As interest from the research community and across industries continues to grow, there has been a shift in what’s possible with machine learning and natural language processing. Here are five trends to look out for in the next few years.
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This is the 2nd article in the post-Covid-19 Digital Transformation Debts series. Organizations are now facing tremendous challenges to cut costs – becoming lean – while delivering high-quality products and services. These are incredibly challenging times.
Value Stream digitization and automation in the post-Covid-19 era have two salient showcases: (1) Operational Excellence especially for customer service and the associated intra-Enterprise processes; (2) Inter-Enterprise Collaboration through Value-Stream-As-A-Service.
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The impact of the Covid-19 lockdown of businesses and government will be felt for many years to come. It is causing mega-cultural trends that are already changing us. We might not be feeling it or appreciating the shifts that are happening. It is too close to home and we are in the midst of it. But it is happening, and it is very real. This is the first of a 10 part series on Digital Transformation Debt - post-Covid-19. It is the most important one, as it covers Culture.
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“The biggest deficit that we have in our society and in the world right now is an empathy deficit. We are in great need of people being able to stand in somebody else’s shoes and see the world through their eyes.” — Barack Obama
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IPAoT is the next phase in the evolution of IoT! Connectivity of devices in our homes, offices, and industries is ubiquitous. Connectivity is exploding. Several technologies such as 5G, Cloud, Blockchain, and AI are critical catalysts in the pervasiveness of robust and pragmatic solutions with IoT. But the glue that ties it together and connects IoT devices to people, enterprises, and trading partners is Process Automation.
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Autonomous systems are coming online in many contexts: vehicles, drones, weapons, industrial robotics, and financial services to name just a few. Many have their own policy stakeholders and regulations, but thousands of startup companies are releasing multi-use technologies without clear guidelines. In this article, Intel AI Senior Director Abigail Hing Wen lays out a general regulatory framework for deploying automated systems safely.
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As states, counties, and cities around the country struggle to find ways to combat COVID, a holistic approach is required to ensure that all communities are being properly monitored for the spread of the disease and that data that is being used to inform public policy is timely, relevant and maintains the privacy of people. This article details how Data Science, Education, and extreme collaboration should be used together as part of this holistic approach to create better and more trusted public policy for all.
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Viewing the world from a cybernetic perspective encourages you to think of informational pathways from human to machine and back again. The idea is to extract as much information from as many sources as possible.
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AI is as good as the data gets, and it is as critical as the problem it's solving. Most solutions for content moderation on platforms today are based on Keywords -- looking for specific words by exact match within a single message. Yet, if you’re on a dating app where messages are typically personal and intimate, how would you determine a message is a flirting text versus a sexual assault? Or, on a freelancing marketplace, how would you take right action over platform circumvention between a genuine contract offer after repeating transactions versus true bad actors trying to go offline to avoid platform fees?
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If you were to ask your daughter about AI, what do you think she’d say? Not too long ago, AI was just a thing of science fiction, but today, AI’s real-world applications range from self-driving cars and search algorithms to creative art and medical diagnosis. But one of AI’s current problems is that it is only as strong as the people who are building it. A lack of diversity in AI has led to flawed and biased systems that perpetuate class and gender imbalances within AI applications.
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Trump's White House has been using to model the COVID outbreak. This is just plain bad math. The key to building a mathematical or statistical model is to include variables that represent the driving phenomenon. For instance, most econometric and epidemiological models include a variable for time because things are expected to change over time.
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Even as data science becomes ubiquitous, we still have a shortage of people who truly understand data. Yaneer Bar-Yam is a Professor and President of the New England Complex Systems Institute. He graduated from MIT and is an expert in complex systems. Throughout this pandemic, he has been meticulously analyzing COVID-19 data using both simple statistical models and complex system models and following government policies from all over the world. He wrote more than a few blog posts on his website to try to validate policy responses and to urge policymakers to consider how they are using data in their decision-making process.
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HR organizations are increasingly turning to AI as a means to identify and rate candidates for employment and promotion. There has understandably been a backlash as oftentimes companies bake discriminatory signals into their algorithms, without even realizing it. Many negative articles have been published about the pseudo-science of facial scanning algorithms to determine job match-worthiness. Obviously, it is critically important that..
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A massive amount of data about the pandemic is generated every day. Although organizations such as WHO, CDC, Johns Hopkins University, and Worldometers are disseminating important statistics daily, the data is not analyzed in an efficient way to provide insights. The COVID-19 pandemic is a complex system involving biology, human behavior, companies, and governments, and it’s influenced by healthcare, economics, governance, and geopolitics. Sophisticated analytical methods could help improve..
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A massive amount of data about the pandemic is generated every day. Although organizations such as WHO, CDC, Johns Hopkins University, and Worldometers are disseminating important statistics daily, the data is not analyzed in an efficient way to provide insights. The COVID-19 pandemic is a complex system involving biology, human behavior, companies, and governments, and it’s influenced by healthcare, economics, governance, and geopolitics. Sophisticated analytical methods could help improve economic, societal, and geopolitical stability. Deep Knowledge Group has developed advanced analytical frameworks to analyze this data. The results are presented in the form of open source country rankings to help people and governments make informed decisions that maximize beneficial outcomes for humanity.
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After a year of being pummeled with information about AI being the next big thing and possibly the most impactful technology ever, it is time to step back and really form a worldview on the potential impacts on humanity. We have a few years of data points to extrapolate a future view that is reasonably accurate.
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In the technology arena, we often fail to use language in beneficial ways. We name categories with monikers like Social, Cloud and Big Data and they soon after losing any valuable meaning. There is already complexity with digital systems and poor definitions make even more difficult. The latest unhelpful moniker may be the worst – Artificial Intelligence (AI).
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Many enterprises, vendors, and startups often confuse the role of data scientist and data engineers. While the overlap of these roles is substantial they’re not particularly interchangeable.
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The most powerful impact of AI can be realized in the context from the end-to-end digitization of value streams (aka “value chains”) involving multiple participants, business units or partners. A value stream is associated with a business measure (a Key Performance Indicator (KPI)) and typically involves multiple participants across the digital enterprise. Borrowing a compelling perspective from Theory of Constraints: “a chain is no stronger than its weakest link.”
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