The recent success of Large Language Models (LLMs) has catalyzed an increasing interest in their self-correction capabilities. This paper presents a comprehensive investigation into the intrinsic self-correction of LLMs, attempting to address the ongoing debate about its feasibility. Our research has identified an important latent factor - the "confidence" of LLMs - during the self-correction process. Overlooking this factor may cause the models to over-criticize themselves, resulting in unreliable conclusions regarding the efficacy of self-correction. We have experimentally observed that LLMs possess the capability to understand the "confidence" in their own responses. It motivates us to develop an "If-or-Else" (IoE) prompting framework, designed to guide LLMs in assessing their own "confidence", facilitating intrinsic self-corrections. We conduct extensive experiments and demonstrate that our IoE-based Prompt can achieve a consistent improvement regarding the accuracy of self-corrected responses over the initial answers. Our study not only sheds light on the underlying factors affecting self-correction in LLMs, but also introduces a practical framework that utilizes the IoE prompting principle to efficiently improve self-correction capabilities with "confidence". The code is available at https://github.com/MBZUAI-CLeaR/IoE-Prompting.git.

Tensor PCA is a stylized statistical inference problem introduced by Montanari and Richard to study the computational difficulty of estimating an unknown parameter from higher-order moment tensors. Unlike its matrix counterpart, Tensor PCA exhibits a statistical-computational gap, i.e., a sample size regime where the problem is information-theoretically solvable but conjectured to be computationally hard. This paper derives computational lower bounds on the run-time of memory bounded algorithms for Tensor PCA using communication complexity. These lower bounds specify a trade-off among the number of passes through the data sample, the sample size, and the memory required by any algorithm that successfully solves Tensor PCA. While the lower bounds do not rule out polynomial-time algorithms, they do imply that many commonly-used algorithms, such as gradient descent and power method, must have a higher iteration count when the sample size is not large enough. Similar lower bounds are obtained for Non-Gaussian Component Analysis, a family of statistical estimation problems in which low-order moment tensors carry no information about the unknown parameter. Finally, stronger lower bounds are obtained for an asymmetric variant of Tensor PCA and related statistical estimation problems. These results explain why many estimators for these problems use a memory state that is significantly larger than the effective dimensionality of the parameter of interest.

We propose a vision for directing research and education in the ICT field. Our Smart and Sustainable World vision targets at prosperity for the people and the planet through better awareness and control of both human-made and natural environment. The needs of the society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.

In this paper we propose mmFall - a novel fall detection system, which comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud along with the body centroid, and (ii) a variational recurrent autoencoder (VRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is claimed to have occurred when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor provides several advantages, such as privacycompliance and high-sensitivity to motion, over the traditional sensing modalities. However, (i) randomness in radar point cloud data and (ii) difficulties in fall collection/labeling in the traditional supervised fall detection approaches are the two main challenges. To overcome the randomness in radar data, the proposed VRAE uses variational inference, a probabilistic approach rather than the traditional deterministic approach, to infer the posterior probability of the body's latent motion state at each frame, followed by a recurrent neural network (RNN) to learn the temporal features of the motion over multiple frames. Moreover, to circumvent the difficulties in fall data collection/labeling, the VRAE is built upon an autoencoder architecture in a semi-supervised approach, and trained on only normal activities of daily living (ADL) such that in the inference stage the VRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs. During the experiment, we implemented the VRAE along with two other baselines, and tested on the dataset collected in an apartment. The receiver operating characteristic (ROC) curve indicates that our proposed model outperforms the other two baselines, and achieves 98% detection out of 50 falls at the expense of just 2 false alarms.

Imagine a world where chatbots answer client e-mails regarding their investments, and software scours the Internet for the latest news, economic data and academic papers to create insightful investment reports. This might seem like the stuff of science fiction – albeit with a wealth management slant. Yet this is the leading edge of financial technology – or fintech – powered by the fast-growing field of artificial intelligence (AI), which is able to process and interpret vast amounts of data at lightning speed. AI is poised to reshape the financial industry, say those involved in developing the technology. For small independent firms, these advances potentially offer the biggest risks and rewards.

In this paper, a generalized multivariate Student-t mixture model is developed for classification and clustering of Low Probability of Intercept radar waveforms. A Low Probability of Intercept radar signal is characterized by a pulse compression waveform which is either frequency-modulated or phase-modulated. The proposed model can classify and cluster different modulation types such as linear frequency modulation, non linear frequency modulation, polyphase Barker, polyphase P1, P2, P3, P4, Frank and Zadoff codes. The classification method focuses on the introduction of a new prior distribution for the model hyper-parameters that gives us the possibility to handle sensitivity of mixture models to initialization and to allow a less restrictive modeling of data. Inference is processed through a Variational Bayes method and a Bayesian treatment is adopted for model learning, supervised classification and clustering. Moreover, the novel prior distribution is not a well-known probability distribution and both deterministic and stochastic methods are employed to estimate its expectations. Some numerical experiments show that the proposed method is less sensitive to initialization and provides more accurate results than the previous state of the art mixture models.

NEW YORK, March 14, 2017 – Genpact (NYSE: G), a global professional services firm focused on delivering digital transformation for clients, has signed a definitive agreement to acquire Rage Frameworks, a leader in knowledge-based automation technology and services providing Artificial Intelligence (AI) for the Enterprise. Terms of the deal are not disclosed. As part of its strategy to drive both digital-led innovation and digital-enabled intelligent operations for its clients, Genpact is investing in leading technologies, such as AI, that are transforming the way companies in many industries compete. Genpact will embed Rage's AI in business operations and apply it to complex enterprise issues to allow clients to generate insights and drive decisions and action, at a scale and speed that humans alone could not achieve. "As advanced technologies such as AI fundamentally change the definition of work, the ability for CXOs to find and leverage new solutions that combine the best elements of human expertise and machine intelligence, will be critical to their ability to gain and sustain competitive advantage," said NV'Tiger' Tyagarajan, president and CEO, Genpact.

Technology outsourcing company Genpact is set to acquire Rage Frameworks, an automation technology and services providing Artificial Intelligence (AI) for the enterprise. Genpact is making investment in leading technologies, such as AI, as part of its strategy to drive both digital-led innovation and digital-enabled intelligent operations for its clients. Genpact will embed Rage's AI in business operations and apply it to enterprise issues to allow clients to generate insights and drive decisions and action. "As advanced technologies such as AI fundamentally change the definition of work, the ability for CXOs to find and leverage new solutions that combine the best elements of human expertise and machine intelligence, will be critical to their ability to gain and sustain competitive advantage," said NV Tyagarajan, president and CEO of Genpact. Rage provides AI platform in cognitive computing that enables large enterprises to leverage AI techniques and simplify automation challenges.

Information contained on this page is provided by an independent third-party content provider. If you are affiliated with this page and would like it removed please contact pressreleases@franklyinc.com Addition of enterprise-level AI capabilities furthers Genpact's ability to drive digital transformation at speed and scale for clients Genpact (NYSE: G), a global professional services firm focused on delivering digital transformation for clients, has signed a definitive agreement to acquire Rage Frameworks, a leader in knowledge-based automation technology and services providing Artificial Intelligence (AI) for the Enterprise. Terms of the deal are not disclosed. As part of its strategy to drive both digital-led innovation and digital-enabled intelligent operations for its clients, Genpact is investing in leading technologies, such as AI, that are transforming the way companies in many industries compete. Genpact will embed Rage's AI in business operations and apply it to complex enterprise issues to allow clients to generate insights and drive decisions and action, at a scale and speed that humans alone could not achieve.

Genpact (NYSE:G), a global professional services firm focused on delivering digital transformation for clients, has signed a definitive agreement to acquire Rage Frameworks, a leader in knowledge-based automation technology and services providing Artificial Intelligence (AI) for the Enterprise. Terms of the deal are not disclosed. As part of its strategy to drive both digital-led innovation and digital-enabled intelligent operations for its clients, Genpact is investing in leading technologies, such as AI, that are transforming the way companies in many industries compete. Genpact will embed Rage's AI in business operations and apply it to complex enterprise issues to allow clients to generate insights and drive decisions and action, at a scale and speed that humans alone could not achieve. "As advanced technologies such as AI fundamentally change the definition of work, the ability for CXOs to find and leverage new solutions that combine the best elements of human expertise and machine intelligence, will be critical to their ability to gain and sustain competitive advantage," said NV'Tiger' Tyagarajan, president and CEO, Genpact.