Weaknesses: The term'unified' should be revised as the paper addresses a partial unification. For instance, the unified framework does not take into account a closed loop between the DNN and the explanation method (the explanation method can be itself another DNN interacting in a double sense with the prediction DNN) or other two-stage adaptive networks [1], [2]. In addition, an alternative to example based explanation is'opening the black box' in terms of intra-layer and inter-layer statistical properties of a DNN [3]: these may be enough to explain lack of generality (and thus absence of recommendation) of a given network depending on the input available data and the classification paradigm considered. Thus, a positioning must be provided with respect to the above issues in order to make the paper more informative with respect to the literature. The weak spots of the analysis are twofold.

Researchers have designed a state-of-the-art walking robot that could revolutionize large construction projects in space. They tested the feasibility of the robot for the in-space assembly of a 25m Large Aperture Space Telescope. A scaled-down prototype of the robot also showed promise for large construction applications on Earth. Maintenance and servicing of large constructions are nowhere more needed than in space, where the conditions are extreme and human technology has a short lifespan. Extravehicular activities (activities done by an astronaut outside a spacecraft), robotics, and autonomous systems solutions have been useful for servicing and maintenance missions and have helped the space community conduct ground-breaking research on various space missions.

Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.

With the pandemic changing the job scenario and opening up newer avenues, upskilling has become a necessity, with a record high of 79 percent of professionals (including freshers) planning to upskill this year. Another 11 percent of respondents said they are considering the option. According to a report by edtech company Great Learning, titled'Upskilling Outlook in India 2022', the emergence of newer domains like Web 3.0, metaverse, NFTs, etc., has propelled this growth. Meanwhile, data science, artificial intelligence (AI), and software development were the top domains for upskilling, with over 43 percent of the respondents expressing their intent in data-focused domains, such as data science, AI, machine learning, and analytics, it added. Based on internal data from Great Learning, a survey was conducted by Pyxis with about 1,000 respondents from cities, including Bengaluru, Chennai, Delhi, Hyderabad, Mumbai, and Pune. Respondents were from information technology (IT) and business process management (BPM), banking, education and training, and automobiles, among other industries.

Nair's prototype neuromodulation device stimulated nerves with electrical pulses through the skin to improve nerve function. After testing it on himself to ensure the device was safe, Nair asked his mother to try it. "She had given up hope because she had suffered for more than a year, and nothing was working," says Nair. His mother agreed to give his device a try. "She was a little disheartened when she couldn't feel anything the first time she tried it," says Nair. "For the first four weeks, she didn't have any kind of reaction to the neuromodulation stimulus. Then her legs started to respond."

Artificial intelligence (AI) techniques are used increasingly in many areas of applications, including banking and finance. They have several advantages over traditional statistical methods: i) ability to handle new data types such as text, audio, and images; ii) flexible models that yield excellent predictive performance; and iii) ability to automate many of the routine, and time-consuming, tasks in model development. However, the use of these algorithms also raise several challenges. A well-known problem is the opaqueness of ML models and the difficulties in understanding and interpreting the model results. In this paper, we focus on a related and equally important challenge: potential for bias and lack of fairness when using AI/ML techniques.

In some stores, sophisticated systems are tracking customers in almost every imaginable way, from recognizing their faces to gauging their age, their mood, and virtually gussying them up with makeup. The systems rarely ask for people's permission, and for the most part they don't have to. In our season 1 finale, we look at the explosion of AI and face recognition technologies in retail spaces, and what it means for the future of shopping. This episode was reported and produced by Jennifer Strong, Anthony Green, Tate Ryan-Mosley, Emma Cillekens and Karen Hao. Strong: Retailers have been using face recognition and AI tracking technologies for years. And what if you could know about the presence of violent criminals before they act? With Face First you can stop crime before it starts.] It detects faces, voices, objects and claims it can analyze behavior. But face recognition systems have a well-documented history of misidentifying women and people of color. And they're trying to sell it and impose it on the entirety of the country?] Strong: This is Representative Alexandria Ocasio-Cortez at a 2019 congressional hearing on facial recognition.

Bengaluru: With disruptive technologies like Artificial Intelligence (AI) driving businesses, the IT sector wants the Union Budget for fiscal 2020-21 to ensure greater use of these to spur a sluggish economy among other measures for the sector, industry experts said on Friday. "The budget should announce a fund like Singapore's Temasek that will invest only in early-stage Indian AI start-ups and lower long-term capital gain's tax for investing in AI-based firms," digital intelligence firm Germin8 founder chief executive Ranjit Nair told IANS. With the US and China racing ahead of India in AI research, AI entrepreneurship and government investment in AI, he said the budget should make it easier for start-ups to access capital, as they face an uphill task in early-stage funding. "The government bring policies that encourage AI companies. Ease of doing business means less bureaucracy so that entrepreneurs can build solutions without distractions," he said.

Technology for MSMEs: As the fourth industrial revolution is seemingly upon us with deep technologies including artificial intelligence (AI), machine learning (ML) being its key drivers, small and emerging businesses in India are increasingly becoming cognizant of upskilling themselves in modern technologies to drive their future growth, according to a survey by upskilling company Great Learning. AI/ML, digital marketing followed by design thinking are the most crucial skills required to stay relevant in the future, according to 25 per cent, 19 per cent, and 10 per cent of 307 businesses surveyed with the majority being small and medium-sized businesses. "Lack of skilled talent in technology is among the key problems that businesses face in India. Even though companies know about the need to bridge this gap, they must take steps immediately towards it," Hari Krishnan Nair, Co-founder, Great Learning told Financial Express Online. Importantly, despite the awareness, 47 per cent of the businesses haven't assigned the budget for upskilling yet even as nearly 30 per cent firms said that they spend over Rs 1 lakh per employee per annum on upskilling and another around 14 per cent spend over Rs 3 lakh per employee per annum for the same.

Artificial Intelligence (AI) and Machine Learning (ML), digital marketing and design thinking are the top skills that organisations will need to focus on to drive future growth, according to a new study. Despite the increased awareness around upskilling, the survey by ed-tech company Great Learning found that 47 per cent of the companies surveyed have still not assigned budgets for upskilling their workforce. "The technology skill gap among employees is one of the biggest challenges that organisations in India are beset with," Hari Krishnan Nair, Co-founder, Great Learning, said in a statement. "Skilled employees will continue to be the biggest asset for any organization going ahead and while options like lateral hiring and outsourcing may help in the short term, from a cost and effectiveness point of view, upskilling is the best way to stay competitive in the long run," Nair said. As per the survey, that involved more than 300 companies ranging from small and mid-size enterprises (SMEs) to large organisations, 25 per cent of all companies believe AI and ML are the most crucial skills needed to ensure an organisation"s future growth.