Public Private Partnerships And The Cybersecurity Challenge Of Protecting Critical Infrastructure
Public Private Partnerships And The Cybersecurity Challenge Of Protecting Critical Infrastructure

In the U.S., most of the critical infrastructure, including defense, oil and gas, electric power grids, health care, utilities, communications, transportation, education, banking and finance, is owned by the private sector (about 85 percent according to DHS) and regulated by the public sector. The public and private relationship in operating and protecting critical infrastructure requires a strong working partnership.

Protecting the critical infrastructure poses a difficult challenge because democratic societies by their nature are interactive, open and accessible. Because of the growing digital connectivity (and interdependence) of both IT and industrial control systems, critical infrastructure is facing an evolving and sophisticated array of cybersecurity challenges.

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Chuck BrooksChuck BrooksChuck Brooks, Cybersecurity, Public Private PartnershipsComment
How Can You Trust the Predictions of a Large Machine Learning Model?
How Can You Trust the Predictions of a Large Machine Learning Model?

Artificial intelligence has emerged as the defining technology of our era, as transformative over time as the steam engine, electricity, computers, and the Internet. AI technologies are approaching or surpassing human levels of performance in vision, speech recognition, language translation, and other human domains. Machine learning (ML) advances, like deep learning, have played a central role in AI’s recent achievements, giving computers the ability to be trained by ingesting and analyzing large amounts of data instead of being explicitly programmed.

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Irving Wladawsky-BergerIrving Wladawsky-Bergerartificial intelligence, complex systems, data science, big data, education and talent, innovation, management and leadership, services innovation, smart systems, society and culture, technology and strategy
Three Habits for Effective Digital Transformation
Three Habits for Effective Digital Transformation

Large scale change has never been easy. Nearly three decades ago, leadership guru Dr John Kotter reported that 70% of all major change efforts in organizations failed. Just a couple years later, the late Dr. Michael Hammer estimated a 70% failure rate for the radical reengineering efforts. Now, that transformational efforts are often driven by technology, the recent success rate is equally bleak according to research by BCG. The root cause of failure with large scale digital change is captured by George Westerman’s first law of digital transformation, which states that: Technology changes quickly, but organizations change much more slowly. 

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Andrew SpanyiAndrew Spanyidigital transformation, business processes, customer oriented, cross functional processes, capability maturity model, digital maturity, CMM, mindset, integration of tools
Catalyzing Innovation via Centers, Labs, and Foundries
Catalyzing Innovation via Centers, Labs, and Foundries

The cornerstone of collaboration is based on knowledge transfer; sharing of research tools, methodologies and findings; and sometimes combining mutual funding resources to meet shortfalls necessary to build prototypes and commercialize technologies.

Collaborations often involve combinations of government, industry and academia who work together to meet difficult challenges and cultivate new ideas. A growing trend for many leading companies is creating technology specific innovation centers, labs, and foundries to accelerate collaboration and invention.

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Chuck BrooksChuck BrooksChuck Brooks, Catalyzing, Innovation, Centers, Labs, FoundriesComment
How low-code platforms can aid intelligent business process management
How low-code platforms can aid intelligent business process management

The potential for low-code/no-code platforms is enormous. Low-code increases the productivity of IT developers — sometimes by several orders of magnitude. And no-code empowers experts and subject matter experts primarily on the business or operations side (as opposed to IT) to become “citizen developers.” But as I explained in a previous article, low-code and no-code platforms are not a panacea; they face challenges.

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Setrag KhoshafianSetrag Khoshafianlow code, enterprise, BPM, digital transformation, workflow, iBPM
Intelligent automation depends on these 4 cornerstones
Intelligent automation depends on these 4 cornerstones

There has been more than a modicum of buzz around what IDC calls intelligent process automation and what Gartner calls hyperautomation. In both cases, these terms refer to the integrated deployment of digital technologies such as robotic process automation (RPA), intelligent business process management suites (iBPMS), artificial intelligence, process mining, etc. Integrating digital technologies is far from a new concept. MIT and Deloitte advocated this approach back in the day when everyone was focused on social, mobile, analytics, and cloud (SMAC).

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Andrew SpanyiAndrew Spanyiintelligent automation, sequence, collaboration, scope, mindsetComment
High Performance Analysis and Control of Complex Systems Using Dynamically Reconfigurable Silicon and Optical Fiber Memory

This is Lars Wood’s only published paper, invited to appear in the proceedings of the first IEEE workshop on FPGAs for Custom Computing Machines in 1993. This paper is significant in that it demonstrates the expanse of FCCM computing using a non-numerical paradigm for numerical computations within a completely asynchronous optoelectronic machine. This paper is referenced as prior art for patents in the space, including one for interconnected optoelectronic FPGAs. The paper is considered a vision statement for FCCM in 1993. View original published paper in IEEE COMPUTER SOCIETY DIGITAL LIBRARY.

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Lars WoodLars WoodFCCM, computation, FPGA
7 Types Of Artificial Intelligence
7 Types Of Artificial Intelligence

Artificial Intelligence is probably the most complex and astounding creations of humanity yet. And that is disregarding the fact that the field remains largely unexplored, which means that every amazing AI application that we see today represents merely the tip of the AI iceberg, as it were. While this fact may have been stated and restated numerous times, it is still hard to comprehensively gain perspective on the potential impact of AI in the future. The reason for this is the revolutionary impact that AI is having on society, even at such a relatively early stage in its evolution.

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Naveen JoshiNaveen JoshiAI, reactive machines, limited memory, Theory of Mind, self aware, artificial narrow intelligence (ANI), artificial general intelligence (AGI), artificial super intelligence (ASI)Comment
Infusion of Machine Learning Operations with Internet of Things
Infusion of Machine Learning Operations with Internet of Things

With advancements in deep tech, the operationalization of machine learning and deep learning models is burgeoning in the machine learning space. In a typical scenario within organizations involving machine learning or deep learning business cases, the data science and IT teams collaborate extensively in order to increase the pace of scaling and pushing multiple machine learning models to production through continuous training, validation, deployment and integration with governance. Machine Learning Operations (MLOps) has carved a new era of the DevOps paradigm in the machine learning/artificial intelligence realm by automating end-to-end workflows.

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Shikhar Kwatra, Utpal ManglaShikhar Kwatra, Utpal Manglainternet of things, IoT, MLOps, infusion of machine learning operations, model operationalization, continuous deployment, continuous integration, model training and versioning, IoT edge platforms
Low code: A promising trend or a Pandora’s Box?
Low code: A promising trend or a Pandora’s Box?

The analyst community is having a field day with hype around “low code.” IDC has predicted that there will be more and more low code used and that the worldwide population of low-code developers will grow with a CAGR of 40.4% from 2021 to 2025. Gartner predicted that low code will increase nearly 30% from 2020 to reach $5.8 billion in 2021. Forrester has also jumped on the low-code hype wagon and forecasted that by the end of 2021, 75% of application development will use low-code platforms.

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Andrew SpanyiAndrew Spanyilow code no code, digital transformationComment
Can AI Help With Autism Diagnosis?
Can AI Help With Autism Diagnosis?

In the US, one in five children are living with a diagnosable behavioral health disorder; however, only 21% of those children who are diagnosed receive needed treatment. Often, parents have to wait one to three years to receive a proper and accurate diagnosis and an individualized treatment plan for their child.

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Jun WuJun Wuartificial intelligence, autism diagnosis, autism and ai, children with autism, therapeutic tools, digital health, digital therapeuticsComment
How AI Is Transforming Agriculture
How AI Is Transforming Agriculture

Agriculture and farming is one of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various technologies. As the world population continues to grow and land becomes more scarce, people have needed to get creative and become more efficient about how we farm, using less land to produce more crops and increasing the productivity and yield of those farmed acres.

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Kathleen WalchKathleen Walchagriculture, farm data, labor challenge, ai and agriculture, transformationComment
Use AI Ethically To Build Relationships, Not Data Warehouses
Use AI Ethically To Build Relationships, Not Data Warehouses

As technology evolves at a rapid rate – especially technology that incorporates artificial intelligence (AI) capabilities – so too does the potential for bias, disconnect, misuse of data, and the automation of impersonal actions or decisions. With the vast amounts of data collected, stored, and exchanged, capitalist societies risk the commoditization of personal data at the expense of the individual, instead of using personal data to foster valuable individual and societal relationships.

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Alan TreflerAlan TreflerAlan Trefler, AI, Ethical AI, Build RelationshipsComment
AutoML 2.0: Is The Data Scientist Obsolete?
AutoML 2.0: Is The Data Scientist Obsolete?

It's an AutoML World. The world of AutoML has been proliferating over the past few years - and with a recession looming, the notion of automating the development of AI and Machine Learning is bound to become even more appealing. New platforms are available with increased capabilities and more automation. The advent of AI-powered Feature Engineering - which allows users to discover and create features for data science processing automatically - is enabling a whole new approach to data science that, seemingly, threatens the role of the data scientist. Should data scientists be concerned about these developments? What is the role of the data scientist in an automated process? How do organizations evolve because of this new-found automation?

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Ryohei FujimakiRyohei FujimakiAutoML 2.0, data science, data scientist, democratization, automationComment
The New Security Playbook: How Open Source and Freemium Can Fuel Rapid Growth in Enterprise Markets
The New Security Playbook: How Open Source and Freemium Can Fuel Rapid Growth in Enterprise Markets

That the world has become a more dangerous place is no surprise to anyone who follows the cybersecurity industry. More devices, more data and more surface area, unfortunately, means more vulnerabilities to attack. I’ve been investing in the cybersecurity space for more than 20 years, and I’ve never seen the market move faster than it is changing today.

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Ken ElefantKen Elefantcybersecurity, Open Source, freemiumComment
A Scoville Heat Scale For Measuring Cybersecurity
A Scoville Heat Scale For Measuring Cybersecurity

The Scoville Scale is a measurement chart used to rate the heat of peppers or other spicy foods. It can also can have a useful application for measuring cybersecurity threats. Cyber-threats are also red hot as the human attack surface is projected to reach over 6 billion people by 2022. In addition, cyber-crime damage costs are estimated to reach $6 trillion annually by 2021. The cybersecurity firm RiskIQ states that every minute approximately 1,861 people fall victim to cyber-attacks, while some $1.14 million is stolen. In recognition of these alarming stats, perhaps it would be useful to categorize cyber-threats in a similar scale to the hot peppers we consume. I have provided my own Scoville Scale-like heat characterizations of the cyber threats we are facing below.

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Chuck BrooksChuck BrooksChuck Brooks, Cybersecurity, Scoville Heat ScaleComment