User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is observed that whether the user's needs are met often triggers various sentiments, which can be pertinent to the successful estimation of user satisfaction, and vice versa. Thus, User Satisfaction Estimation (USE) and Sentiment Analysis (SA) should be treated as a joint, collaborative effort, considering the strong connections between the sentiment states of speakers and the user satisfaction. Existing joint learning frameworks mainly unify the two highly pertinent tasks over cascade or shared-bottom implementations, however they fail to distinguish task-specific and common features, which will produce sub-optimal utterance representations for downstream tasks. In this paper, we propose a novel Speaker Turn-Aware Multi-Task Adversarial Network (STMAN) for dialogue-level USE and utterance-level SA. Specifically, we first introduce a multi-task adversarial strategy which trains a task discriminator to make utterance representation more task-specific, and then utilize a speaker-turn aware multi-task interaction strategy to extract the common features which are complementary to each task. Extensive experiments conducted on two real-world service dialogue datasets show that our model outperforms several state-of-the-art methods.

On February 8, 2023, Marc Benioff and Salesforce made a Twitter announcement about the teaser, giving a few cryptic hints about what to expect. Now that the official announcement has been made, the mystery surrounding Einstein GPT is slowly being cleared up. The first generative AI for CRM, Einstein GPT, was announced by Salesforce, the top provider of CRM software worldwide. This ground-breaking technology aids businesses of all sizes in automating procedures, boosting productivity, enhancing customer service and much more. By fusing NLP and machine learning, Einstein GPT can assist businesses in better understanding their clients, increasing sales, and improving marketing.

Teaching machines to parse through large volumes of data to learn new concepts and rules is a critical area of development in artificial intelligence, experts told CNBC. That concept is called machine learning, and it's been a longtime goal for the AI discipline: The term was coined in 1959 by AI pioneer Arthur Samuel who defined it as a computer's ability to learn without being explicitly programmed. To do that, mathematical models are built and then fed with huge volumes of data, experts said. The algorithms learn to identify patterns and assumptions from those data sets that are then applied to process new information. "We want to be able to use the machine's own capability to learn from complex data," Eric Chang, senior director at Microsoft Research Asia, told CNBC.

Teaching machines to parse through large volumes of data to learn new concepts and rules is a critical area of development in artificial intelligence, experts told CNBC. That concept is called machine learning, and it's been a longtime goal for the AI discipline: The term was coined in 1959 by AI pioneer Arthur Samuel who defined it as a computer's ability to learn without being explicitly programmed. To do that, mathematical models are built and then fed with huge volumes of data, experts said. The algorithms learn to identify patterns and assumptions from those data sets that are then applied to process new information. "We want to be able to use the machine's own capability to learn from complex data," Eric Chang, senior director at Microsoft Research Asia, told CNBC.

BENGALURU: As the Indian IT services industry enters 2018, it will see more artificial intelligence (AI)-driven business than ever before, said analysts. While leaders of the $154-billion software services industry foresee fewer human involvement across projects; industry body Nasscom claims a lot of technology firms have started seeing AIbased decision-making. Clients of the IT service providers are increasingly using smart technologies such as using a chatbot over phone call to get banking related services giving rise to AI and automation-driven offerings. This, analysts said, requires more data and machine learning-driven work for technology services providers and lesser human intervention to do low-end work. Overall value of enterprise automation and AI is expected to reach nearly $10 billion globally in 2018, while spend on AI will be around $1.6 billion, wrote Phil Fersht, chief executive officer, HfS Research, in one of his recent blogs.