative, predictive- or prescriptive models, Python, Spark, SQL, etc.

Apply here.

Location: Remote

Skills Required: Machine Learning & AI, data science Python, R, design and develop training programs, etc.

Apply here.

Location: Bangalore

Skills Required: Integrating applications and platforms with cloud technologies (i.e. AWS), GPU acceleration (i.e. CUDA and cuDNN), Docker containers, etc.

Apply here.

Location: Bangalore

Skills Required: ETL developer, SQL or Python developer, Netezza, etc.

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50 Latest Data Science And Analytics Jobs That Opened Last Week - Analytics India Magazine

An unlikely scandal engulfed the British government last month. After COVID-19 forced the government to cancel the A-level exams that help determine university admission, the British education regulator used an algorithm to predict what score each student would have received on their exam. The algorithm relied in part on how the schools students had historically fared on the exam. Schools with richer children tended to have better track records, so the algorithm gave affluent students even those on track for the same grades as poor students much higher predicted scores. High-achieving, low-income pupils whose schools had not previously performed well were hit particularly hard. After threats of legal action and widespread demonstrations, the government backed down and scrapped the algorithmic grading process entirely. This wasnt an isolated incident: In the United States, similar issues plagued the International Baccalaureate exam, which used an opaque artificial intelligence system to set students' scores, prompting protests from thousands of students and parents.

These episodes highlight some of the pitfalls of algorithmic decision-making. As technology advances, companies, governments, and other organizations are increasingly relying on algorithms to predict important social outcomes, using them to allocate jobs, forecast crime, and even try to prevent child abuse. These technologies promise to increase efficiency, enable more targeted policy interventions, and eliminate human imperfections from decision-making processes. But critics worry that opaque machine learning systems will in fact reflect and further perpetuate shortcomings in how organizations typically function including by entrenching the racial, class, and gender biases of the societies that develop these systems. When courts and parole boards have used algorithms to forecast criminal behavior, for example, they have inaccurately identified Black defendants as future criminals more often than their white counterparts. Predictive policing systems, meanwhile, have led the police to unfairly target neighborhoods with a high proportion of non-white people, regardless of the true crime rate in those areas. Companies that have used recruitment algorithms have found that they amplify bias against women.

But there is an even more basic concern about algorithmic decision-making. Even in the absence of systematic class or racial bias, what if algorithms struggle to make even remotely accurate predictions about the trajectories of individuals' lives? That concern gains new support in a recent paper published in the Proceedings of the National Academy of Sciences. The paper describes a challenge, organized by a group of sociologists at Princeton University, involving 160 research teams from universities across the country and hundreds of researchers in total, including one of the authors of this article. These teams were tasked with analyzing data from the Fragile Families and Child Wellbeing Study, an ongoing study that measures various life outcomes for thousands of families who gave birth to children in large American cities around 2000. It is one of the richest data sets available to researchers: It tracks thousands of families over time, and has been used in more than 750 scientific papers.

The task for the teams was simple. They were given access to almost all of this data and asked to predict several important life outcomes for a sample of families. Those outcomes included the childs grade point average, their grit (a commonly used measure of passion and perseverance), whether the household would be evicted, the material hardship of the household, and whether the parent would lose their job.

The teams could draw on almost 13,000 predictor variables for each family, covering areas such as education, employment, income, family relationships, environmental factors, and child health and development. The researchers were also given access to the outcomes for half of the sample, and they could use this data to hone advanced machine-learning algorithms to predict each of the outcomes for the other half of the sample, which the organizers withheld. At the end of the challenge, the organizers scored the 160 submissions based on how well the algorithms predicted what actually happened in these peoples lives.

The results were disappointing. Even the best performing prediction models were only marginally better than random guesses. The models were rarely able to predict a students GPA, for example, and they were even worse at predicting whether a family would get evicted, experience unemployment, or face material hardship. And the models gave almost no insight into how resilient a child would become.

In other words, even having access to incredibly detailed data and modern machine learning methods designed for prediction did not enable the researchers to make accurate forecasts. The results of the Fragile Families Challenge, the authors conclude, with notable understatement, raise questions about the absolute level of predictive performance that is possible for some life outcomes, even with a rich data set.

Of course, machine learning systems may be much more accurate in other domains; this paper studied the predictability of life outcomes in only one setting. But the failure to make accurate predictions cannot be blamed on the failings of any particular analyst or method. Hundreds of researchers attempted the challenge, using a wide range of statistical techniques, and they all failed.

These findings suggest that we should doubt that big data can ever perfectly predict human behavior and that policymakers working in criminal justice policy and child-protective services should be especially cautious. Even with detailed data and sophisticated prediction techniques, there may be fundamental limitations on researchers' ability to make accurate predictions. Human behavior is inherently unpredictable, social systems are complex, and the actions of individuals often defy expectations.

And yet disappointing as this may be for technocrats and data scientists, it also suggests something reassuring about human potential. If life outcomes are not firmly pre-determined if an algorithm, given a set of past data points, cannot predict a persons trajectory then the algorithms limitations ultimately reflect the richness of humanitys possibilities.

Bryan Schonfeld and Sam Winter-Levy are PhD candidates in politics at Princeton University.

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Algorithms may never really figure us out thank goodness - The Boston Globe