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Combines unique quality assurance technology &amp; services to help our clients rapidly achieve their QA and performance improvement goals.

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Bill Pardue, who co-founded Weather Analytics LLC and now is Chairman &amp; Chief Executive Officer of the merged companies, said both the Athenium acquisition and the $25 million additional investment will expand greatly insurers&#x27; current cost-effective choices for advanced decision support.

Weather Analytics Acquires & Merges with Athenium Inc.

Weather Analytics &amp; Athenium have joined forces. New page here: https://www.linkedin.com/company/athenium-analytics/ | Weather Analytics helps insurance companies, businesses and organizations impacted by the weather see weather differently with decision-support solutions.

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Athenium Analytics is an Insurtech SaaS company that uses historical data and predictive analytics to help insurance carriers \u0026 other organizations measure and manage quality and risk

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The Minotaur Myth The is a perfect example of something that had tremendous power yet also had a fatal weakness. The Minotaur was a half-human, half-bull creature with great strength and ferocity. Minotaur from Greek mythology However, it was ultimately defeated by the Athenian hero Theseus, who used the creature's one fatal weakness—its inability to navigate the labyrinth in which it was imprisoned—against it. Now, let’s find out how this powerful Greek mythical creature has anything to do with modern-day Microsoft, and how can aspiring young entrepreneurs find ways to fight its almost insurmountable power. Microsoft’s Resurgence When Satya Nadella took the reins as Microsoft's CEO in 2014, he inherited a company that was facing stagnation and struggling to compete in the rapidly evolving technology landscape. Nadella's leadership has since been credited with reinvigorating Microsoft by pivoting its focus from traditional operating systems and productivity software to cloud computing and AI. One good example is the Office productivity suite, and how it has evolved both before and after Nadella’s reign. Microsoft Office, first introduced in 1989, has become a cornerstone of productivity and communication in the business world. As a suite of software applications, it initially comprised Word, Excel, and PowerPoint, but has since grown to include other tools such as Outlook, Access, and Publisher. Microsoft's consistent efforts to improve and refine these applications have allowed them to maintain a dominant position in the market. By the early 2000s, Microsoft Office had become the industry standard for document creation, data management, and presentation design, a position it continues to hold today. In 2011, Microsoft introduced Office 365, a significant shift in the company's business strategy. By transitioning to a cloud-based, subscription model. This model helped solidify Microsoft's dominance in the market, while also creating a strong customer lock-in effect. The subscription model ensured a steady revenue stream for Microsoft and encouraged users to stay within the ecosystem, as the cost of switching to alternative solutions became more cumbersome and less appealing. But Microsoft’s ambitions went far beyond Office 365 and Azure cloud computing. Enter the Minotaur As we recall from the Greek myth, the Minotaur had great strength, mostly because it was a hybrid creature of half human and half bull. If we might make an analogy, Microsoft is: Half Human: human language and wisdom via OpenAI exclusive license Half Bull: computer language and intelligence via its ownership of GitHub We shall delve into each of these equally powerful halves for deeper analysis. Half Human - OpenAI and ChatGPT Microsoft's strategic investment in and its exclusive licensing partnership with OpenAI, the inventor of ChatGPT, has played a significant role in solidifying the company's position as a dominant player in the AI field. Announced in 2019, the partnership has helped Microsoft leverage OpenAI's cutting-edge research to develop new AI technologies and integrate them into its products and services. This collaboration has fueled the growth of Microsoft's AI ecosystem, enabling the company to deliver AI-driven capabilities across its offerings, including Azure AI services, Cortana, and even Office 365 applications. The sheer scope and unusually high velocity of OpenAI integration inside Office 365, Microsoft’s crown jewel of productivity suite, speaks volumes of the strategic importance of OpenAI to Microsoft. So, in a way, Microsoft now captures the entirety of human language, knowledge, and at least some inferred wisdom (which some Microsoft researchers called ). Through its exclusive licensing deal with OpenAI, it won’t have any close competitors. “a spark of AGI” You may argue that Google and Meta might challenge OpenAI/Microsoft’s monopoly, but their respective large language models, Bard and LLaMA, appear to be at least one generation behind. In addition, OpenAI has evolved from a non-profit organization to a capped-profit organization. And there is this amusing cap of profit at 100 times investment. Since Microsoft has already pumped and planned to pump over 10 billion dollars into OpenAI, you can safely assume that OpenAI will not disassociate from Microsoft any time soon. And Microsoft can always “invest” more money into OpenAI instead of buying products and services from OpenAI. This will perpetuate the symbiosis between OpenAI and Microsoft. It is no surprise that a bunch of very important people are alarmed enough to sign a petition to . halt all AI research for the next 6 months Half Bull - GitHub, Codex, and Co-Pilot The acquisition and integration of GitHub in 2018, a leading platform for software development and collaboration, further extended Microsoft’s grip on human intelligence as a whole. GitHub is a hub for all leading open-source projects, and you can consider it the aggregated repository of human intelligence expressed in a computer language that runs everything for everyone in today’s world. I would like to highlight the significance of OpenAI's Codex, an advanced language model that has had a profound impact on the field of software development, particularly through its integration with GitHub's Copilot. Codex, which is part of the GPT-3 family of models, is capable of understanding and generating human-like text, including programming code. Its ability to interpret natural language queries and generate accurate and contextually relevant code snippets has made it a valuable tool for developers. GitHub's Copilot, which leverages the capabilities of Codex, serves as an AI-powered coding assistant that helps developers write code more efficiently by providing real-time suggestions and automatically completing code segments. Copilot's integration with Codex has transformed the software development process by reducing the time and effort required to write code, minimizing errors, and enabling developers to focus on higher-level design and problem-solving tasks. After using GitHub Co-Pilot for an extended period of time and recently combining it with ChatGPT v4, I have just started to realize its tremendous power and immense potential. As a product manager and co-founder, now I can conceptualize an idea, write a succinct requirement, and let ChatGPT spit out a pretty decent boilerplate. Then, in Visual Studio Code with GitHub Co-Pilot, I can modify the function and parameters where necessary, fine-tune a bit of control flow and business logic, and I have a fully working prototype within hours. Since I’m not a real practicing software engineer, such prototyping would have cost me 1-2 weeks of hacking time or begging some software engineers to work on it. And it is not that far off from a true “no code” software development movement that will flourish among people who don’t even understand the basics of existing computer programming languages. For experienced software engineers, there are claims of 3x or even 10x productivity improvements via the combination of GitHub Co-Pilot and chatGPT v4. These tools can easily from GitHub’s gigantic repository and fit into the context of the software system that the developer is currently working on. find the design patterns It saves a lot of Google searches, reduces typing and error checking, and automates a large portion of documentation and testing. One engineer even lamented that he felt like losing one of his arms when GitHub Co-Pilot was offline for a few hours. The Potential Danger of the Minotaur Google’s mission is to “organize the world's information and make it universally accessible and useful”, and it achieves that by basically doing a PageRank on all the websites and building a giant index for them. But with Microsoft/OpenAI’s GPT large language model and GitHub codex, the world’s information might become condensed into billions and trillions of weights (numbers of statistical importance, figuratively speaking). And that’s when it hit me that we have entered the Minotaur moment of Microsoft. Microsoft's deep-rooted relationship with OpenAI has raised concerns about the potential for the company to use its combined power to create a monopoly in the field of artificial intelligence. OpenAI's GPT-4 language model is one of the most powerful AI systems in the world today, and GitHub is the world's largest code-hosting platform. If Microsoft were to use these tools to stifle competition, it could have a significant impact on the development of AI and the tech industry as a whole. It is important to note that these are just concerns, and there is no evidence that Microsoft is planning to use its combined power in a monopolistic way. However, it is crucial to be aware of the potential dangers of such a move and to ensure that Microsoft does not abuse its power. The Hope for Theseus (and Startups) Innovate on the Shoulders of Giants Startups can leverage big companies' platforms to innovate on new products by tapping into the resources, infrastructure, and user base that these platforms offer. Here are a few examples of previous success stories: Apple App Store and Google Play Store: By building apps on these platforms, startups can access a vast market of smartphone users, easily distribute their products, and benefit from the app stores' secure payment systems. Amazon Web Services (AWS): Startups can utilize AWS's cloud computing infrastructure to build and scale their applications, store data, and access advanced analytics tools. This allows them to focus on innovation and product development without worrying about investing in and managing their own IT infrastructure. By leveraging big companies' platforms, startups can gain access to resources, infrastructure, and user bases that would otherwise be difficult or expensive to acquire. This enables them to focus on innovation and product development, increasing the chances of success in the competitive market. We are already witnessing the Cambrian Explosion for AI-powered tools and solutions. Not a single day goes by without a flurry of announcements from aspiring startups mashing up  OpenAI’s GPT with new datasets (legal, financial), other AI models (voice recognition, image recognition), other generative AI solutions (voice synthesizing, text-to-image and text to video), and many more. Seek Help From the Sovereign One of the key risks of building on 3rd party for-profit platforms is that the platform might change, die, or even compete against its own ecosystem partners. One example is Facebook vs Zynga, while another example is major social networks’ de-platforming of different ideologies. Therefore, entrepreneurs might be able to find help in unusual places: Governments and  Regulators to thwart any anti-competitive behavior. And they might be able to learn a page from the previous anti-trust lawsuits in the technology sector. Playing the Game in the Open Open source has been the trusted and true weapon to fight big closed-source monoliths. Since OpenAI has closed-source its GPT-3 and GPT-4 models, Meta did an atypical move and open-sourced its own large language model named LLaMA for research purposes. This is by far the most advanced open-source LLM, with up to 65 billion weights trained on 1.4 trillion words. Even more interestingly, Meta also open-sourced 7, 13, and 33 billion weight models. These smaller LLMs enabled many research teams to come up with their own fine-tuned models based on the LLaMA. To name just a few: Alpaca from Stanford, Vicuna and Koala from Berkeley, GPT4All, and many many more. Using Reinforced Learning via Human Feedback, these research efforts claim they can reach 90-95% performance of the commercial ChatGPT 3.5 (175 billion weights), with only 7 billion or 13 billion weights. When you have these smaller LLM models and optimized C++ implementations like , suddenly you can run these models locally on your laptop. You now have a smart assistant running locally that does not rely on cloud service and costs nothing. llama.cpp The LLaMA models are for research only, thus you cannot use them commercially. There does truly exist open source LLMs, even though their scale and performance are generations behind the state of art commercial alternatives. However, many more open-source LLMs are popping up like mushrooms after an autumn rain. If you keep alert on huggingface and GitHub, you can find many of them such as: OpenAI: GPT-2 and GPT-Neo Colossal AI Together.xyz OpenChatKit Nomic-ai GPT4All Think Outside of the Labyrinth Office 365 might be Microsoft’s own Labyrinth. Why would I say that? Above all, Office 365 is critically important to Microsoft from a revenue and profit point of view (23% of total revenue in 2022 ( ). Microsoft might be able to add OpenAI capabilities to Office 365, but it might remain to be an incremental update rather than a fundamental change. source This is simply because Microsoft cannot move their enterprise customers, who are known to be risk-averse and glacial in change, into a new paradigm at too fast a pace or too dramatic a way. So in a way, Office 365 is kind of Microsoft’s shackles as well. So where are the opportunities for young startups? If we step back a bit, the reason we created the Microsoft Office suite, including Word, Powerpoint, and Excel, was that we could communicate better in a business environment. How can we invent new ways of communication that transcend beyond these existing tools? Think how Slack and Notion have transcended from emails, and how Figma has surpassed the traditional Adobe creative suite. Imagine how newer human-machine and human-human interfaces can work in a business environment. If you want to push into sci-fi territories, you can take into account Elon Musk's Neuralink where humans and machines are electrically connected. The advent of technologies like Elon Musk's Neuralink, which aims to establish direct communication between human brains and machines, opens up exciting possibilities for reimagining communication in the business environment. Here are a few ways such technology could potentially transcend traditional tools like the Microsoft Office suite: Thought-to-text communication: With the help of brain-computer interfaces (BCIs) like Neuralink, people could communicate their thoughts directly as text or visual content, bypassing the need for keyboards or other input devices. This could lead to a more efficient and faster exchange of ideas, minimizing misunderstandings and miscommunications that can arise from conventional methods. Collaborative brainstorming: Brain-computer interfaces could enable real-time sharing of thoughts and ideas during brainstorming sessions, allowing team members to simultaneously contribute and visualize concepts. This could enhance creative problem-solving and decision-making processes, fostering a more collaborative and efficient work environment. Immersive presentations: Combining BCIs with augmented reality (AR) or virtual reality (VR) technologies could revolutionize business presentations. Instead of relying on traditional tools like PowerPoint, presenters could create immersive, interactive experiences that engage the audience's senses and emotions, leading to more effective communication of complex concepts and ideas. Emotion-aware communication: BCIs could potentially detect emotions and mental states during conversations, enabling participants to better understand each other's feelings and perspectives. This could enhance empathy, facilitate constructive feedback, and foster healthier working relationships. Direct knowledge transfer: Neuralink and similar technologies could pave the way for direct knowledge transfer between individuals or from machines to humans. This could revolutionize learning and training processes in the workplace, making them more efficient and personalized. While the development and implementation of technologies like Neuralink still have a long way to go, their potential to transform communication in the business environment is undeniable. By enabling more direct, efficient, and empathetic forms of communication, brain-computer interfaces could revolutionize how we collaborate, make decisions, and share knowledge in the workplace. An Uncertain Future While I am excited and elated by the newfound productivity gain from Microsoft’s OpenAI and GitHub combination, I am also concerned that one company can hold that much power over the entirety of the human race’s collective intelligence. I hope young Theseus can pick up the open-source sword and fight a new path outside of the labyrinth so that humanity’s intellect can prosper freely. : all illustrations are by the author and MidJourney Footnote

Microsoft’s Minotaur Moment

মাইক্রোসফটের মিনোটর মোমেন্ট

Der Minotaurus-Moment von Microsoft

El momento Minotauro de Microsoft

Le moment Minotaure de Microsoft

माइक्रोसॉफ्ट का मिनोटॉर मोमेंट

マイクロソフトのミノタウロスの瞬間

마이크로소프트의 미노타우로스 순간

Momento Minotauro da Microsoft

Microsoft'un Minotaur Anı

Khoảnh khắc Minotaur của Microsoft

微软的牛头怪时刻

LIME stands for .  It is a technique used by researchers in machine learning to explain the trust of the model and to measure the confidence of the algorithm. “Local Interpretable Model-Agnostic Explanations” Now, you will have an obvious doubt that we have many evolution metrics to measure the performance of the models irrespective of being a supervised or unsupervised category. So, why do we need LIME? The problem with the mathematical evaluation metrics is that they cannot explain the reasons for the prediction in a simple understandable way.  LIME is not an opposite technique to that of traditional evaluation metrics but it is just a set of guidelines to increase the trustiness of the model in addition to that of statistical metrics with some additional interpretable information. The intuition behind the “LIME” concept in AI & ML Let us consider that we have built an image classification algorithm to predict whether a person is male or female if we provide the photo as input- The model will predict Image 1 as male and Image 2 as female based on some evaluation metrics like accuracy, precision, recall or f1 score. Now, what is the reason behind these evaluation metrics which tends the model to predict the input as male or female? Seems confusing? Let us have another example, John is suffering from a fever. He consulted a doctor to check whether his fever is due to coronavirus or not. He explained the symptoms currently he is facing like sneezing, body pain, headache etc. The doctor conducted some medical tests and said to John that it was not a corona fever but just a usual one in just a single word (Yes/No). Now, do you think John will be satisfied with the doctor’s inference just because of the reason that the doctor is an expert in the medical field? No, he will be somewhat confused and not confident because the response from the doctor looks like a naïve- binary Yes/No answer. The above case is similar to any machine learning model. Most of the models deployed in the production environment are black boxes i.e. the people who consume it as naïve users don’t know about the mathematical logic coded inside the model for doing the prediction. Implementing the predictions from such a model may result in losses even if the evaluation metrics are confidently positioned. Let us consider that doctor says to John that - “John, I have conducted an antigen test to diagnose whether SARS-CoV-2 is present in your body or not. Luckily, it is not present. Additionally, I have also done serology tests to detect antibodies that indicate whether you have been infected with the virus or not. The result is negative. You are not affected by the coronavirus…Stay happy!!!” Now, John will be very confident about the inference provided by the doctor and his trust in the doctor has increased. He will be no more confused about his health condition. He was a little bit confused when the doctor gave him a naïve-binary response. This is the actual difference between LIME and traditional acceptance of prediction. Instead of taking the prediction which is recommended by the model, LIME enforces to interpret the explanation behind the reasoning ability of the model. In our Image classification example, the user will be much more satisfied if the model says the mathematical reason behind the prediction instead of naively classifying the image as male or female such as- The person in Image 1 has a beard and moustache. He seems to have a hard skin tone and short hair. Hence the person should be a male (Accuracy – 90% and precision – 85%). The person in Image 2 has no beard or moustache. She seems to have a soft skin tone and long hair. Hence the person should be a female (Accuracy – 90% and precision – 85%). Now, the prediction seems more easily interpretable and acceptable as the model explains the reason behind the predictive inference. This is nothing but LIME. Let us have a look at the intuition and interpretation behind the LIME. LIME is the abbreviated form of – Local Interpretable Model-Agnostic Explanation The “ is nothing but a clarification and descriptive statement that can briefly convey the clear-cut and unbiased reasons behind the predictive inference. explanation To become model “ , LIME usually don’t go to the depth of the approaches used inside the algorithm but to find out which part of the interpretable input is contributing to the prediction, we perturb the input around its neighbourhood and see how the model's predictions behave. agnostic” For example, Let us consider that we are building a model to analyze the sentiment about a film – whether it is good or bad based on the reviews floating on the internet.  Consider that a reviewer writes that   “I loved the movie”. Now, LIME recommends perturbing the sentence like “I loved”, ‘I movie”, “loved the movie”, “I movie”, etc. as inputs to the model. Now, after some iterations, the model can intuitively understand that the word “loved” is the most important word to measure the sentiment of the reviewer. Rest of the words like “I”, “movie”, “the” etc. are not so important in an algorithmic perspective. is nothing but one of the characteristics of LIME that makes the user understandable the reasons and explanations in a very lucid and easy manner. There should be no high-level mathematics or scientific concepts so that the naïve user finds it difficult to understand the reason behind the predictions of the model. “Interpretable” For example, In word embedding, we can see that the model explains the inferences in complex charts that is not at all visually appealing and easy to understand. LIME can interpret those kinds of information in simple forms (like sentences and words in the English language). The output of LIME is a list of explanations, reflecting the contribution of each feature to the prediction of a data point. This provides local interpretability, and it also allows us to determine which feature changes will have the most impact on the prediction. Let us see this example from a visual perspective- Imagine that we are working on a binary classification problem. Consider that the blue circle denotes the decision boundary (prediction area) of this model in 2 feature dimensions – X and Y. The green diamond is a particular data point and the red circles are perturbed data points (the ones we generated/tweaked).  The size of the circles indicates the proximity of the original data point. The data points can be assigned certain priority weights according to their proximity. Now, we have to find how the model’s prediction changes for perturbed inputs. For that, we should train a surrogate model on the dataset by using the perturbed data points as the target. This explanation and findings may not be acceptable globally but acceptable for the local region around the green diamond. Here is the standard step-by-step procedure- refers to local trust or local belief - i.e., we want the explanation to reflect the behaviour of the classifier "around" the instance being predicted. The fidelity measure (how well the interpretable model approximates the black box predictions) gives us an idea of how reliable the interpretable model is in explaining the black box predictions in the neighbourhood of the data point of interest. “Local” This is an example mentioned in the research paper. Here, the classifier predicts Electric Guitar even though the image contains an acoustic guitar. The explanation image shows the reason for its prediction. (This library is not still able to explain the reasons in a sentence format and currently, the reasons are being explained in an image format for image classification problems. There are some libraries which can explain the reasons in sentence format in Python and R). This is another example from the research paper where the model is built to predict whether a paragraph proposes Christianity or Atheism based on the words used in the paragraph. First of all, we need to understand the difference between model-specific and model-agnostic interpretation tools to understand how this is implemented in LIME- Model-specific interpretation tools Model-specific interpretation tools are specific to a single model or group of models.  These tools depend heavily on the working and capabilities of a specific model. For example, Linear regression depends upon various parameters like slope, coefficient etc to make predictions. The decision tree uses a technique called entropy to find the best split of nodes to increase the predictive ability of the model. Neural networks have millions of parameters for connecting the neurons and making a prediction. All these above parameters and techniques are specific to their model architecture. These rules, guidelines or parameters of the model cannot be applied globally for making interpretations. These are called model-specific interpretations. Model-agnostic interpretation tools Model-agnostic tools can be used on any machine learning model no matter how complicated. These agnostic methods usually work by analysing feature input and output pairs. Model-specific interpretations will have more accuracy since their model architecture will be complex and well-built like linear regression, random forest, neural network etc., but model-agnostic interpretation will have low accuracy since the interpretations are done only by analysing the patterns of input and output. So how can we build model-agnostic interpretable models that don’t compromise on accuracy? One of the solutions to this problem is the global surrogate method. This method suggests using an additional model called an which can explain the predictions done by the black box model. “interpretable model” or “surrogate model” For example, If we want to build an ML model for predicting whether a student will pass the exam or not then we will have 2 models- Black box model (To predict whether the student will pass the exam or not) Surrogate model (To interpret the approximate logic of the black box used for the decision making). The black box model will be a complex algorithm and the surrogate model will be a simple algorithm. For example, We need to build a machine learning model for predicting whether a student will pass the exam or not. We have 100’s independent variables. Hence, we built a neural network which is highly complex and not easily interpretable. Now, after getting the results, how can we interpret what kind of features and characteristics of predictor variables resulted in the pass/fail of students? For that, we can build a simple decision tree which is very less complicated than a neural network and easily interpretable. Using this decision tree, it will be easy to find which all are factors mainly influenced the pass/fail of the students in the examination like “Marks in the assignments”, “Marks in the internal exams”, “Attendance percentage” etc. This is done by training a decision tree on the predictions of the black-box model. Once it provides good enough accuracy, we can use it to explain the neural network. Here, the neural network is a black box model and the decision tree is a surrogate model. Hence, we can conclude the black-box model by interpreting the surrogate model. The following image shows the steps to build a surrogate model- The above procedure is based on an assumption that – “When the complexity of the model increases, accuracy increases and interpretability decreases. Hence, it makes sense to make predictions using a complex machine learning algorithm for getting highly accurate predictions and use the same inputs and predictions from the complex machine learning model to train a simple machine learning model so that the interpretability of the predictions can be easily inferred through the low complicated architecture of simple model”. However, a disadvantage of the global surrogate method is that it can explain the global features and rules that can interpret the predictions of a black-box model. However, it cannot interpret how a single prediction was made for a given observation. For example, If we are predicting the exam results (pass/fail) of thousands of students in a university then a global surrogate method can show which are the factors that globally influence the marks of students like “Marks in internal exams”, “Marks in projects”, “Attendance percentage” etc. But it cannot interpret the reason why “John (Roll number -198) belonging to MBA final year” was not able to clear the exam. LIME’s model-agnostic approach becomes a key attraction in these kinds of scenarios where it can interpret these kinds of scenarios. Advantages of LIME We can change the black box model and still use the same local surrogate model because it does not have any heavy impact on interpretability. For example, In our above example , let us consider that the blue-coloured decision boundary (binary classification problem) is created by Kernel SVM and we use a decision tree as the local surrogate model. We can change the black box model from Kernel SVM to Random forest and still use the same decision tree as a surrogate model for local interpretation. Sometimes, the local surrogate model will give much more information about the feature importance than the black box model which may be a helpful global inference. (Fig 1 and Fig 2) LIME can be implemented in any kind of data like raw texts, images, data frames etc. Inbuilt packages are available in R and Python. Disadvantages of LIME Simple perturbations might not be enough and complex perturbations may lead to bias in the model. Hence, it requires some soft skill algorithms or techniques to introduce perturbation to the model. Although, LIME is model agnostic, the type of modifications that need to be performed on the data are use-case specific. For example, a model that predicts sepia-toned images to be retro cannot be explained by the presence or absence of superpixels (Iris dataset) Non-linearity at local regions in specific datasets makes the model difficult to interpret. Such models are not easily adaptable with LIME. Conclusion The of the machine learning outputs from complex black box models is always a concern for researchers in the analytics field. To an extent, LIME can be considered a solution to this problem for many use cases. There are many more libraries that are getting developed as a part of the ongoing research and development in the analytics field and LIME is a successful initial step in this learning journey. I hope, you got a brief understanding of the fundamental principles of LIME in this article. For more in-depth information, you can refer to the official research paper which I have mentioned in the reference section. “Explainability” References Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, “Why Should I Trust You?” - Explaining the Predictions of Any Classifier Vilone, Giulia; Longo, Luca (2021). . Information Fusion. December 2021 - Volume 76: 89–106. : . "Notions of explainability and evaluation approaches for explainable artificial intelligence" doi 10.1016/j.inffus.2021.05.009 . DARPA. DARPA. Retrieved 17 July 2017. "Explainable Artificial Intelligence (XAI)" Castelvecchi, Davide (2016-10-06). . Nature. (7623): 20–23. "Can we open the black box of AI?" 538

The Intuition Behind the “LIME” Concept in AI & ML

🚀 This is issue #104 of THE EXPONENTIAL VIEW. Sign-up for the newsletter here. Darknet is more decentralised than Internet 🙊 : . “People want voice to be the new thing …. the tech industry is casting around looking for the Next Big Thing. I suspect that voice is certainly a big thing, but we’ll have to wait a bit longer for the next platform shift.” Benedict Evans Voice & the uncanny valley of AI 👨🏽‍🌾 How . “These superstars can offer more variety, cheaper prices and convenience, but the bigger chunk of profits that they capture is split among fewer workers.” superstar firms have shrunk labour’s share of the pie 👫 ? John Danaher takes an Aristotelian view. Will robots help our hinder the creation and maintenance of meaningful human friendships 🌟 Tim Berners-Lee: (Paradoxically, the Darknet now which may make it more resilient to attack or oligopoly power.) We need to take control of our data, tackle misinformation and manage political messaging in order to save the Web. has a more decentralized structure than the mainstream Internet 🔥 argues . “It finally recognizes its power and the ways in which that power has caused problems in the world, but its instinct is to wield that power even more, rather than back off.” Facebook wants more power in our lives, and we should resist, Jan Dawson Dept of artificial intelligence from Singularity. Says Ng: Interview with Andrew Ng from Baidu and Neil Jacobstein Just as about 100 years ago electrification changed every single major industry, I think we’re in the phase where AI will change pretty much every major industry. And he continues Things may change in the future, but one rule of thumb today is that almost anything that a typical person can do with less than one second of mental thought we can either now or in the very near future automate with AI.This is a far cry from all work. But there are a lot of jobs that can be accomplished by stringing together many one-second tasks. 📈 Put this together with that “machine and AI algorithmic innovation transform analytics … to supercharge the [Pareto 80/20 principle]”, and it paints a picture of how important it is for firms to get the grips with AI’s impact. Michael Schrage’s argument in Harvard Business Review learning In essence, the availability of data across organisations will identify opportunities where the optimisation is not an 80/20 but perhaps a super-pareto (a 99/1). In other words, taking Pareto’s observation that small components may have substantial impact and lasering in on where those effects are more significant. 🔮 To see how this works in practice, I really recommend . Lange has run machine learning platforms at Microsoft, Amazon, Uber and Unity. (I recently saw him speak at an excellent .) Danny Lange ’s excellent presentation on AI in the enterprise Nordic.AI event The arrival of AI is in firms. also changing organisational structures Many companies are now putting greater emphasis on cultural fit and adaptability, knowing that individual roles will have to evolve along with the implementation of AI. Both Ng and Schrage describe AI in the terms that “potent tools that promise a more prosperous and comfortable ”. Kaplan reckons that “[p]ublic discourse about AI has become untethered from reality.” Jerry Kaplan calls future Or as says in this lovely essay: “ ” Ines Montani please, finally, stop using that same old fucking wired brain illustration. Elsewhere: 🚗 How Drive.AI is . There has been much discussion about whether deep learning on its own can handle the complexities of autonomous driving or needs to be married with more traditional robotics approaches. Drive seems to be one firm emphasising a holistic deep learning approach. using deep-learning from soup-to-nuts in their new autonomous vehicle tech FASCINATING 🗼 How could AI could improve the understanding of a site and its environs, support design decision making and the creative endeavour, foster better client relations and facilitate novel AI-robot enhanced craftsmanship. artificial intelligence change architecture? . Moving into trial in 50 burger joints over the next two years. (Video) Flippy the burger making robot Nice application of machine learning to assist in fairer bail and sentencing IBM’s achieves a 5.5% error rate continuous speech recognition system A look at Graphcore’s chip architectures which are optimised for machine learning Did ? Possibly not. Cambridge Analytica’s secret psychographic sauce really help Trump’s victory 48m twitter accounts are actually bots with bots Shanghai factory plans to replace all humans Quantum dept of maybe I’ve been meaning to cover more quantum computing for a few weeks but this week and my buddies at went to town on it. You can access here. Nature The Economist Jason Palmer’s survey in The Economist which promises to make quantum computing more widely accessible. It won’t outperform conventional computing (yet) but could be a useful playground for developers. IBM has announced IBM Q, ArsTechnica has more details on IBM’s 50-qubit computer. Time crystals, the seventh state of matter, are official. (We hinted at this in ). Simple ; more . EV#98 explanation from WSJ detailed explanation from Nature 🔬 Obituary of Peter Mansfield, pioneer of magnetic resonance imaging Small morsels to appear smart at dinner parties We a re watching more video, just not more TV The turning to new chip architectures at Intel’s cost. Wintel marriage is falling apart and Microsoft is Handy brief history of Blockchain within 100 days. Tesla reckons it can solve South Australia’s energy problems 🇨🇳 China will have more than a quarter of a billion over-60s within 3 years How the food (and wider) trade blockchain might improve (Machine learning meets psychology with a surprising result.) Your name might affect your facial appearance. Is ambiguity a ? better barometer of market uncertainty than volatility UK as coal use tails off carbon emissions drop 6% Half of all new Norwegian cars are electric or hybrid 🌍 Brian Whittaker: The rise of Arab atheism Tinder Select is a new invite-only version for highly-rated members. 👨🏽‍🔬 Gorgeous to five different people (including a five year old kid). video of a neuroscientist explain the connectome

🔮 How AI will change the enterprise and robot friendships ++ #104

… for most companies. Over the years, I’ve heard a lot of people talk about algorithmic business and how big data will improve business strategy. For most, I strongly suspect it won’t. I originally wrote this post in and what I’ve observed since then makes me feel it is worth repeating. To explain the problem, I’m going to have say certain things that many will find uncomfortable. February 2015 Chess and business I want you to imagine you live in a world where everyone plays Chess against everyone else. How good you are at Chess really matters, it determines your status and your wealth. Everyone plays everyone through a large computer grid. Ranks of Chess players are created and those that win are celebrated. Competition is rife. The oddest part of this however is that no-one in this world has actually seen a Chessboard. When you play Chess in this world, you do so through a and it looks like this … control panel Each player takes it in turn to press a piece. Each player is aware of what piece the other player pressed. White starts first. Then Black counters, then White, then Black and so the game continues building up the sequence of presses. The game continues until at some point, usually after a long sequence of presses, the system announces that someone has won or it is a draw. Unbeknownst to either player, there is actually a board and when they press a piece then a piece of that type is randomly selected. That piece is then moved on the board to a randomly selected position from all the legal moves that exist. White’s opening move might be to select ‘Pawn’, one of White’s eight pawns would then be moved one or two spaces forward. But all of this is hidden from the players, they don’t know any of this exists, they have no concept there is a board … all they see is the control panel and the sequence of presses. However, we have the sequence! Within that sequence people will find ‘favourable’ patterns e.g. the best players seem to press the Queen as often as possible! People will come up with their own favourite combinations and try to learn the combinations of other winners! “When your opponent moves the Knight, you should counter with Queen, Queen, Rook” etc. People in this world would write books on secret knowledge such as or . As you aggregate more and more data from different games in ever larger “big data” systems and AI is applied then the more patterns will be proclaimed and the more secrets of success announced in popular publications. You can already imagine that article in Harvard Business Review collecting sequences from over 100,000 games and announcing the killer combination of . “The art of the Bishop” “The ten most popular sequences of Successful Winners” Queen, Queen, Pawn, Knight Remember this isn’t because people are daft, they simply have no concept of the board in this world. Some through experience might even gain a vague notion that there is a landscape, a board on which we are playing. Let us suppose one of those people — by luck, by accident or by whatever means — twigs that a landscape does exist and somehow finds a way to interact with it. Imagine one day, you play that individual but they can see something quite remarkable … the board. You start off planning to use your favourite opening combination of You make your first move. Pawn, Pawn, Knight. They counter with , you make your next move, they counter and you’ve lost. Pawn you scream. Obviously, this was just luck! But just in case this is a new secret sequence, you scribble down their presses. “ , “What the fiddlesticks happened there!” Pawn Queen — wow what a simple sequence!” But every time you play this player, you lose and you lose fast. You keep recording their sequences and even try to use their sequence against them. They beat you in two presses — Pawn (b) and Queen (b) (known as Fool’s Mate) or they beat you in four presses — Pawn (b), Queen(b), Bishop(b), Queen(b) (known as Scholar’s mate) but you can’t replicate their success even though you copy their sequences, it never works and you keep on losing. People will be amazed by the incredible skill of this player but also confused that they can’t replicate the success. They will start to seek all different forms of answers to explain what is happening. Maybe it’s not just the sequence but the way that the player presses the button? Maybe it’s timing? Maybe it’s what they had for lunch? Maybe it’s their attitude, a cultural thing? They seem to be happy (because they’re winning). Is happiness the secret to winning? All sorts of correlations and speculations will be jumped upon. However there is no luck to this or special way of pressing buttons. The player controlling Black simply has far greater situational awareness. The problem for the player controlling White is they have no real understanding of the context of the game they are playing. White’s is just a shadow of the landscape and the sequence of presses lacks any positional information or consistent understanding of movement on the board. When faced with a player who does understand the environment then no amount of large scale data analysis on combinations of sequences of presses through the or application of artificial intelligence or algorithms that is going to help you. control panel control panel I need to emphasise this point of understanding the landscape and the importance of situational awareness and so we’ll now turn our attention to another story — Themistocles. Themistocles SWOT The battle of Thermopylae (the tale of the three hundred) and the clash between the Greeks and the mighty army of Xerxes has echoed throughout history as a lesson in the force multiplier effect of landscape. Themistocles devised a strategy whereby the Athenian navy would block the straits of Artemisium forcing the Persians along the coastal road into the narrow pass of Thermopylae where a smaller force could be used to hold back the vastly greater Persian numbers. I’ve provided a map of this below. Now, Themistocles had options. He could defend around Athens or defend around Thebes but he chose to block the straits and exploit the landscape. Each of Themistocles’ options represents a different on the map. In the same way that our enlightened Chess player has many to move pieces on the board. By understanding the landscape you can make a choice based upon one move is better than another. This is a key point to understand. WHERE WHEREs WHY People often talk about the importance of in strategy but is a relative statement i.e. here over there. To answer the question of you need to first understand and that requires situational awareness. WHY WHY WHY WHY WHERE Now imagine you’re in that Greek army. Themistocles had turned up on the eve of battle, he had enthused the troops with purpose and a goal — “ . Let us assume the Greek army (a collection of state armies) had varying but overall decent principles e.g. working as a team, being skilled in what you do, collaborating with each other and were well trained. But now imagine, that no-one on the Greek side had a concept of maps or even an understanding of maps. We need to defend the Greek states against the horde of Xerxes” Themistocles steps forward and tells the troops to have as he announces his great vision in the form of a SWOT (strengths, weaknesses, opportunities and threats) diagram. “no fear, we are the greatest warriors in the world” How confident would you feel? I know I’d be running for the hills. Now in terms of combat, I’d hope you’d agree that a strategy based upon an understanding of the landscape is going to be vastly superior than a strategy that is derived from a SWOT. Equally from the point of view of communication and learning then a map beats a SWOT hands down. The question you need to ask yourself however is — what do we most commonly use in business? SWOT or MAP? On business and situational awareness The problem that exists in most businesses is that they have little or no situational awareness. Most executives are even unaware that you can map the business environment and increase situational awareness. They are usually like the Chess Players using the Control Panel and oblivious to the existence of the board, relying on magic secrets of success from other players — “I hear Amazon just did this, we should do the same” I say oblivious because these people aren’t daft, they just don’t know that a landscape exists. But how can we be sure this is happening? Language and behaviour are often our biggest clues. If you take our two Chess players (White with little or no situational awareness, Black with high levels) then you often see a marked difference in behaviour and language. Those with high levels of situational awareness often talk about positioning and movement. Their strategy is derived from WHERE and so they can clearly articulate WHY they are making a specific choice. They tend to use visual representation (the board, the map) to articulate encounters both present and past. They learn from those visualisations. They learn to anticipate others moves and prepare counters. Those around them can clearly articulate the strategy from the map. Those with situational awareness talk about the importance of WHERE — , . They describe how strategy is derived from understanding the context, the environment and exploiting it to your favour. The strategy always adapt to that context. “we need to drive them into the pass” “we need to own this part of the market” Those with low levels of situational awareness often talk about the HOW, WHAT and WHEN of action (e.g. the combination of presses). They tend to focus on execution as the key. They poorly articulate the WHY having little or no understanding of potential WHEREs. They have little or no concept of positioning and they often look to ‘copy’ the moves of others — . Any strategy they have is often gut feel, story telling, alchemy, copying, magic numbers and bereft of any means of visualising the playing field. Those around them often exhibit confusion when asked to describe the strategy with any precision beyond vague platitudes. Those who lack situational awareness often talk about the importance of WHY? They describe how strategy should be derived from your vision (i.e. general aspirations). They often look at you agog when you ask them “Company X gained these benefits from implementing a digital first strategy, we should do the same” “Can you show me a map of your competitive environment?” Back in 2012, I interviewed 160 different high tech companies in Silicon Valley looking at their level of strategic play (specifically their situational awareness) versus their use of open (whether source, hardware, process, APIs or data) as a way of manipulating their environment. This was part of a study for the and we considered that these companies were the most competitive of the competitive. Leading Edge Forum What the study showed was there was significant difference in the levels of strategic play and situational awareness between companies. When you add in a tendency towards action and performance measure by market cap changes over the last seven years then those with high levels of strategic play had performed vastly better than those with low levels. Leading Edge Forum 2012 study on situational awareness versus action Now this was Silicon Valley and a select few. I’ve conducted various interviews since then and I’ve come to conclusion that less than 1% of companies have any mechanism of visualising their environment and continuously learning from it. The vast majority suffer from little to no situational awareness but then they are competing in a world against others who also lack situational awareness. This is probably why the majority of strategy documents contain a tyranny of action and why most companies seem to duplicate strategy memes and rely on backward causality. We should be digital first, cloud first, we need a strategy for big data, social media, cloud, insight from data, IoT … yada yada. It’s also undoubtably why companies get disrupted by predictable changes and are unable to overcome their inertia to the change. At the same time, I’ve seen a select group of companies and parts of different Governments use mapping to remarkable effect and discovered others who have equivalent mental models and use this to devastate opponents. Now, I don’t believe we’re anywhere near the pinnacle of situational awareness in business — far better methods, techniques and models will be discovered than those that exist today. Which brings me back to the title. In the game of Chess above, yes you can use large scale data analytics, AI and algorithms to discover new patterns in the sequences of presses and certainly this will help you against equally blind competitors. Such techniques will also help you in business improve your supply chain or understand user behaviour or marketing or loyalty programmes or operational performance or any number of areas in which we have some understanding of the environment. But this won’t help you in strategy against the player with better situational awareness. Most business strategy itself operates in a near vacuum of situational awareness. For the vast majority then I’ve yet to see any real evidence to suggest that big data is going to improve this. There are a few and rare exceptions but in general, the key is first to understand the landscape and that a landscape exists. Alas for most, it will become hunting for that magic sequence … Pawn, Pawn … what? I’ve lost again? If you’re going — — it’s fairly easy. To help you on your way, , all creative commons on just how to do that with a technique that I’ve been using for over a decade. Please feel free to help yourself. “Well, how do you map a business?” I’m writing a book Attribution-ShareAlike 4.0 International Originally published at blog.gardeviance.org on January 8, 2017.

Why big data and algorithms won’t improve business strategy

The Idea of a University Defined and Illustrated: Discourse III -Bearing Of Theology On Other Branch

A Treatise on Domestic Economy; For the Use of Young Ladies at Home and at School by Catharine Esther Beecher, is part of the HackerNoon Books Series. You can jump to any chapter in this book . PREFACE here PREFACE - TO THE THIRD EDITION. The author of this work was led to attempt it, by discovering, in her extensive travels, the deplorable sufferings of multitudes of young wives and mothers, from the combined influence of , , . The number of young women whose health is crushed, ere the first few years of married life are past, would seem incredible to one who has not investigated this subject, and it would be vain to attempt to depict the sorrow, discouragement, and distress experienced in most families where the wife and mother is a perpetual invalid. poor health poor domestics and a defective domestic education The writer became early convinced that this evil results mainly from the fact, that young girls, especially in the more wealthy classes, . In early life, they go through a course of school training which results in great debility of constitution, while, at the same time, their physical and domestic education is almost wholly neglected. Thus they enter on their most arduous and sacred duties so inexperienced and uninformed, and with so little muscular and nervous strength, that probably there is not , that young women of the present day, will pass through the first years of married life without such prostration of health and spirits as makes life a burden to themselves, and, it is to be feared, such as seriously interrupts the confidence and happiness of married life. are not trained for their profession one chance in ten The measure which, more than any other, would tend to remedy this evil, would be to place on an equality with the other sciences in female schools. This should be done because it be properly and systematically taught (not , but as a ), as much so as or , or any other branch of study; because it embraces knowledge, which will be needed by young women at all times and in all places; because this science can never be taught until it is made a branch of ; and because this method will secure a dignity and importance in the estimation of young girls, which can never be accorded while they perceive their teachers and parents practically attaching more value to every other department of science than this. When young ladies are taught the construction of their own bodies, and all the causes in domestic life which tend to weaken the constitution; when they are taught rightly to appreciate and learn the most convenient and economical modes of performing all family duties, and of employing time and money; and when they perceive the true estimate accorded to these things by teachers and friends, the grand cause of this evil will be removed. Women will be trained to secure, as of first importance, a strong and healthy constitution, and all those rules of thrift and economy that will make domestic duty easy and pleasant. domestic economy can practically science political economy moral science properly study To promote this object, the writer prepared this volume as a for female schools. It has been examined by the Massachusetts Board of Education, and been deemed worthy by them to be admitted as a part of the Massachusetts School Library. text-book It has also been adopted as a text-book in some of our largest and most popular female schools, both at the East and West. The following, from the pen of Mr. George B. Emmerson, one of the most popular and successful teachers in our country, who has introduced this work as a text-book in his own school, will exhibit the opinion of one who has formed his judgment from experience in the use of the work: "It may be objected that such things cannot be taught by books. Why not? Why may not the structure of the human body, and the laws of health deduced therefrom, be as well taught as the laws of natural philosophy? Why are not the application of these laws to the management of infants and young children as important to a woman as the application of the rules of arithmetic to the extraction of the cube root? Why may not the properties of the atmosphere be explained, in reference to the proper ventilation of rooms, or exercise in the open air, as properly as to the burning of steel or sodium? Why is not the human skeleton as curious and interesting as the air-pump; and the action of the brain, as the action of a steam-engine? Why may not the healthiness of different kinds of food and drink, the proper modes of cooking, and the rules in reference to the modes and times of taking them, be discussed as properly as rules of grammar, or facts in history? Are not the principles that should regulate clothing, the rules of cleanliness, the advantages of early rising and domestic exercise, as readily communicated as the principles of mineralogy, or rules of syntax? Are not the rules of Jesus Christ, applied to refine and preserve a , as important as the abstract principles of ethics, as taught by Paley, Wayland, or Jouffroy? May not the advantages of neatness, system, and order, be as well illustrated in showing how they contribute to the happiness of a family, as by showing how they add beauty to a copy-book, or a portfolio of drawings? Would not a teacher be as well employed in teaching the rules of economy, in regard to time and expenses, or in regard to dispensing charity, as in teaching double, or single entry in bookkeeping? Are not the principles that should guide in constructing a house, and in warming and ventilating it properly, as important to young girls as the principles of the Athenian Commonwealth, or the rules of Roman tactics? Is it not as important that children should be taught the dangers to the mental faculties, when over-excited on the one hand, or left unoccupied on the other, as to teach them the conflicting theories of political economy, or the speculations of metaphysicians? For ourselves, we have always found children, especially girls, peculiarly ready to listen to what they saw would prepare them for future duties. The truth, that education should be , has the greatest force with children. The constantly-recurring inquiry, 'What will be of this study?' is always satisfied by showing, that it will prepare for any duty, relation, or office which, in the natural course of things, will be likely to come. domestic manners good temper a preparation for actual, real life the use "We think this book extremely well suited to be used as a text-book in schools for young ladies, and many chapters are well adapted for a reading book for children of both sexes." To this the writer would add the testimony of a lady who has used this work with several classes of young girls and young ladies. She remarked that she had never known a school-book that awakened more interest, and that some young girls would learn a lesson in this when they would study nothing else. She remarked, also, that when reciting the chapter on the construction of houses, they became greatly interested in inventing plans of their own, which gave an opportunity to the teacher to point out difficulties and defects. Had this part of domestic economy been taught in schools, our land would not be so defaced with awkward, misshapen, inconvenient, and, at the same time, needlessly expensive houses, as it now is. Although the writer was trained to the care of children, and to perform all branches of domestic duty, by some of the best of housekeepers, much in these pages is offered, not as the result of her own experience, but as what has obtained the approbation of some of the most judicious mothers and housekeepers in the nation. The articles on Physiology and Hygiene, and those on horticulture, were derived from standard works on these subjects, and are sanctioned by the highest authorities. is another work prepared by the author of the Domestic Economy, in connexion with several experienced housekeepers, and is designed for a supplement to this work. On pages and will be found the Preface and Analysis of that work, the two books being designed for a complete course of instructions on every department of Domestic Economy. The American Housekeeper's Receipt Book 354a 354b The copyright interest in these two works is held by a board of gentlemen appointed for the purpose, who, after paying a moderate compensation to the author for the time and labour spent in preparing these works, will employ all the remainder paid over by the publishers, to aid in educating and locating such female teachers as wish to be employed in those portions of our country, which are most destitute of schools. The contract with the publisher provides that the publisher shall guaranty the sales and thus secure against any losses for bad debts, for which he shall receive five . He shall charge twenty per cent. for commissions paid to retailers, and also the expenses of printing, paper, and binding, at the current market prices, and make no other charges. The net profits thus determined are then to be divided equally, the publishers taking one half, and paying the other half to the board above mentioned. per cent About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Catharine Esther Beecher (2007). A Treatise on Domestic Economy; For the Use of Young Ladies at Home and at School. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/21829/pg21829-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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