If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
With pattern recognition approach, there are many methods to implement a handwriting digits recognition task. In my previous stories, I have introduced the Linear Discriminant Analysis base on the Gaussian model maximum likelihood estimation. In this post, I apply the Logistic Regression model on the English numeral handwriting digits recognition task. In the logistic regression model, an occurrence probability of an event is represented by a logistic function. For example, in a two-class problem, the logistic sigmoid function is commonly used.
'All models are wrong, but some are useful' As this famous quote by George Box (known as the Box Theorem) shows, no model is ever going to be 100% accurate. If one is, run for the hills! Rather, models should be evaluated by their impact on the bottom line, or how useful they are to the business. In this blog post, we will explore a way in which models can be more useful, by embracing and leveraging uncertainty to maximize business results. Much of the time, business users want a single number to represent the'goodness' of a model, but machine learning models can tell us so much more than just a single number (like accuracy).
Quantum Computing is approaching a period of commercialization that may change our reality. Early adopters of quantum's remarkable capacity to take care of specific kinds of issues may accomplish achievements that empower new business models. Visionary enterprises are now lining up with the developing quantum computing ecosystem to become "quantum ready." These ground breaking enterprises are exploring use cases and related algorithms that address complex business issues. Artificial Intelligence (AI) and Machine Learning (ML) based analytics solutions require aggregating and analysing data to train them to copy real-world observed behaviours.
Even the most experienced Data Scientists are not always familiar with the best practices involved with developing a Machine Learning pipeline. There is a lot of confusion about what steps should be involved, what should be their sequence and, in general, how to ensure that the insights you create are accurate and valuable. There is also a very limited number of good resources describing a practical and correct approach. However, after many data science projects, you begin to realise the approach to building a pipeline always remains the same. Machine Learning pipelines are modular, and, depending on the situation, some steps can be added or skipped.
Over the last several months, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a global pandemic, resulting in nearly 480,000 COVID-19 related deaths as of June 25, 2020 . While the disease can manifest in a variety of ways--ranging from asymptomatic conditions or flu-like symptoms to acute respiratory distress syndrome--the most common presentation associated with morbidity and mortality is the presence of opacities and consolidation in a patient's lungs. Upon inhalation, the virus attacks and inhibits the lungs' alveoli, which are responsible for oxygen exchange. This opacification is visible on computed tomography (CT) scans. Due to their increased densities, these areas appear as partially opaque regions with increased attenuation, which is known as a ground-glass opacity (GGO).
It's time to reset, re-create and collaborate on a new paradigm where Compassion and Kindness are the prevailing norms, one where technology is a tool for making humans more humane and creating an Abundant world for the majority. Join us to turn this vision into reality. Let's look into the'White Mirror' … Inspired by Black Mirror (Netflix series) - 'White Mirror' (holding name while we devise a suitable one) provides an immersive flash forward (glimpse/vision) of our Utopian future. In uncertain times (like now), technological disruption and impactful stories can change our mental worldview - our perceptions and eventually our reality. Black Mirror is a powerful show, depicting a dystopian future caused in part by misused evolving technologies.
The graph represents a network of 1,554 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 13 July 2020 at 10:38 UTC. The requested start date was Monday, 13 July 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 22-hour, 34-minute period from Saturday, 11 July 2020 at 01:18 UTC to Sunday, 12 July 2020 at 23:53 UTC.
As AI becomes ubiquitous, more and more high-stakes decisions will be made automatically by machine learning models. AI can determine the very future of your business and can make life-or-death decisions for real people. But as the world changes, an AI system is often faced with new examples that it hasn't seen before, and it may not know the right answer. Without proper guardrails, these automated decisions can quickly turn into catastrophic failures if left unchecked and can reduce trust in AI. As the stakes get higher, it is critical that AI systems are built to be humble -- just like humans, AI should know when it doesn't know the right answer.
From drones for food delivery and robots for automation to COVID-19 contact tracing apps, and online education learning platforms, we've seen a great acceleration in adoption of different technologies in the past few months. Technology has been a great pillar of strength during the pandemic and it's also going to help redefine the post COVID-19 world. Now, different businesses and industries will benefit from different technologies, but there are some common ones that are likely to dominate the world after COVID-19. Nuff said, let's take a look at some of the tech trends that are likely to see a surge in adoption post COVID-19. We know this one's too obvious but that's for a reason - AI is playing a massive role in helping us all get through the pandemic and it will see a greater adoption after the pandemic is over.
"Brings us to aws flicks took the at the time unconventional decision to go all in on aws many years ago at this point, and that's treated. The the whole idea around blessed programming languages where you make a strong decision within an organization to restrict the number of programming languages with an organization and it it that constraint ends up helping the organization make decisions more quickly and allow for engineering mobility and so on. This has been the case with aws when when Netflix? Strongly moved onto aws and continue to do that. That extends to medfly show. A better flow is an open source framework, but it has a tight coupling with aws. So why is the tight coupling to aws useful for machine learning framework? Sue I won't say that. We are tightly coupled to eight of us. So when leave it open sourcing MEDOFF. No at that point in time, because we had a good amount of operational expertise with aws, we chose indicating the details are ready for this cloud integration, ...