Latest news with #NLP
Yahoo
4 hours ago
- Automotive
- Yahoo
Piper Sandler Initiates Coverage of SoundHound AI (SOUN) With Overweight, Cites AI Growth Potential
On Tuesday, Piper Sandler initiated coverage of SoundHound AI Inc. (NASDAQ:SOUN) with an Overweight rating and a $12 price target. The firm identifies SoundHound as a direct play on the AI revolution due to its voice AI platform. Piper Sandler analysts, including James E. Fish and Caden Dahl, highlight SoundHound's competitive advantage in delivering dynamic and real-time conversational AI experiences through its combined Automatic Speech Recognition/ASR and Natural Language Processing/NLP architecture. The company's Houndify platform is used by automotive, IoT, and restaurant organizations to enhance customer experiences and drive efficiencies through voice AI. A software engineer focused on a computer screen, writing code to create a conversational assistant. Piper Sandler sees quick-service restaurants/QSRs and customer experience as the most promising verticals for SoundHound. A key factor in Piper Sandler's outlook is SoundHound's acquisition of Amelia, which has allowed the company to enter the conversational AI space for contact centers. Analysts estimate this to be a $30 billion addressable market by 2027. Overall, they project a $47 billion serviceable opportunity across use cases by 2027, with SoundHound positioned as an early leader in these markets. SoundHound is also transitioning to a subscription-based model, with subscription and Over-Time revenues expected to comprise ~90% of total revenues by 2027. Piper Sandler believes that this shift, along with anticipated synergies from the Amelia acquisition, will boost margins, potentially by ~10% over the next few years. However, auto exposure currently accounts for around 25% of global production, and SoundHound's four key OEM clients are expected to see a 4% decline in sales this year. Analysts acknowledge these headwinds but remain bullish long-term. While we acknowledge the potential of SOUN to grow, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns and have limited downside risk. If you are looking for an AI stock that is more promising than SOUN and that has 100x upside potential, check out our report about the cheapest AI stock. READ NEXT: and . Disclosure: None. This article is originally published at Insider Monkey. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Time Business News
21-05-2025
- Business
- Time Business News
6 AI Techniques That Make Predictive Analytics Actionable
Imagine knowing what your customers will do next — not guessing, but actually knowing. That's the power of predictive analytics. But predictive analytics on its own can only take you so far. What truly makes it transformative is how it's powered and sharpened by artificial intelligence. In this article, we explore 6 AI Techniques that turn predictive analytics from a raw data process into a dynamic engine for real-world decisions. These aren't theoretical concepts — they are practical, proven methods that businesses use every day to anticipate customer needs, improve operations, and optimize marketing strategies. With the rise of data-driven decision-making, knowing how these techniques work can give you a substantial edge, whether you're in e-commerce, finance, healthcare, or SaaS. At the heart of most predictive analytics systems lies machine learning. These models don't just analyze data — they learn from it. Over time, they improve their performance based on outcomes, becoming more accurate and insightful. Supervised learning techniques like linear regression, decision trees, and support vector machines are commonly used to make sense of historical data and forecast future trends. Meanwhile, unsupervised learning algorithms such as clustering and anomaly detection uncover hidden patterns without being explicitly trained on labeled outcomes. By applying these AI Techniques, businesses can predict everything from customer churn to inventory needs. For example, a telecom company might use supervised learning to predict which customers are most likely to switch providers. That insight then becomes actionable when the company targets those users with retention campaigns. The ability to process and understand human language is critical in today's data landscape, where a huge volume of insights resides in unstructured text — think customer reviews, social media posts, and support tickets. Natural Language Processing (NLP) makes it possible to sift through all this noise and extract signals that can inform predictive analytics. NLP helps businesses forecast customer sentiment, predict brand perception changes, and even anticipate product demand based on conversations happening online. It goes beyond just word counts — NLP models understand context, tone, and intent. For instance, an e-commerce platform might use NLP to analyze product reviews and detect early signs of dissatisfaction. By identifying emerging issues before they become widespread, companies can take preventive action — such as adjusting product descriptions, issuing refunds, or improving quality — turning passive analysis into proactive intervention. Not all data is created equally, and time-dependent data requires specialized techniques. Time series analysis is a powerful method used in predictive analytics to model and forecast patterns over time. AI enhances this by automating feature extraction and model selection based on past temporal data. ARIMA models, exponential smoothing, and increasingly, deep learning-based models like LSTM (Long Short-Term Memory) networks are popular choices for this type of prediction. These models can account for seasonality, trends, and cyclical changes in data, providing more accurate forecasts. In the retail industry, for example, time series analysis can predict sales volume spikes before Black Friday or seasonal drops in Q1. By leveraging this data, inventory can be managed more efficiently, staffing can be optimized, and marketing campaigns can be aligned with predicted customer behavior. When you're dealing with complex datasets that have many dimensions — such as images, videos, or large sets of interconnected variables — traditional models can fall short. Deep learning steps in here, allowing businesses to handle and make sense of high-dimensional data. Neural networks with multiple layers (hence 'deep') can find intricate patterns in data that other algorithms miss. For example, convolutional neural networks (CNNs) excel at analyzing image data, while recurrent neural networks (RNNs) are used for sequential data, such as time series or text. Deep learning is used in predictive maintenance, where models can analyze sensor data from machinery to predict failures before they happen. This minimizes downtime, reduces repair costs, and improves operational efficiency — tangible actions from predictive insights. As deep learning models become more accessible through cloud platforms, their role in predictive analytics will only grow. Unlike other AI Techniques that rely on static datasets, reinforcement learning focuses on learning through interaction. It's particularly effective in environments where decisions need to adapt continuously based on feedback. In reinforcement learning, an agent makes decisions and receives rewards or penalties based on the outcome. Over time, the agent learns an optimal strategy to maximize rewards. This technique has been famously used in gaming and robotics but is now being applied in business contexts. One practical use is in pricing strategies for e-commerce. An AI system using reinforcement learning can adjust prices in real time based on customer behavior, competitor actions, and inventory levels. By learning which pricing decisions lead to the best conversion rates or margins, the model helps businesses act decisively and profitably. One of the criticisms of advanced AI systems — particularly deep learning — is that they often function as black boxes. You get a prediction, but not the 'why' behind it. Explainable AI addresses this issue by making models interpretable and their decisions understandable. In regulated industries like healthcare and finance, explainability isn't just a nice-to-have; it's essential. Doctors and bankers need to know the rationale behind a prediction before they can trust or act on it. By using techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), data scientists can open the black box. These tools highlight which features had the greatest impact on a prediction, making it easier for humans to validate and apply the results confidently. Explainable AI helps organizations bridge the gap between insight and action. When stakeholders understand why a model made a certain prediction, they're more likely to trust it and base critical business decisions on it. Each of these AI Techniques plays a unique role in making predictive analytics not just possible, but practical. By combining multiple methods — for instance, using NLP to gather sentiment data and time series analysis to model trends — businesses create richer, more accurate predictive models. But the most powerful outcomes occur when predictions are tied directly to actions. A marketing forecast is only valuable if it informs campaign strategy. A customer churn model only matters if it leads to better retention workflows. That's where many organizations stumble — they invest in data science but fail to close the loop with execution. For example, predictive insights gained from AI models can be directly integrated into marketing automation platforms, enabling personalized campaigns triggered by predictive scores. This is increasingly relevant in digital training and education, where platforms offering a generative AI marketing course can dynamically tailor content and delivery based on predictive user engagement models. AI is not just enhancing predictive analytics — it's democratizing it. Tools that were once reserved for data science experts are now available through no-code or low-code platforms. Cloud-based machine learning services like Google Cloud AutoML, Amazon SageMaker, and Microsoft Azure ML Studio allow marketers, product managers, and analysts to build and deploy AI-powered prediction systems with minimal technical knowledge. Moreover, advancements in generative AI are complementing traditional predictive models. Generative AI can simulate future scenarios, generate synthetic training data, and even create automated insights based on predictions, further closing the gap between data analysis and decision-making. The convergence of these technologies means the barriers to entry are lower than ever, but the expectations are higher. Organizations must not only adopt AI Techniques but also align their operations to act on insights swiftly and strategically. Predictive analytics is no longer just about understanding what might happen — it's about knowing what to do when it does. The six AI Techniques explored here — machine learning, NLP, time series analysis, deep learning, reinforcement learning, and explainable AI — are redefining how data informs action. For businesses aiming to compete in fast-moving markets, applying these methods is no longer optional. It's the path to faster decisions, smarter strategies, and measurable outcomes. By investing in the right mix of AI-powered analytics and operational integration, companies can truly harness the predictive power of their data — and act on it with confidence. TIME BUSINESS NEWS
Cision Canada
21-05-2025
- Business
- Cision Canada
Bossjob Showcases Next-Gen AI Recruitment Platform at TEAMZ Web3/AI Summit 2025
MANILA, Philippines, May 21, 2025 /CNW/ -- Bossjob, the premier AI-driven recruitment platform, made waves as the exclusive HR tech invitee at Japan's flagship TEAMZ WEB3/AI Summit 2025 (April 16-17, Tokyo). The platform unveiled its cutting-edge AI-powered recruitment suite, featuring instant candidate profiling, smart talent pool management and its new Copilot tool. These innovations mark the dawn of Recruitment 3.0, delivering end-to-end digital hiring solutions tailored for the Web3 ecosystem. AI Redefine s Hiring Efficiency At the event, Bossjob unveiled its next-generation AI recruitment capabilities, including an AI resume analysis engine that processes thousands of resumes and matches candidate skills with job requirements in real time using its proprietary NLP algorithms. It also previewed an upcoming AI assistant that enables dynamic, multi-stage candidate interactions for automated resume collection and qualification. These features enhance hiring efficiency, while delivering precision-driven talent solutions for Web3 companies. Bossjob has built an extensive HR database for the Web3 and AI sectors through its AI technology. The talent pool's latest upgrade introduces intelligent management capabilities that transform how companies identify and engage top tech talent. The enhanced talent pool now supports AI-driven resume archiving and categorization with automatic "Inactive" tagging for bulk screening. It also integrates multiple resume sources, simplifying talent data consolidation by enabling one-click additions and instant profile imports from external links. The new Copilot tools uses AI to generate GPT-powered "job-candidate fit" scores, allowing employers to filter mismatches and compare candidates side by side. These tools significantly reduce screening time and improve hiring outcomes. A Dedicated Job Portal Launched to Address Japan's 30B Yen Web3 Talent Gap Japan's Web3 industry has experienced rapid growth, with a surge in blockchain, NFT, and metaverse-related businesses. According to Japan's Ministry of Economy, Trade and Industry, the local Web3 market grew by 35% in 2023, yet the sector faces a talent shortage of more than 20,000 skilled professionals. Traditional recruitment platforms fail to meet the demand for niche skills such as smart contract development and DeFi architecture, while job seekers struggle with fragmented job postings and low matching accuracy. In response, Bossjob's AI-driven precision matching addresses both challenges, culminating in the launch of its dedicated Web3 job portal. As an AI-powered platform, Bossjob delivers personalized job recommendations for Web3 professionals and includes direct chat functionality for seamless employer-candidate communication. This approach improves recruitment accuracy while adapting to the sector's evolving talent needs, helping businesses secure high-level technical talent and promoting long-term growth in the Web3 labor market. A Bossjob Japan spokesperson stated, "We deeply understand the urgent demand for specialized talent in Web3 and recognize the limitations of traditional recruitment in this emerging field. By creating a Web3-dedicated job platform, we aim to bridge the gap and provide robust talent support for Japan's Web3 ecosystem." Industry analysts note that as the Japanese government accelerates its Web3 strategy, the talent supply-demand imbalance will intensify. Bossjob's targeted approach not only taps into a corporate recruitment service market valued at over 30 billion yen but also positions the platform as key infrastructure for Japan's Web3 growth by enhancing talent allocation efficiency. Launched in 2017 in Singapore, Bossjob is a pioneering AI-powered recruitment platform serving 13 countries globally. To date, it boasts over 4.5 million registered job seekers and 55,000 corporate users. By utilizing advanced AI capabilities, Bossjob automates everything from job creation to optimal talent matching, solidifying its leadership in the Web3 recruitment space and heralding the era of AI Recruitment 3.0.
Bloomberg
20-05-2025
- Business
- Bloomberg
Bloomberg Expands Chatbots in IB for Easy Collaboration Across Firms
Announcements Share Chatbots help IB community save time and surface insights in secure communications with counterparties Bloomberg today expanded its IB Connect solutions to transform the way financial professionals interact with their counterparties and collaborate with front-, middle-, and back-office colleagues. IB Connect: Cross-Firm Chatbots ('bots') is an add-on service that helps Bloomberg Anywhere users bring in-house, proprietary bots to IB chat rooms with multiple firms. Why it's important: Clients can build their bots to automatically surface relevant information from their internal applications and share it in their IB communications with other firms. This includes status updates from order management systems, market reports from research content management platforms, and trade ideas from client relationship management tools. By bringing a bot into an IB chat room, colleagues and counterparties can get information by directing a question to the bot without having to leave their discussions. How it works: The solution uses a two-way API enriched with Bloomberg's NLP to provide structured data and context to IB chats directed to the bot. That technology is fine-tuned for capital markets and understands finance lingo to help make communications more machine-readable. Whether a bot is posting a notification or responding to a user query, relevant information like order updates and market commentary can be delivered by the bot in neatly organized visuals called BCards. The bots can also post free text, tables, links and @mentions. IT teams can customize bots for their firm's unique tech stacks, in line with Bloomberg's API protocols. The big picture: The bots complement Bloomberg's suite of IB Connect solutions designed for users to seamlessly integrate their IB communications with in-house workflow tools, helping streamline collaboration with colleagues and counterparties. This new solution builds on IB Connect: Intra-Firm Chatbots as part of Bloomberg's efforts to make it easier for the IB community to network across the buy- and sell-side and collaborate on multi-asset investment opportunities. Roger Birch, Head of Product: Communication and Collaboration Systems for IB at Bloomberg, said: 'Financial market professionals spend too much of their valuable time pivoting between applications and juggling hundreds of chat requests. By welcoming Cross-Firm Chatbots, we're helping our clients build information hyperloops so they can focus on what truly matters in their chats with counterparties – delivering insights, building client relationships, and sharing the next big trade idea.' About Instant Bloomberg (IB): IB helps Bloomberg Terminal users connect with the financial markets and each other in real time to exchange ideas, share actionable information and optimize communication workflows in a secure environment. Bloomberg offers additional services that enable clients to seamlessly integrate IB with their firm's in-house applications, helping streamline collaboration with colleagues and counterparties. About Bloomberg Terminal: For more than four decades, the Bloomberg Terminal has revolutionized the financial services industry by bringing transparency and innovation to the capital markets. Trusted by the world's most influential decision-makers, the Terminal provides real-time access to news, data, insights and trading tools that help our customers turn knowledge into action. About Bloomberg: Bloomberg is a global leader in business and financial information, delivering trusted data, news, and insights that bring transparency, efficiency, and fairness to markets. The company helps connect influential communities across the global financial ecosystem via reliable technology solutions that enable our customers to make more informed decisions and foster better collaboration. For more information, visit or request a demo. Media Contact: Robert Madden, rmadden29@ +1 (646) 803-0794 Read more
Associated Press
20-05-2025
- Business
- Associated Press
WorkTango Expands AI Suite Across Platform to Empower Leaders and Drive Engagement
AUSTIN, TX, May 20, 2025 (GLOBE NEWSWIRE) -- WorkTango, a leading SaaS-based employee experience technology company, today announced the second installment of Generative AI across its entire platform, introducing new capabilities designed to help organizations better understand their workforce and enable more effective leadership. With this expansion, Generative AI is now embedded across key areas of the WorkTango Employee Experience Platform, including Surveys & Insights and Recognition & Rewards. The enhanced capabilities deliver timely, actionable insights into employee sentiment, team dynamics, and leadership behaviors, empowering HR to drive strategy with data and enabling people leaders to take focused action to improve employee engagement and performance. 'Leaders today are being asked to do more, often with less visibility into how their teams are really doing,' said Nadir Ebrahim, Chief Product & Customer Officer at WorkTango. 'Expanding AI across our entire platform suite gives them faster, clearer insights they can use to lead more effectively and support their teams in real-time.' Key WorkTango AI features include: Powered by advanced Natural Language Processing (NLP), these tools analyze open-ended survey responses to uncover common themes and underlying sentiment. Leaders can quickly grasp what employees are saying and focus efforts where they matter most. This new feature scores the content of Recognition messages based on five key factors, so organizations can promote more thoughtful appreciation that drives participation and boosts employee engagement. Higher-quality Recognition has been shown to drive greater employee participation and boost overall engagement across the platform. Leadership Score and Archetypes surface how employees demonstrate core strengths such as strategy, collaboration, and communication. These insights help guide leadership development, support targeted coaching, and ensure alignment with organizational culture and business objectives. This feature provides personalized, real-time prompts to managers based on feedback and Recognition activity. By encouraging timely follow-up and reinforcing effective behaviors, Leadership Nudges help people leaders stay engaged and responsive in their day-to-day interactions. This tool helps users craft clearer, more thoughtful Recognition by offering suggested improvements to tone, grammar, and structure at the click of a button. With multilingual support and a focus on inclusive, high-quality messaging, it encourages more meaningful appreciation. Currently in Beta, this feature drives even deeper employee connection and engagement. 'We're loving the AI so far and are excited to see what's coming next,' said Paula Yrigoyen, Lead Talent Partner at ACT. 'For the first time, our executive team and leaders can more easily access survey results quickly and explore the dashboards on their own. It's made a big impact already.' WorkTango AI is designed as a connected experience, translating raw data into actionable insights to help organizations lead with intention and build stronger, more engaged teams. As expectations for people leaders continue to grow, the enhanced WorkTango platform meets the moment by offering tools that are intuitive, intelligent, and grounded in real employee experience data. Each WorkTango AI feature is built to help organizations support their employees with intention and clarity. This reflects a commitment to continuous innovation and signals a future of even more powerful, people-first solutions. Trusted by organizations like Habitat for Humanity, American Eagle Financial Credit Union, Schoox, and Eventbrite, WorkTango helps companies across all industries elevate the employee experience and build more connected, engaged workplaces. To learn more about WorkTango's AI-powered Employee Experience Platform, visit WorkTango PR [email protected]
