Dr. Ansar Kassim

AI has entered the next phase to revolutionize how he interact, retrieve, share and consume information. This phase is driven by significant strides in large models driven Generative AI with inferencing in the cloud and at the edge. It becomes more and more important for GenAI powered use-cases at the edge not just from sustainability or cost perspective but also from data privacy and security angle as well. Obviously, with strides in powerful compute and memory semi in datacenters helping train and inference those large models is now possible at the most popular edge device - the smartphone with full stack efforts from Arm, Qualcomm, MediaTek, Micron Technology, Samsung Electronics and others within the ecosystem enabling GenAI inferencing at the edge quickly and at low power. Counterpoint Technology Market Research having tracked smartphone and semiconductor evolution for decades, we believe smartphone industry is at a cusp of offering a "step change" in how we are going to interact with our devices and consume content or information with Gen AI or AI Smartphones. 📱We estimate GenAI smartphone share of overall smartphone shipments will reach 11% by 2024 and 43% by 2027 📱Samsung Electronics, an early mover in the space, will lead GenAI smartphone OEM rankings for 2024, while Apple will likely lead in 2025. 📱Qualcomm will lead the AI SoC space in 2024, capturing almost half of all GenAI smartphone shipments, followed by MediaTek with a 13% share. 📱Two key price tiers will emerge – ≥$600 and $400-$599, accounting for 9 out of every 10 GenAI smartphones sold in 2024. 📱China ecosystem of HONOR, Xiaomi Technology OPPO vivo Huawei China Mobile iFLYTEK Co., Ltd. and others will be instrumental in bringing GenAI to China and outside of China at mainstream levels in coming years. 📱Players such as Google with Gemini nano and Meta with Llama 2 and above with Microsoft, OpenAI will be key to driving GenAI experiences outside of China across smartphones. When will the GenAI trickle down the mainstream and mass market smartphone tiers? For more insights, the release below gives some viewpoints and feel free to reach out for detailed research and conversation on this topic.

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#GPT4o gives us a glimpse of things to come. The interface to customers is going to change so fast. The old GTM models are going to be obsolete. What Amazon did to retailers is soon going to happen to sales organizations.. where just being in front of customer is not going to be strategic advantage any more. The product / pricing / positioning would become a lot more important than just sales and marketing reach to customers. While the relationships would always matter, the mundane outreach activities could be completely taken over by #AI #sales #marketing

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Mastering O-RAN RIC: Key Considerations for AI/ML Model Selection When developing an application for a specific use case on O-RAN's RIC platform, keep two key points in mind. ◼ First, select the appropriate AI/ML model. Your choice between a simple model and a more sophisticated one, like deep learning, should depend on your goals and the specific environment (refer to the table below). Additionally, you can leverage AutoML tools. AutoML explores a variety of machine learning algorithms and model architectures, employing techniques such as Bayesian optimization, genetic algorithms, or reinforcement learning to identify the top-performing models. Example Scenario: - Scenario: High Variability and Complexity Data: Large, sequential datasets with non-linear patterns. Resources: Ample computational power and storage. Requirements: Real-time adaptation and low latency inferences. Choice: Deep Reinforcement Learning (DRL) with RNN/LSTM. This setup can learn from real-time data and adjust policies dynamically, handling complex patterns in user behavior and traffic conditions. - Scenario: Stable and Predictable Environment Data: Moderate-sized, non-sequential datasets with linear patterns. Resources: Limited computational power. Requirements: High interpretability and quick implementation. Choice: K-Means Clustering or Logistic Regression. These models are easy to implement, require fewer resources, and provide clear insights into user behaviors and traffic patterns. By carefully considering these criteria and conditions, you can select the most appropriate machine learning model for, for example, traffic pattern recognition in your specific O-RAN network use case. ◼ Second, remember that what you are developing is an independent piece of software, comprising an algorithm and one or more AI/ML models. While you can utilize modules and RIC SDK libraries from xApps and rApps, you have the flexibility to use different types of models in your software. For instance, it's common to have multiple models in your software, for example, a time series model for traffic prediction and a reinforcement learning model (or policy) to make decisions based on those predictions. The key advantage of having a O-RAN ALLIANCE RAN Intelligent Controller (RIC) is the ability to efficiently manage and optimize the radio network. By leveraging open interfaces and r/xApps, O-RAN facilitates the implementation of customized algorithms for specific applications. The O-RAN ALLIANCE outlines various use cases and establishes a policy framework to control the algorithms supporting these use cases. The whitepaper by Rimedo Labs provides an overview of the Traffic Steering use case within the O-RAN framework. It details the use case requirements, the operation of O-RAN nodes with specified interfaces, and the scenario as outlined in the O-RAN ALLIANCE specifications. #ORAN #OpenRAN #ORANRIC #TrafficSteering #AutoML #ModelSelectionCriteria #RICUsecases

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Not all models are created equal. While Large Language Models (LLMs) offer impressive capabilities, Domain-Specific Models (DSMs) provide a more practical and efficient alternative for enterprises wanting to adopt Generative AI now but have reservations. DSMs are tailored to specific tasks of a workflow, resulting in higher accuracy and lower costs for enterprise use cases compared to LLMs. As Aidan Gomez, CEO of Cohere, noted, "I can speak to the enterprise world, and there, enterprises are super risk averse. They’re always looking for opportunities, but they’re extremely risk averse. That’s the first thing that they’re thinking about." While replacing humans with Artificial General Intelligence (AGI) might be a distant possibility, DSMs can hyper-automate repetitive manual enterprise workflows today. Generative AI in the enterprise requires well-understood use cases, an iterative approach to automation, and clear metrics to measure success. DSMs can hyper-automate up to 90% of repetitive employee tasks, delivering tangible and measurable value today. In summary, DSMs offer cost-effective, highly accurate, and immediate value for automating enterprise workflows. If you have employees doing manual, repeatable workflows, reach out to learn more.

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The era of A.I.= 𝐀𝐩𝐩𝐥𝐞 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 for Apple users beckons as Apple unveiled its well-rounded strategy to bring Generative AI capabilities across its ecosystem at the #WWDC24 #AppleEvent yesterday. Following up on Google, Samsung Electronics, Qualcomm and Chinese brands with MediaTek's silicon, tools and implementations of #GenAI at the edge and in the cloud, Apple is not leaving its loyal users behind to take the AI experience to a new level with Apple Intelligence. It has been surprising to some industry watchers since Apple has been a year behind talking or unveiling GenAI and has been at a disadvantage vs Google, Meta or Microsoft which collect tons of data and early to train their foundation models kickstarting and making headways in the GenAI ecosystem. ⚡ Though, as expected, Apple has taken a holistic and highly integrated approach to bringing GenAI experiences to Apple ecosystem. ⚡At the foundation is the "𝐀𝐩𝐩𝐥𝐞 𝐒𝐢𝐥𝐢𝐜𝐨𝐧" one of the most powerful and advanced compute available in the market with capability to unlock those advanced AI capabilities natively. ⚡Building on this are Apple's "𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 𝐚𝐧𝐝 𝐀𝐝𝐚𝐩𝐭𝐞𝐫𝐬" trained with underlying privacy-focused "Responsible AI" principles. Adapters are the small neural network modules which fine-tune the models for specific tasks. ⚡Layered on the FM is the "𝐀𝐩𝐩 𝐈𝐧𝐭𝐞𝐧𝐭𝐬" framework which is the linchpin here in Apple's overall AI strategy. The framework exposes the AI capabilities and drives deeper integration into the Apple's prized application ecosystem. This is the holy grail of Apple's AI strategy. ⚡Further to that is the Siri 2.0 - where Siri becomes more useful beyond a prompt into a natural conversational and contextual language assistant built on top of the App Intent framework now powered by the LLMs. This allows Siri to be more intelligent and take new actions across the apps. (as i said holy grail). ⚡OpenAI ChatGPT-4o partnership adds an extra layer of intelligence for complex queries with universal data across apps ⚡We believe users and devs will trust Apple's "privacy-led" narrative both at the edge and in the cloud (Apple's Private Cloud Compute) to "opt in" with their data to boost the Apple Intelligence strategy. ⚡While its free for now, however, at some point Apple will either raise the hardware prices or go with "hardware-as-a-service" model and start charging for AI, with "Apple Intelligence+". ⚡It can also be a double-sided business model where developers can also earn some if Apple is generous enough as an incentive to integrate App Intents Overall, Counterpoint Technology Market Research we believe Apple will have more than 250 Million fully "Apple Intelligence" capable iPhones by end of 2025 for developers, Apple and users to start enjoying the scale of Apple Intelligence. For more detailed analysis and discussion please feel free to reach out. $AAPL

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https://lnkd.in/ecJMyKix "The key takeaway is that we’re at a critical inflection point, where multiple groundbreaking technologies are coming together. From now on we will continue to see massive acceleration of machine intelligence – and we can expect that to accelerate other cutting-edge technologies in turn." Exploration mindset with disciplined experimentation is needed to define and deliver business and social value.

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dApps: Real-Time O-RAN RIC Apps ◼ rApps, xApps and dApps - rApps: Applications that run on the non-real-time RIC, responsible for long-term control actions and decision-making that require timescales greater than one second. - xApps: Applications operating on the near-real-time RIC, which handle control loops ranging from 10 milliseconds to a few seconds. They are capable of closed-loop control for use cases like mobility management and network slicing. - dApps: Distributed applications running directly on RAN elements, such as CUs and DUs. They handle real-time control loops below 10 milliseconds, focusing on radio management, resource scheduling, beamforming, and modulation functionalities. dApps provide faster control for these functions by operating closer to the data source, ensuring fine-grained management and reducing overhead. ◼ Need for dApps While rApps and xApps deliver meaningful improvements, their architecture falls short in providing efficient control at sub-10 millisecond scales due to inherent latency in data collection and decision implementation. dApps address this gap by embedding control logic directly into the RAN nodes. They achieve reduced latency, programmability at the edge. ◼ Interaction with the User Plane: Access to low-level user data, such as IQ samples, allows dApps to manage interference, beam directionality, and modulation while maintaining privacy and security. ◼ Challenges and Architectural Evolution The concept of dApps, though promising, presents challenges related to resources, orchestration, and standardization: - Resource Management: With multiple dApps sharing the same physical resources at DUs/CUs, intelligent resource allocation is crucial to avoid conflicts. - Standardized Interfaces: The interfaces connecting dApps to the CUs/DUs need to be defined and standardized to facilitate seamless data collection and control. - Orchestration: An orchestration framework is essential to determine which tasks are suitable for xApps versus dApps and to mitigate conflicts between them. ◼ Proposed Architecture for dApps - dApps as Softwarized Containers - Southbound and Northbound Interfaces - Conflict Mitigation - Intent-Based Orchestration ◼ Real-World Applications - Beam Management: dApps can implement AI-driven beam selection logic to enhance spectrum utilization and reduce interference. - Ultra-Low Latency Applications: By managing scheduling and slicing at sub-10 millisecond timescales, dApps can support ultra-reliable low-latency communication (URLLC). Institute for the Wireless Internet of Things (WIoT), which has been a leader in research on real-time RIC and dApps, published a paper on the topic and made a more detailed proposal at the last RIC Forum. Collaboration between industry and research is crucial to realizing this vision, addressing challenges in standardization, resource management, and orchestration. Tommaso Melodia Salvatore D'Oro Michele Polese #ORAN #OpenRAN #RealTimeRIC #dApps

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➡ Essential aspects to cover for technology thought leadership 🔴 Technology thought leadership must evolve as you progress around the hype curve. In the early stages, it is more about you and the benchmark numbers that make your technology stand out in a press release. ✔ As your technology matures and prepares for commercial deployment, you must pivot about what you talk about. Less about speeds and feeds and more about putting your technology in a business context. 📄 Enjoy the brief read on pivots that can make your technology thought leadership stand out. This post was inspired by insights from Susan Thomas, James Watson, Brown Davis, and Mikko Nurmimäki. #ThoughtLeader #ThoughtLeadership #TechnologyThoughtLeadership

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As Generative AI enthusiasm grows, industries are working hard to demonstrate practical use cases, scale solutions to meet increasing demand, and innovate to unlock its full capabilities. Spirent's new eBook covers the complexities of AI’s impact, including challenges and practical strategies for adoption. Download it here. https://okt.to/bchF7O

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There are over 1.4 billion active iPhone users and over 2.2 billion active devices. Apple’s ability to drive adoption of new technology and innovation across its ecosystem is vast. At the WWDC 24 Apple brought AI to its applications with the introduction of Apple Intelligence; including incorporating ChatGPT into SIRI In addition to the staple upgrade announcements, there was the introduction of Vision OS2 which builds on the Vision Pro introduction to the metaverse to incorporate more access, content and services. The ability of big tech and web2 companies to drive mass adoption is significant, as we now see mass availability of AI across Apple devices and software and metaverse with the Vision 0S advancements. #apple #wwdc24 #ai #’metaverse

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'Decoupled' SMO The O-RAN ALLIANCE's Service Management and Orchestration (SMO) framework is increasingly critical in a multi-vendor O-RAN environment, primarily due to: Interoperability and Flexibility, Standardization and Openness, Centralized Management and Automation. ◼ Decoupled SMO The O-RAN ALLIANCE initiated the development of the 'decoupled SMO architecture' to meet the following industry demands. - Multi-Vendor Supply: Promotes interoperability among different vendors' products. - Diverse Operational Demands: Provides the necessary flexibility to tailor management and orchestration functions. - Varying Development Cycles: Accommodates independent updates and integration of RAN-specific and more generic functions, which often have different development cycles. - Integration with Legacy Systems: Allows seamless integration of new SMO functions with existing legacy systems, maintaining operational continuity and efficiency. - Integration into Existing Networks: Supports the integration of SMO functions into current network ecosystems, facilitating gradual enhancements without disrupting existing operations. - Deployment and Scaling: Decoupled architecture facilitates the flexible deployment and scaling of network functions, enabling operators to efficiently manage diverse network configurations and demand levels. ◼ Key SMO Services in Decoupled SMO Architecture: 1. Configuration Management: Responsible for setting up and maintaining the desired state of RAN elements, including provisioning, updating, and removing configuration parameters. 2. Performance Management: Focuses on monitoring, collecting, and analyzing performance metrics from RAN components to ensure they are operating efficiently and meeting service level agreements (SLAs). 3. Fault Management: Involves detecting, isolating, and correcting faults within the RAN. 4. Security Management: Ensures the integrity, confidentiality, and availability of the RAN. 5. Lifecycle Management: Manages the entire lifecycle of network functions, from initial deployment to decommissioning. 6. AI/ML Workflow Management: Incorporates artificial intelligence and machine learning algorithms to optimize network operations, improve resource utilization, and enable predictive maintenance. 7. Integration with Non-RT RIC and Near-RT RIC: Ensures communication and coordination with both non-real-time RIC for policy management and AI/ML-driven insights, as well as near-real-time RIC for control loop functions. 8. Data Analytics and Reporting: Provides tools for analyzing large volumes of data collected from the network, generating insights, and creating reports for better decision-making. In a decoupled SMO architecture, these services are orchestrated in a way that aims to streamline the management of complex, heterogeneous RAN environments while supporting the evolution toward more autonomous network operations. #ORAN #OpenRAN #ORANSMO #DecoupledSMO #MultiVendorORAN

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The Future of Enterprise Operations: Intelligent Automation 2.0 🌟 Did you know that enterprises are on the brink of a new era with Intelligent Automation 2.0 powered by generative AI? This isn't just about task automation anymore; it's about creating self-learning systems that analyze data, make decisions, and execute entire value chains independently. 📊 Key trends to watch: Autonomous workflows are overcoming traditional limitations, automating end-to-end processes without human involvement. AI agents are delivering human-like interactions, with some companies reporting a 60 percent reduction in full time equivalent hours. AI-first coding platforms are empowering citizen developers, enhancing productivity by up to 50%. And you can find out about even more trends in this interesting article. This integration of cutting-edge technologies is streamlining processes, boosting productivity, and enhancing customer experiences. With enterprises increasingly adopting Intelligent Automation, the landscape of work and business operations is set for transformative changes. How do you see Intelligent Automation impacting your industry or daily life? #GenerativeAI #EnterpriseTech #FutureOfWork

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AI is set to transform our lives in the 2030s, with 80 percent of consumers potentially using AI for major decisions like buying homes or making lifestyle changes 🏠💡 Ericsson ConsumerLab's new report reveals fascinating insights: more than 60 percent of even the most ardent AI fans worry about not having full control over AI's impact on their lives. Interestingly, 37 percent of AI hopefuls and 27 percent of AI fearful believe they will retain full control by 2030. And 95 percent of respondents believe at least some AI trends will become a reality. How do you see AI shaping your future? #AI #FutureTech #Ericsson

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3 Bold prediction of Gartner 1. GenAI models that enterprises use will be specific to either an industry or business function will be more than 50% in 2027 from 1% in 2023. 2. Businesses will use generative AI to create synthetic customer data, up from less than 5% in 2023 to 75% in 2026. 3. 30% of GenAI implementations will be optimized using energy-conserving computational methods, driven by sustainability initiatives by 2028. #ArtificialIntelligent #AI #DigitalTransformation

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An interesting essay by Frank Bruni, teaching at Duke University. Although he references Americans only in his analysis, I think we can all identify with this and the influence of social media in promoting this.. "The Age of Grievance I talk with students about Americans’ quickness to take offense these days — about our insistence on seeing ourselves as wronged. We live in an era defined and overwhelmed by grievance — by too many Americans’ obsession with how they’ve been wronged and their insistence on wallowing in ire. This anger reflects a pessimism that previous generations didn’t feel. The ascent of identity politics and the influence of social media, it turned out, were better at inflaming us than uniting us. They promote a self-obsession at odds with community, civility, comity and compromise. It’s a problem of humility. The Jan. 6 insurrectionists were delusional, frenzied, savage. But above all, they were unhumble. They decided that they held the truth, no matter all the evidence to the contrary. We live in profoundly unhumble times. Too many politicians present themselves not as our servants but as our saviors. Too many political actors interpret one group’s actual or possible gains as another group’s actual or possible losses. And too many of us view any and all events through a self-centric lens: How will this affect me? Or, at least as often, how will this diminish me? We conflate real offenses with imagined ones. We jumble righteous causes with pettier complaints. And what’s eclipsed along the way is a humble, mature understanding that we don’t get and shouldn’t expect a world completely to our liking, that life is a tangle of mercies and disappointments, and that the common good deserves as much consideration as our individual lots. A dose of humility would help illuminate that." (This is just a short extract, but hopefully you get his point). Published in the New York Times today.

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Bringing trust to AI. As you venture into the Wild West of generative AI, these three considerations will help you ensure that trust always stays in the saddle: ➡ Trusted Data AI needs accurate data to produce quality output. Give AI what it needs to succeed by combining company-specific knowledge (like a CRM) with unified, real-time data and external data, collected via integration tools. ➡ Trusted Architecture It’s critical to build in privacy and security from the start. Use a platform that offers data access controls, data masking for confidential information, and zero retention to prevent AI from storing proprietary prompts. ➡ Trusted Experiences When trust is baked into AI, when data is secured by the right tools, you can foster long-term relationships with both customers and employees who feel confident about the safety of their information. ✨ New video ✨ In the AI Wild West, your customer data is the new gold. Best protect it, from bad guy AI, partner. Learn more: https://sforce.co/3V4uy6F #AskMoreOfAI #Salesforce #TrustedData #TrustedArchitecture #TrustedExperience

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Multi-Vendor O-RAN RIC xApps/rApps Coordination O-RAN ALLIANCE O-RAN architecture includes the RAN Intelligent Controller (RIC). RIC comes in two flavors: the Non-Real-Time (>1sec) RIC rApps and the Near-Real-Time (10ms<Near RT<1sec) RIC xApps. These applications interact via the O-RAN A1, E2, O1 interfaces. ◼ Importance of Multi-Vendor xApps/rApps Coordination in RIC Operations A core objective of the O-RAN initiative is to facilitate a competitive, multi-vendor ecosystem. With standardized interfaces like O1, O2, A1, E2, xApps and rApps from different vendors can coexist on the same RIC platform, providing Telcos with the flexibility to select the best applications for their unique needs. However, effective coordination between multi-vendor xApps and rApps is vital for smooth RIC operations. These applications work at different layers and speeds to optimize network performance. Without clear coordination, they risk conflicting or redundant actions, ultimately degrading network performance. ◼ Coordination Example: A practical illustration of the importance of coordination involves an energy-saving rApp from Vendor A and a traffic-steering xApp from Vendor B. - Energy-Saving rApp: This application aims to conserve power by intelligently switching off unused or underused cells while maintaining adequate coverage. It monitors long-term traffic patterns to predict low-demand periods. - Traffic-Steering xApp: This application balances traffic loads between different cells and layers in near real-time. It ensures users are always connected to the best-performing cell, reducing congestion and improving user experience. ◼ Coordination in Practice - Initial Setup: Both applications initialize their data collection and configuration processes. The traffic-steering xApp begins adjusting cell traffic loads based on near-real-time metrics, while the energy-saving rApp assesses long-term usage patterns. - Monitoring: The energy-saving rApp notices a consistent low-traffic period and plans to deactivate a specific capacity cell for energy savings. Meanwhile, the traffic-steering xApp ensures that active cells can absorb the additional load. - Notification and Adjustment: The energy-saving rApp informs the traffic-steering xApp through the RIC framework of its intention to deactivate the capacity cell. The traffic-steering xApp then directs the remaining UEs connected to this cell to alternative coverage cells before deactivation. - Action Execution: Once the load balancing is complete and the capacity cell is safely offloaded, the energy-saving rApp executes the shutdown, ensuring no UE is impacted. This was a joint demo by Juniper Networks, AirHop Communications, Rimedo Labs, Keysight Technologies, and Vodafone at the RIC Forum hosted by NITA in March, showing energy savings of up to 25% at the capacity layer. #ORAN #OpenRAN #xApps #rApps #RIC #EnergySaving #TrafficSteering #RICAppsCoordination #RICForum

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#AI is set to change the very definition of work, and human collaboration will be key. https://okt.to/JrRpTY

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