Here is the list of the top 10 AI professors who help their students excel in technology.

Artificial intelligenceand other disruptive technologies likemachine learning,natural language processing, data analytics, cloud computing, etc. are created by humans to make life easier. They are mostly human-based, which means they resemble or work like us. The trigger behind all the technological developments inartificial intelligence advances and influences speech recognition, visual perception, language identification, decision making, etc. Further, the efforts are turned to machines that we use in our daily life. To scholar disruptive technologies, aspirants need clear people who can provide guidance and help them achieve their goals. That is whatartificial intelligence professorsdo in colleges. Artificial intelligenceprofessors in Indiaand elsewhere elaborate their students on technology and motivate them to achieve big things in life. Analytics Insight has listed the top 10artificial intelligence professorswho offer the right opportunity to students and help them excel in their interested fields.

Balaraman Ravindran is the Mindtree Faculty Fellow and a professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Madras. He also heads the Robert Bosch Centre for Data Science and AI at the IITM. Ravindran holds a PhD in computer science from the University of Massachusetts, Amherst. Currently, his research interests are centred on learning from and through interactions and span the area of data mining, social network analysis, and reinforcement learning. Ravindran has published over 100 papers in journals and conferences, including premier venues such as ICML, AAAI, IJCAI, etc.

LinkedIn-Balaraman Ravindran

Umesh Bellur is the Head of the Department of Computer Science at the Indian Institute of Technology Bombay (IITB). He holds a B.E in Electronics Engineering from Bangalore University and secured his PhD in Computer Engineering from Syracuse University, New York. Bellur is interested in anything distributed around virtualization and cloud computing where he is looking at problems in derivative clouds and serverless computing, VM provisioning, placement, and migration. As an educational professional, he is skilled in distributed systems, Linux, Algorithms, C/C++/Python, and cloud computing.

LinkedIn-Umesh Bellur

Pulak Ghosh is IIMB Chair of Excellence and Professor of Decision Sciences at the Indian Institute of Management, Bangalore (IIMB). Ghosh specializes in intersections of big data, machine learning, artificial intelligence, and its use in economics, finance, policy, and social value creation. Before joining IIMB, he served as Associate Director, Novartis Pharmaceuticals. He also held teaching jobs as Assistant Professor at Georgia State University ad Associate Professor at Emory University, USA. Ghosh also served in the Advisory group of big data at the United Nations (UN) Global Pulse, a big data initiative by the UN and the knowledge commission of UNESCO-MGEIP.

LinkedIn-Pulak Ghosh

Sanjiva Prasad is the professor and head of the Department of Computer Science and Engineering, Indian Institute of Technology Delhi (IITD). He received his B.Tech in Computer Science from IIT Kanpur in 1985 and his MS and PhD in Computer Science from SUNY, Stony Brook, USA. Prasads research interests are in the broad area of formal methods, programming language and their semantics, concurrency theory, verification, proof theory, mobile computation, formal foundations of networks, including IoT and SDN, security. He is specially focused on information flow and formal methods for reconfiguration architecture.

LinkedIn-Sanjiva Prasad

Pushpak Bhattacharyya is a professor of Computer Science and Engineering Department IIT Bombay. His area of research is natural language processing and machine learning. He currently holds the Major Bhagat Singh Rekhi Chair Professorship of IIT Bombay. Before joining IITB, he worked as the director of IIT Patna and served as the President of the Association of Computational Linguistics. Due to his interest in NLP and machine learning, Bhattacharyya has published more than 350 research papers on the topics. He also authored a textbook called Machine Translation.

LinkedIn-Pushpak Bhattacharyya

Suyash P. Awate is the Associate Professor of the Computer Science and Engineering Department, IIT Bombay. His interests are in image analysis, medical image computing, machine learning, computer vision, statistical modelling, and inference. Awate has done research on statistical shape analysis, kernel methods, image classification, image quantification, image reconstruction, statistical analysis of the cemetery of the human brain cortex, image restoration and denoising, image segmentation, and image registration. He has authored and published many papers on image analysis technology.

Chiranjib Bhattacharyya is the Professor and Chair of the Department of Computer Science and Automation at Indian Institute of Science. His interests are centred on machine learning, convex optimization, and bioinformatics. He holds BE and ME degrees, both in Electrical Engineering, from Jadavpur University and the Indian Institute of Science. Bhattacharyya completed his PhD from the Department of Computer Science and Automation, IISc. Before joining IISc, he worked as a postdoctoral fellow at UC Berkeley. Bhattacharyya has authored papers in leading journals and conferences in machine learning.

Susheela Devi is a Principal Research Scientist in the Department of Computer Science and Automation at IISc, Bengaluru. She has a keen interest in the field of pattern recognition, data mining, and soft computing. In her role, she educates students on data structures and algorithms, computational methods of optimization, artificial intelligence, intelligent agents, topic in pattern recognition, data mining for proficiency, algorithms and programming, and soft computing.

LinkedIn- V. Susheela Devi

Sudeept Mohan is the Head of the Department of Computer Science and Information Systems at Birla Institute of Technology and Science, Pilani. His interest centres on intelligent control and robotics. Mohan has spent all his learning years at BITS Pilani. He holds degrees in B.E in Electrical & Electronics, M.Sc in Physics, M.E in Electronics and Control, and PhD in Control of Robot Manipulators, all from BITS Pilani.

Rajeswari Sridhar is the Head of the Department of Computer Science and Engineering at the National Institute of Technology, Trichy. Her area of interest includes data structures and algorithms, compilers, machine learning and deep learning, artificial intelligence, natural language processing, data science and analytics, and cloud computing. Sridhar secured her B.E degree in ECE from Government College of Technology, Coimbatore and M.S in Computer Science from City University, USA. She also holds a PhD in CSE from Anna University, Chennai.

LinkedIn-Rajeswari Sridhar

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Wizards of Tech: Top Artificial Intelligence Professors in India - Analytics Insight

Ayear ago, most stakeholders in the education process were blindsided by the realization that closure of educational institutions were to be for an indefinite period. Accepting that education had to be carried out in the virtual medium, there was overnight a flood of online platforms catering to the newfound necessity of contacting the student community and keeping a semblance of education going under severe socio-economic conditions. The immediate crisis that came to the fore tore across the student community and the proverbial divide between the haves and have-nots was put into the sharpest focus, unseen across generations.

Dependence on the internet and suitable and expensive mobility equipment immediately threw almost half of the nations student population out of the educational system. Compounded by the fact that sudden closure of establishments rendered millions without the basic necessities of existence, it was a foregone conclusion that poverty would push another 30 per cent of the student population out of the reach of educational institutions. Effectively, within a month, the active student population of the country shrunk by 80 per cent and by June 2020 it was a foregone conclusion that most of the dropouts would be unable to return to the fold in the near future.

For a country whose policy makers and stakeholders had been exerting themselves continuously to bring down student drop-outs to single digit percentages, the sudden turn of events annulled decades of painstaking reach-out to bring in inclusive education across the country. Also, the sudden closure of educational institutions and the consequent mass dropouts across the country rendered the newly framed National Educational Policy practically infructuous in the short and medium run. The crisis is not limited to our country with UNESCO figures claiming a total drop-out number of 1.6 billion children on a modest estimate.

Such dropout figures are not limited to south Asia or sub-Saharan Africa but encompass countries as diverse and advanced as the middle-income economies of Brazil, Argentina, Mexico, Spain and Portugal. Clearly, Covids impact on education is not merely universal but is also incomprehensible in its extent, range and depth. In order to frame recovery policies, comprehensive data-mining efforts have been planned and are under various stages of implementation. The chief among them is the UNESCO-UNICEFWorld Bank joint survey on National Educational Response to Covid-19 School Closures and the Covid-19 Global Educational Recovery Tracker, a new tool developed in partnership by Johns Hopkins Universitys e-School+ Initiative, UNICEF and the World Bank.

The primary focus of this global outreach is to monitor school reopening and aid recovery planning efforts in more than 200 countries and territories. Initial data, as of April 2021 reveal that more than 168 million children globally have been shut out of any form of in-person learning for almost an entire year. Alarmingly, this figure does not include the children who have dropped out of school entirely as a result of the pandemic. Covid-induced educational disruptions in India are increasingly bringing out other equally serious manifestations of the harm inflicted by the pandemic on education.

While active involvement of the mobile service providers and equipment manufacturers for communication devices have mitigated the hardship to the student community to a large extent by increasing the coverage of mobile services and availability of cheaper equipment, it is increasingly becoming clear that education and assessment in the virtual medium are exposing their own patterns of disruptions, most of which threaten to expand to life-long cognitive debilities.

In the Indian context, the greatest challenge to education has come from the exclusive but unavoidable reliance on the virtual medium. While personal proximities and close physical monitoring of progress has been the hallmark of our education from time immemorial, virtual teaching has significantly eroded the role of the teaching community from exercising effective control of their instructions, with most teachers still struggling to know the number of students actively participating in the virtual instruction. The scenario with online examinations and evaluation is more precarious.

While we have adopted a blended evaluation mode, closely resembling the western concept of the open-book examination, it is commonly seen that the evaluation has reduced itself to students copying answers at home from books or the web and transferring them to institutions for evaluation. Needless to say, they end up with marks which are at the upper end of the spectrum. Clearly, such evaluation and the inflated marks they carry are not only poor indicators of student proficiency, but may actually turn counterproductive for such students in the long run with the stigma of having been evaluated under such farcical circumstances sticking to them before future employers and institutional job providers.

This is compounded by the huge cognitive and academic deficiencies that the student community is facing with most stakeholders agreeing that effective online instruction is easier said than done. The challenge to the teaching community is no less profound. Within the space of a year, teachers have been burdened not only with managing online classes and evaluation but are constantly weighted down by a constant stream of online teaching products competing for their attention, most being repetitive applications and redundant online methodologies.

Such inane teaching technologies have significantly diverted the energy of the teaching and educational administrative community; such energy could have been better used in augmenting effective instructional and evaluation efforts. Under such challenging circumstances, the need of the hour is to garner a talent pool of stakeholders to steer education at such a critical time. Clearly, conventional policy bureaucratese would serve little purpose. Also, Indian educational contexts and conditions are radically different from international scenarios and we would not have the luxury of adopting foreign strategies which would face strong headwinds in micro-level educational contexts in our country.

Mitigating the severe setback to education and rehabilitating the compromised education of millions of students across the country through appropriate remedies are the only ways in which damage to our student community can be minimized. A year into the pandemic and the long-term impact on education is already threatening an entire generation of our youth through poor skill attainment and compromised instruction. This shall directly impact their ability to attain proficiencies for employability. The talent pool bottleneck this crisis shall create would threaten and choke the services and manufacturing sectors in the long run, thereby impacting the economy as a whole. The earlier this long-term danger is understood and addressed, the better it would be for our community.

(The writer is Assistant Professor of English, Pratilata Waddedar Mahavidyalaya, West Bengal)

Excerpt from:

Challenge to education - The Statesman

Posted by Paul Moore on 30th April 2021

The future of autonomy in mining is set to include much more open and interoperable platforms than exist today. And the evolution of fleet management systems or FMS as they are known in the industry is a key part of that enabling mining customers to get the elusive single source of the truth across the on the ground reality of mixed fleets and contractor machines. Ahead of an in-depth article on the future of FMS in the May 2021 edition of IM, Editorial Director Paul Moore caught up with Wencos Reid Given, Senior Product Manager & Patrick Ligthart, Principal Product Manager to explore the topic of Open Autonomy and where the latest FMS functionality

Q How important is the FMS system to achieving true open autonomy and how has your open autonomy approach been received so far by the mining industry?

RG FMS is only one component in achieving Open Autonomy. Whats more important for Open Autonomy than any individual component is establishing open standards that break down the current closed approach and, instead, allow customers to mix and match components from their preferred vendors. This way, customers can choose technologies that drive the best ROI for them in their unique circumstances the most efficient trucks, the smartest and safest autonomous drivers, the FMS most tightly integrated with their systems and processes, and so on. Now that weve introduced this vision of Open Autonomy, its gathering a lot of momentum. Wenco and other industry contributors are making progress on ISO 23275 and proposing new standards for other components. Were also working with several customers and industry thought leaders to bring the Open Autonomy approach commercially to market. Non-traditional mining OEMs are especially excited about the prospects of Open Autonomy, as it gives them a path to enter our market. Open Autonomy enables new mining strategies to become profitable, such as swarm mining a tactic that uses trucks previously considered too small for our industry. As a result, were being engaged by companies from the automotive, long-distance trucking, and military industries looking to apply their autonomy technologies to mining use cases.

Q Is FMS interoperability still an issue in mining in enabling mines to access the technologies that they want to use; what progress is Wenco making in this regard?

PL Interoperability can still prove a challenge when mines rely on critical technologies that remain siloed. Without the ability to exchange data freely between their operational systems, mines struggle to optimise their decision-making that is, have the right decisions made at the right time by the right person. Mines typically have vast volumes of data to support these decisions, but theyre not treating their operational data as the asset it is. Too much data is left untapped in huge databases with only limited connection to other systems at best. Wenco has always taken care to make our database as accessible as possible, allowing mines to turn their data into actionable intelligence with the least amount of overhead. Were continuing to expand our capabilities in this area with our own technologies and with other vendors in the pit-to-port landscape. We currently have projects working to integrate solutions from various OEMs and aftermarket vendors that enforce stricter material compliance, facilitate ISA-95 automation, and strengthen management of unexpected events using cameras and other sensors. All these projects are aimed at extending our interoperability with others to help mining customers extract more unrealised value.

Q Automation aside, what role do todays FMS systems in enabling highest levels of mining efficiency such as high precision and asset health systems?

RG The real power of any data system comes from its improved decisions. FMS and other operational mine technologies deliver greater control, yes, but they also create synergies and enable more robust insights than are possible otherwise. The contextual data about equipment behaviour that comes from an FMS allows these other technologies to make much more accurate decisions around ore/waste determination (and, therefore, enable selective mining) and predictive maintenance. It works both ways as well. With the FMS serving as the orchestrator for in-pit operations, data from high-precision and asset health systems gives dispatchers and mine controllers the ability to act on deviations that occur within a shift. For example, access to messages and events from third-party systems allows our FMS to make smarter assignments, such as diverting a truck that was in the process of being loaded when a ground-engaging tool alarm was generated away from the crusher.

Q What potential is there in teaming Wencos FMS technology with Hitachi tech such as ConSite to achieve best results for customers?

PL Wenco is creating ConSite Mine for Hitachi Construction Machinery (HCM) on a digital IoT platform, to be delivered this year, with the intention of integrating Wenco, HCM, and third-party technologies into solutions that deliver the best results for customers. Of course, this platform will ultimately integrate Wenco FMS capabilities with advanced technologies from Hitachi and other ecosystem partners. The digital IoT platform being created by Wenco on behalf of HCM is designed to serve as a one-stop shop for capture, storage, processing, exchange, and analysis of data through an open architecture and with common interfaces. This digital IoT platform is not only intended for our current customer base of Tier 1 and Tier 2 mines, but also for customers in markets such as quarries, construction, and beyond who understand the efficiency gains possible from digital technologies. There is huge demand from these sectors for an integrated, cloud-based fleet management solution that isnt tied to a specific location. This platform will be able to deliver certain cross-functionalities that are difficult to establish with single purpose on-premises technologies, while also bringing capabilities normally reserved for top-tier mining companies to a whole new series of customers. It also offers new ways to scale and manage FMS functions in a much more tailored way, so our customers can invest discretely in solutions that really drive their operation forward.

Q How can long term existing Wenco FMS customers benefit from the latest functionalities how easy is it for them to upgrade or is it effectively like putting in an entirely new system?

RG Were very careful about ensuring our long-term customers can take advantage of our latest functionalities. Its top of mind for us as we build our new solutions, including our digital IoT platform. Our philosophy is to make the transition to our new platform as seamless as possible as we gradually release new capabilities. We know the impact a hardware replacement can have on our customers, so were very careful about designing our technology to avoid cases where a hardware upgrade is required to derive optimal value. We obviously strive to avoid the change management requirements that come when a new solution is implemented. As such, our pathway to a new platform is much more evolutionary, rather than revolutionary.

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Wenco expands on a more open digital mining future - in architecture, analytics and autonomy - International Mining

PHOTO:Jin xc | unsplash

This week a person I never met contacted me on Twitter to ask if I had done any further research on the integration of two subjects big data and knowledge management based on a 5-1/2-year-old article I wrote on the subject.

I had to admit I had not really done much more research on the topic. At the time I was director of knowledge management for the very large legal and compliance group of one of Canadas largest banks, so I wrote it from the KM point of view. Since then, I moved to another bank as product manager for enterprise search, and now I am one year and nine months into my director of product management role at a SaaS information management vendor. Oh, yeah, and we had a global pandemic ....

The big data world has gone through almost as many changes in the intervening years.

The pace of change in the KM world is somewhat more pedestrian I dont mean that in a bad way. I see KM as a management discipline. You can have a KM strategy, but I dont believe anyone can sell you a KM system." KM practitioners and academics who study and do research in the field sometimes take time to catch up with the technology, working practices and social changes that can be integrated into a KM strategy. In that nearly 6-year-old article I argued we should integrate big data into our KM processes, to be treated as another source of information from which knowledge could be derived, in order to provide actionable insights for decision making and creation of organizational value:

The diagram above encapsulates my high-level thinking from 2015, but the question from Twitter was really asking whats new?

Related Article: The State of Knowledge Management in 2020

Deloitte create a regular report called Global Human Capitol Trends, which includes insights into KM. As we started with an article I wrote in 2015, I thought it would be interesting to paraphrase its KM trends from the past six years:

However, the 2020 report provided this interesting commentary:

For organizations that are struggling, the good news is that technology is offering up solutions that can help. Emerging AI capabilities such as natural language processing and natural language generation can automatically index and combine content across disparate platforms. These same technologies can also tag and organize information, automatically generating contextual metadata without human intervention and eliminating a major barrier to actually using the knowledge that an organizations people and networks create.

Why is that quote so interesting to me? Well I have always said that a KM strategy relies on good information management, and we are starting to understand that good information management practices with metadata, taxonomy and ontologies can really benefit the quality of outputs provided by AI systems.

We have a symbiotic relationship between information management and some elements of AI good practice in IM can improve AI by providing well-structured taxonomies and ontologies, at the same time elements of the AI toolkit such as NLP can help automatically create metadata. At the same time application of the AI toolkit to analytics capabilities helps us to derive value from the ever-expanding sea of big data. SoIM helps AI, AI in turn helps analyze big data.

Related Article: Using AI for Metadata Creation

Let's start with a reminder of what we mean by "big data."Wikipedia has a good definitionwhich includes this key statement: Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

So we are talking about massive amounts of data, volumes so large commonly available tools (like Microsoft Excel) simple cannot handle them. Big data can be used as a source for business intelligence, but the two aren't the same. According to Wikipedia, BI uses applied maths tools and descriptive statistics, while big data uses mathematical analysis and optimization techniques, and inductive statistics. I cannot really pontificate further on this, as I am not a subject matter expert in big data. However one point we can all understand is the definition of big, got well, bigger in the last six years. One of the defining characteristics of big data is the volume of data it encompasses and the rate at which this volume expands is accelerating. Five or six years ago, we might have been talking about hundreds of gigabytes to terabytes. Now we are talking petabytes and upwards.

With so much data, analyzing it to uncover insights becomes problematical. You need data management to ensure data quality and to avoid being swamped by false signals. Data mining uncovers correlations and patterns.

From a technical perspective, a key element of the last five to six years is the ability to do in-memory analytics analyzing large data sets in fast system memory without swapping data back and forth from storage (hard drives). Products for data visualization have also advanced, and while data scientists can still use specialist tools, we've seen a move to allow non-specialists to create and manage their own dashboards. However in its 2020 Data and Analytics trends report, industry analyst Gartner predicts the demise of the pre-built dashboard as AI capabilities help analytics and business intelligence software vendors offer new user experiences beyond the now ubiquitous dashboard.

Which brings us nicely into the final element which sets 2021 apart from 2015, artificial intelligence.

The introduction of so-called artificial intelligence tools, such as natural language processing, machine learning, neural networks and deep learning have had a great impact on big data analysis. With so much data to analyze, even with the best in visualization technologies, it is difficult for human analysts to spot the most complex patterns and inter-relationships.

The application of AI capabilities to the analysis of enormous data sets will be key in moving forward with the creation of information which can then be combined with metadata, contextual information from other sources and tacit knowledge in order to create new insights for decision support and value generation for an organization.

So a lot has changed in the last five to six years with respect to how big data can be integrated into a KM strategy. Big data just keeps on getting bigger and that trend will never reverse. Good information management practices and tools can assist AI capabilities, that in turn will analyze the ever-growing data sets in our data warehouses and data lakes. Visualization technologies improved to help us find patterns, but that too will need an added layer of AI technologies to keep up. In the search for competitive advantage, things rarely get simpler. Dealing with the accelerating rate of data growth is certainly never going to be easy, but with improvements to tools and capabilities to help us generate knowledge and insights, it's up to us to do something with them!

Jed Cawthorne is Director, Security & Governance Solutions at NetDocuments. He is involved in product management and working with customers to make NetDocuments phenomenally successful products even more so.

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The Intersection of Big Data and KM: An Update for 2021 - CMSWire

Michigan publishers must be extra careful in complyingwith the states privacy law. Thats the import of a federal court decision involving Crain Communications.

Without ruling on the merits of the case, a U.S.District Court determined that an out-of-state resident had the standing to sue Crain under the Michigan Personal Privacy Protection Act (PPPA). And it dismissed a motion by Crain to dismiss the suitin late March.

Virginia subscriber Gary Lin filed a class action suit in 2019, alleging that Crainviolated the PPPA by selling his and other subscribers personal reading information to third parties without obtaining consent, JDSupra writes in an analysis.

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Crain had earlier sought to squash the lawsuit on the groundthat Lin lacked standing to file suit as a non-state resident.

In January, the court concluded the PPPA does not impose a residency requirement for customers to haveprotections, thus allowing the Lin suit to go forward, JDSupra notes.

U.S. District Judge Victoria A. Roberts wrote: if the Michigan legislature intended to limit thestatute to Michigan residents, it could have done so explicitly,

Other state statutes, including the California Consumer Privacy Act (CCPA), the California Privacy RightsAct (CPRA), and the Virginia Consumer Data Protection Act (VCDPA) do not cover non-state residents, JDSupra explains.

The privacy rights created by these statutes extendonly to residents of California and Virginia, respectively, JDSupra writes. Moreover, unlike the PPPA, none of these statutes provide a private right of action for privacy-relatedviolations, it adds.

Lin had alleged that Crain violated his protected privacy interest by disclosing PRI to data-mining companies and third-party databasecooperatives.

Continued here:

Federal Judge Ruled Against Crain Over Right To Sue In Privacy Case 05/03/2021 - MediaPost Communications