Listen to this episode from Tcast on Spotify. Artificial Intelligence is all the rage these days. There are universities doing research, op-eds in newspapers, and even several articles in this space talking about artificial intelligence and machine learning and how it will affect our lives in the years to come. Unfortunately, most of the focus has been on how it can be used to improve the bottom lines for businesses around the world. Don’t get us wrong, we’re not knocking the profit motive. However, we are knocking the idea that you have to keep on making more and more profit. That drive has a way of dehumanizing people (and frankly even the people with the drive) and making people lose focus on the things that really matter in the world.  One of the effects of the constant drive for more profits is the drive to consume more things. More and more we look like Huxley’s Brave New World in which consumerism is promoted by the state, to the extent they put out slogans like “the less stitches, the more riches” to promote people buying new clothes instead of mending what they already have. Every economic ‘stimulus’ is given in the hopes that people use that money to go buy a bunch of stuff to keep things moving. Consumerism is a huge problem in the modern world. This is true both spiritually and materially, though for this article we’ll be focused on the material problems.  Whether it is the government or business telling us that the way to happiness is the latest and greatest smartphone, TV, car, etc. this creates problems. All of that stuff requires resources to make. Minerals, trees, oils and who knows what are used every time something like that is purchased. And the old goes into landfills, which are gigantic, so gigantic in some places that people literally live on them in places like India, making a living off reselling some of the things in them. We’ve gotten better at reusing a lot of that stuff, being able to recycle things made of the rare minerals mined in Africa or melting down plastics so they can be remolded into something else. However, there is a finite amount of stuff on the planet and a growing population that will naturally keep using that stuff. We might well find ourselves able to get into space and use resources there before much longer, but it wouldn’t hurt to also reduce our dependency on the drive to constantly have more stuff as well.  Which brings us back to our primary issue for this article, how can we use our digital technology to reduce that need? How can we use things like AI to make things more sustainable? Fortunately, our ability to collect and analyze data is just as unparalleled as our increased drive to consumerism. Improved analytics can be used for a variety of efforts that will make farming more efficient, enabling people to get the most food out of a plot of land while doing the least amount of harm to the environment.  We can learn how to build safer, smaller, and lighter vehicles so that they use fewer resources, are more fuel efficient and still allow people to get from point A to point B. AI can be used to study the effects of different zoning laws. Would it be better to allow more mixing of business and residential areas so people don’t need to drive ten minutes whenever they need a gallon of milk?  There is a lot of potential here. And a lot of ways that potential can be undermined. We’ll be exploring both of those a bit more next time. What’s your data worth? www.tartle.co

Did you miss the opportunity to join the conversation on Artificial Intelligence and how we impact the next frontier of our humanity? First, we're so sorry that you missed it! The event took place on Saturday, 20 February 2021 at 09:00 AM Pacific Time (US & Canada). We had an incredible time together discussing our role with black leaders, top experts, and innovators from the world's best tech companies and our community. That's EXACTLY why we'll make the replay available.

For example, a "what" concept is "where"-invariant and a "where" concept is "what"-invariant, as explained in [55], [68]. Section IV discusses an optimal framework through which such abstractions can take place from learning simple rules during early life that enable learning of more complex rules during later life-- called scaffolding [69]. Theorem 2 leads to two observations on data fitting on a static data set: Observation 1: Any data fitting on a static data set without learning invariant concepts are nonscalable, including the n-fold cross-validation discussed below. Unfortunately, data fitting on a static data set is a norm in all ImageNet Contests [66]. Namely, the remaining subsections in this section analyze approaches that are nonscalable. For example, computer vision is not a "one-shot" pattern classification problem as argued by Li Fei-Fei et al. [19] (which was questioned in PubMed without responses), but rather a spatiotemporal problem to learn various invariant concepts present in cluttered natural scenes through autonomous attention saccades, as explained further in Observation 2. Observation 2: Learning invariant concepts seem nonscalable for any data fitting on a static data set either, because there are too many images to be labeled by hand (e.g., all pixel locations) [55], [68]. Like a human baby, any scalable machine learning methods must be conscious through which the machine learner must consciously guess concepts (i.e., not just active learning [70]) (e.g., an object type) and verify their invariance rules (e.g., the where-invariance of a what concept).

Zimbabwe's commonest crimes include robbery, petty theft, vehicle burglary, home invasion, and smash-and-grab vehicle break-ins. The Zimbabwe security services have made a lot of efforts to make society as safe as possible but the nation's crime remains prevalent. Zimbabwe's answer to these kinds of crimes is technology; our hidden weapon. Universities in Zimbabwe have students who are getting educated in Artificial intelligence (AI) and machine learning (ML) with the Harare Institute of Technology (HIT) leading the way. These kinds of developments in AI and ML mean that technology has a growing role to play in upholding the law.

"Deep fakes"--a term that first emerged in 2017 to describe realistic photo, audio, video, and other forgeries generated with artificial intelligence (AI) technologies--could present a variety of national security challenges in the years to come. As these technologies continue to mature, they could hold significant implications for congressional oversight, U.S. defense authorizations and appropriations, and the regulation of social media platforms. Though definitions vary, deep fakes are most commonly described as forgeries created using techniques in machine learning (ML)--a subfield of AI--especially generative adversarial networks (GANs). In the GAN process, two ML systems called neural networks are trained in competition with each other. The first network, or the generator, is tasked with creating counterfeit data--such as photos, audio recordings, or video footage--that replicate the properties of the original data set.

Decision-makers are concerned about how the use of artificial intelligence (AI) technology can negatively impact their brand and result in a loss of stakeholder and customer trust. In fact, research has shown that 56% of executives globally have slowed down their AI adoptions because of such fears. These concerns have given rise to the concept of Responsible AI (RAI). It refers to the way organisations use AI technologies, and the adherence to certain principles that relate to the greater good, the protection of individuals and their fundamental rights, and generally the trustworthiness of the AI application. While everybody has a role to play in RAI, business and public leaders are ultimately accountable to ensure that AI technologies are used responsibly.

Data fusion is a well-known technique, becoming more and more popular in the Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its ability of reinforcing AI4EO applications by combining multiple data sources and thus bringing better results. On the other hand, like other methods for satellite data analysis, data fusion itself is also benefiting and evolving thanks to the integration of Artificial Intelligence (AI). In this letter, four data fusion paradigms, based on Convolutional Neural Networks (CNNs), are analyzed and implemented. The goals are to provide a systematic procedure for choosing the best data fusion framework, resulting in the best classification results, once the basic structure for the CNN has been defined, and to help interested researchers in their work when data fusion applied to remote sensing is involved. The procedure has been validated for land-cover classification but it can be transferred to other cases.

Food security is more prominent on the policy agenda today than it has been in the past, thanks to recent food shortages at both the regional and global levels as well as renewed promises from major donor countries to combat chronic hunger. One field where machine learning can be used is in the classification of household food insecurity. In this study, we establish a robust methodology to categorize whether or not a household is being food secure and food insecure by machine learning algorithms. In this study, we have used ten machine learning algorithms to classify the food security status of the Household. Gradient Boosting (GB), Random Forest (RF), Extra Tree (ET), Bagging, K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Ada Boost (AB) and Naive Bayes were the classification algorithms used throughout this study (NB). Then, we perform classification tasks from developing data set for household food security status by gathering data from HICE survey data and validating it by Domain Experts. The performance of all classifiers has better results for all performance metrics. The performance of the Random Forest and Gradient Boosting models are outstanding with a testing accuracy of 0.9997 and the other classifier such as Bagging, Decision tree, Ada Boost, Extra tree, K-nearest neighbor, Logistic Regression, SVM and Naive Bayes are scored 0.9996, 0.09996, 0.9994, 0.95675, 0.9415, 0.8915, 0.7853 and 0.7595, respectively.

Most classical planners use grounding as a preprocessing step, reducing planning to propositional logic. However, grounding comes with a severe cost in memory, resulting in large encodings for SAT/QBF based planners. Despite the optimisations in SAT/QBF encodings such as action splitting, compact encodings and using parallel plans, the memory usage due to grounding remains a bottleneck when actions have many parameters, such as in the Organic Synthesis problems from the IPC 2018 planning competition (in its original non-split form). In this paper, we provide a compact QBF encoding that is logarithmic in the number of objects and avoids grounding completely by using universal quantification for object combinations. We compare the ungrounded QBF encoding with the simple SAT encoding and also show that we can solve some of the Organic Synthesis problems, which could not be handled before by any SAT/QBF based planners due to grounding.

Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others' learning as an external knowledge base.