We propose CX-ToM, short for counterfactual explanations with theory-of mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that generate explanations as a single shot response, we pose explanation as an iterative communication process, i.e. dialog, between the machine and human user. More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user. To do this, we use Theory of Mind (ToM) which helps us in explicitly modeling human's intention, machine's mind as inferred by the human as well as human's mind as inferred by the machine. Moreover, most state-of-the-art XAI frameworks provide attention (or heat map) based explanations. In our work, we show that these attention based explanations are not sufficient for increasing human trust in the underlying CNN model. In CX-ToM, we instead use counterfactual explanations called fault-lines which we define as follows: given an input image I for which a CNN classification model M predicts class c_pred, a fault-line identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class c_alt. We argue that, due to the iterative, conceptual and counterfactual nature of CX-ToM explanations, our framework is practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, demonstrating that our CX-ToM significantly outperforms the state-of-the-art explainable AI models.

In practice, and more especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven predictive performances on different population segments highlights the representativeness issues possibly induced by a naive aggregation of image datasets. Indeed, sampling bias does not vanish simply by considering larger datasets, and ignoring its impact may completely jeopardize the generalization capacity of the learned prediction rules. In this paper, we show how biasing models, originally introduced for nonparametric estimation in (Gill et al., 1988), and recently revisited from the perspective of statistical learning theory in (Laforgue and Cl\'emen\c{c}on, 2019), can be applied to remedy these problems in the context of visual recognition. Based on the (approximate) knowledge of the biasing mechanisms at work, our approach consists in reweighting the observations, so as to form a nearly debiased estimator of the target distribution. One key condition for our method to be theoretically valid is that the supports of the distributions generating the biased datasets at disposal must overlap, and cover the support of the target distribution. In order to meet this requirement in practice, we propose to use a low dimensional image representation, shared across the image databases. Finally, we provide numerical experiments highlighting the relevance of our approach whenever the biasing functions are appropriately chosen.

A U.S. judge has ruled that artificial intelligence can't get a patent for its creations, ruling that such a privilege is reserved for people. District court judge Leonie Brinkema backed a decision by the U.S. patent office to turn away applications made on behalf of a "creativity machine" named DABUS. Brinkema issued a ruling saying that "the clear answer is'no'" to the question of whether an AI machine qualifies as an inventor under patent law. "As technology evolves, there may come a time when artificial intelligence reaches a level of sophistication that might satisfy accepted meanings of inventorship," Brinkema said in the ruling. "But that time has not yet arrived and, if it does, it will be up to Congress to decide how, if at all, it wants to expand the scope of patent law."

Should an artificially intelligent machine be able to patent its own inventions? For a US federal judge, the larger implications of that question were irrelevant. In April 2020, the US Patent and Trademark Office (USPTO) ruled that only "natural persons" could be credited as the inventor of a patent, and a US court decided Thursday that yes, that's what the law technically says (via Bloomberg). Not every country agrees with that direction. South Africa and Australia decided to go the other direction, granting one patent and reinstating a second patent application filed by AI researcher Steven Thaler, whose AI system DABUS reportedly came up with a flashing light and a new type of food container.

In this paper, the robot-assisted Reminiscence Therapy (RT) is studied as a psychosocial intervention to persons with dementia (PwDs). We aim at a conversation strategy for the robot by reinforcement learning to stimulate the PwD to talk. Specifically, to characterize the stochastic reactions of a PwD to the robot's actions, a simulation model of a PwD is developed which features the transition probabilities among different PwD states consisting of the response relevance, emotion levels and confusion conditions. A Q-learning (QL) algorithm is then designed to achieve the best conversation strategy for the robot. The objective is to stimulate the PwD to talk as much as possible while keeping the PwD's states as positive as possible. In certain conditions, the achieved strategy gives the PwD choices to continue or change the topic, or stop the conversation, so that the PwD has a sense of control to mitigate the conversation stress. To achieve this, the standard QL algorithm is revised to deliberately integrate the impact of PwD's choices into the Q-value updates. Finally, the simulation results demonstrate the learning convergence and validate the efficacy of the achieved strategy. Tests show that the strategy is capable to duly adjust the difficulty level of prompt according to the PwD's states, take actions (e.g., repeat or explain the prompt, or comfort) to help the PwD out of bad states, and allow the PwD to control the conversation tendency when bad states continue.

Weighting methods are a common tool to de-bias estimates of causal effects. And though there are an increasing number of seemingly disparate methods, many of them can be folded into one unifying regime: causal optimal transport. This new method directly targets distributional balance by minimizing optimal transport distances between treatment and control groups or, more generally, between a source and target population. Our approach is model-free but can also incorporate moments or any other important functions of covariates that the researcher desires to balance. We find that the causal optimal transport outperforms competitor methods when both the propensity score and outcome models are misspecified, indicating it is a robust alternative to common weighting methods. Finally, we demonstrate the utility of our method in an external control study examining the effect of misoprostol versus oxytocin for treatment of post-partum hemorrhage.

A US judge has ruled that artificial intelligence can't get a patent for its creations, ruling that such a privilege is reserved for people. District court judge Leonie Brinkema backed a decision by the US patent office to turn away applications made on behalf of a "creativity machine" named DABUS. Brinkema issued a ruling on Thursday saying that "the clear answer is'no'" to the question of whether an AI machine qualifies as an inventor under patent law. "As technology evolves, there may come a time when artificial intelligence reaches a level of sophistication that might satisfy accepted meanings of inventorship," Brinkema said in the ruling. "But that time has not yet arrived and, if it does, it will be up to Congress to decide how, if at all, it wants to expand the scope of patent law."

Importantly, they can also prevent costly defects and avoid operational inefficiencies. While COVID-19 sped up the pace of adoption for many industries, including industrial manufacturing, manufacturing companies have historically embraced new ways of working. Manufacturers were early endorsers of Kaizen, Six Sigma, and Lean, known business improvement models with direct impacts to the continuous improvement methodology critical to manufacturing processes. And now, AI is being embraced for its ability to make supply chains more flexible -- mostly to evaluate vulnerabilities identified during the COVID-19 pandemic among their suppliers and in the supply chain itself -- reduce costs, and fully leverage human talent and intelligence. According to a new KPMG report, Thriving in an AI World, 93% of industrial manufacturing respondents indicated they have moderate or fully functional AI, primarily machine learning technologies, implemented into their processes.

A scholarship programme seeking to nurture talent in technological research in Africa's public universities has been launched. The three-year programme aims to meet the rising demand for expertise in responsible artificial intelligence (AI) and machine learning (ML) in the continent. While machine learning encompasses the study of computer algorithms and use of data, artificial intelligence involves the simulation of human intelligence by machines, usually computer system. The scholarship programme will support selected scholars to undertake PhD research in AI and ML in African universities, and early career academics to strengthen their research and development capacities in the two areas. Murang'a County to give dairy firm to farmers Sacco What Matiang'i didn't reveal on deployment of police officers The initiative, dubbed the A14D Africa scholarship, is implemented by the African Centre for Technology Studies (ACTS) based in Kenya in partnership with Kwame Nkrumah University in Ghana, University of Linkoping, Sweden, University Cheikh Anta Diop de Dakar, Senegal, University of California, Human Sciences Research Council and Institute for Humanities in Africa based in South Africa and the University of Eduardo Mondlane, Mozambique.

Sooner or later, the concept of digitization will completely take over all repetitive tasks. Today, with the help of big data, advanced technologies like automation, artificial intelligence, IoT, and machine learning are leveraging unimaginable amounts and types of information to work from. It is streamlining tedious, repetitive, and difficult tasks, which tend to slow down production and also increases the cost of operation. Owing to the evolution of technology, artificial intelligence startups are mushrooming like never before. The companies are driving the world into a new phase of digitization with a mixture of disruptive statistical methods, computational intelligence, soft computing, and traditional symbolic AI. Artificial intelligence is the combination of two amazing concepts namely science and engineering. With the infusion of disruptive trends and human intelligence, intelligent machines and intelligent computing programs are emerging. Slowly, the flare of innovations moved away from IT and entered into diverse industries including healthcare, education, finance, marketing, business, telecommunication, etc. Organizations realized that by digitizing repetitive tasks, an enterprise can cut the cost of paperwork and labor which further eliminates human error, thus boosting efficiency. Automating processes involve employing artificial intelligence solutions that can support digitization and deliver data-driven insights. Artificial intelligence startups emerge as a ready-made solution provider that supports every company's individual needs. AI startups in 2021 use big data to sophisticated AI models and leverage new solutions that could better serve customers. Analytics Insight has listed the top 100 artificial intelligence startups that are driving the next-generation development in technology. It democratizes the way investments are done by bringing sophisticated elite trading technology to laymen. Accrad is a health tech company that assists radiologists to reduce their workload with the precision of artificial intelligence. Radiologists work under different circumstances and deadlines and might find diagnosis through x-rays a bit difficult. Therefore, Accrad has come up with a futuristic solution to help with accurate and fast image diagnosis. The company has made x-ray processing more convincing and simpler. Its signature product CheXRad, a deep learning algorithm that identifies locations in the chest radiograph has the capability to predict 15 different diseases including Covid-19. Affable.ai is a data-driven influencer marketing platform where customers can find relevant and authentic influencers and manage marketing operations. By using cutting-edge computer vision algorithms on social media posts, the company delivers actionable insights about micro-influencers and their audience. Similar to how Google has sophisticated its search and promote relative ads to users, Affable.ai has also built one-click marketing at a shorter scale.