Why Higher Ed Needs to Go All-in on Digital

In the wake of the COVID-19 pandemic, the majority of students within the 18-24-year-old demographic now expect hybrid learning environments–even once we are beyond the pandemic. And a vast number of adult learners are seeking options that accommodate their work and family lives now that it’s clear that effective learning can indeed occur virtually. Implementing cloud technologies and achieving digital maturity within higher education will enable institutions to be innovative and responsive to evolving student preferences, while being prepared for future disruptions.

The state of digital maturity

In February and March 2021, Boston Consulting Group (BCG), in partnership with Google, surveyed U.S. higher education leaders on their views of the state of digital maturity in the higher education sector. This survey found that institutional and technology leaders strongly agreed that moving legacy IT systems to the cloud, centralizing and integrating data, and increasing the use of advanced analytics is necessary to make a successful digital transformation, and ultimately achieve digital maturity.

But what is digital maturity? Digital maturity—a measure of an organization’s ability to create value through digital delivery—focuses on three areas of technological advancement that drive large-scale innovation:

1. Using cloud infrastructure
2. Expanding access to data
3. Using that data to improve processes through advanced analytics, such as Artificial Intelligence and Machine Learning (AI/ML)

Although university leaders agree on prioritizing digital maturity, more than 55% said they considered their schools to be “digital performers” or “digital leaders.” However, only 25% of tech leaders at these universities stated that their schools regularly use data analytics. As with corporations and governments, higher education institutions face barriers to technological innovation, such as:

• Competing priorities to meet step-change goals and decentralized decision making
• Budget constraints
• Cultural resistance to change
• Tech staff skillset gaps

Still, leaders understand that the way to overcome institutional inertia is with a strong, goal-oriented vision of what is best for the institution overall. Although only a handful of schools have reached digital maturity as we define it, others can learn a great deal from their examples. Here are the top takeaways from higher education leaders who successfully transformed their institutions:

Digital solutions can improve the student journey in many ways

As digital capabilities hold the key to dealing effectively with declining enrollment and rising costs, higher ed leaders identified four goals that are critical to improving performance:

1. Improve the student journey
2. Increase operational efficiency
3. Scale computing power in advanced research
4. Innovate education delivery

The research found that technology investments can help enhance the student journey in the recruiting and retention of students, improving digital education delivery, government funding, and donations from alumni. Digital maturity can make institutions more agile and efficient in delivering education that aligns with the changing societal norms, evolving student preferences, and future disruptions. Survey participants shared that they plan to increase the use of the cloud by more than 50% over the next three years. By shifting legacy IT systems to the cloud, institutions can increase scalability, lower the cost of ownership, and improve operational agility, while offering a more secure, long-term data storage solution.

Cloud-native software-as-a-service (SaaS) solutions provide an excellent platform for centralizing data. However, institutions that attempt to “lift and shift” their legacy systems to the cloud may encounter challenges to achieving measurable improvements in data integration and cost reduction. Higher ed leaders must realize that centralizing data and transitioning to the cloud do not happen simultaneously.

Leaders who are able to articulate a strong vision and commitment will experience a more successful technology transformation. By linking their vision to specific needs, such as more effective recruiting, leaders will find their technology investments will have a more substantial return. University presidents should base their decisions about which systems to move, when, and how on desired performance outcomes.

Big visions become a reality with small steps. Small pilot projects are an excellent way to start the journey toward digital maturity. Small steps toward a significant transformation can reduce resistance to change, build positive momentum, and produce better student outcomes. Read the full report here. If you’d like to talk to a Google Cloud expert, get in touch. 

Large Machine Learning (ML) models – such as large language models, generative AI, and vision models – are dramatically increasing the number of trainable parameters and are achieving state-of-the-art results. Increasing the number of parameters results in the model being too large to fit on a single VM instance thus demands distributed compute to spread the model across multiple nodes. Google Kubernetes Engine (GKE) has built-in support for NCCL Fast Socket, to help improve the time to train large ML models with distributed, multi-node clusters.

Enterprises are looking for faster and cheaper performance to train their ML models. With distributed training, communicating gradients across nodes is a performance bottleneck. Optimizing inter-node latency is critical to reduce training time and costs. Distributed training uses collective communication as a transport layer over the network between the multiple hosts. Collective communication primitives such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, and point-to-point send and receive are used in distributed training in Machine Learning. 

The NVIDIA Collective Communication Library (NCCL) is commonly used by popular ML frameworks such as TensorFlow and PyTorch. It is a highly optimized implementation for high bandwidth and low latency between NVIDIA GPUs. Google developed a proprietary version of NCCL called NCCL Fast Socket to optimize performance for deep learning on Google Cloud.

NCCL Fast Socket uses a number of techniques to achieve better and more consistent NCCL performance.

• Use of multiple network flows to attain maximum throughput. NCCL Fast Socket introduces additional optimizations over NCCL’s built-in multi-stream support, including better overlapping of multiple communication requests.
• Dynamic load balancing of multiple network flows. NCCL can adapt to changing network and host conditions. With this optimization, straggler network flows will not significantly slow down the entire NCCL collective operation.
• Integration with Google Cloud’s Andromeda virtual network stack.This increases overall network throughput by avoiding contentions in virtual machines (VMs).

We tested (NVIDIA NCCL tests) the performance of NCCL Fast Socket vs NCCL on various machine shapes with 2 node GKE clusters.

The following chart shows the results. For each machine shape, the NCCL performance without Fast Socket is normalized to 1. In each case, using NCCL Fast Socket demonstrated increased performance in a range of 1.3 to 2.6 times faster internetwork communication speed.

As a built-in feature, GKE users can take advantage of NCCL Fast Socket without changing or recompiling their applications, ML frameworks (such as TensorFlow or PyTorch), or even the NCCL library itself. To start using NCCL Fast Socket, create a node pool that uses the plugin with the --enable-fast-socket and --enable-gvnic flags. You can also update an existing node pool using gcloud container node-pools update.

gcloud container node-pools create NODEPOOL_NAME \
    --accelerator type=ACCELERATOR_TYPE, count=ACCELERATOR_COUNT \
    --machine-type=MACHINE_TYPE \
    --cluster=CLUSTER_NAME \
    --enable-fast-socket \
    --enable-gvnic

To achieve better network throughput with NCCL, Google Virtual NICs (gVNICs) must be enabled when creating VM instances. For detailed instructions on how to use gVNICs, please refer to the gVNIC guide. 

To verify that NCCL Fast Socket has been enabled, view the kube-system pods:

kubectl get pods -n kube-system

And the output should b similar to:

NAME                         READY   STATUS    RESTARTS   AGE
fast-socket-installer-qvfdw  2/2     Running   0          10m
fast-socket-installer-rtjs4  2/2     Running   0          10m
fast-socket-installer-tm294  2/2     Running   0          10m

To learn more visit GKE NCCL Fast Socket documentation. We look forward to hearing how NCCL Fast Socket improves your ML Training experience on GKE.

With the advent of the pandemic, contact center traffic has increased by as much as 300%, taxing center capabilities. To help handle the surge, and keep up with heightened customer demands, many customer experience providers have deployed automation in the form of virtual agents that serve as the first—and, sometimes the last—line of customer support. How effective these virtual agents are in delivering timely, complete customer service depends in large part on the contact center’s infrastructure and the power of the automation solution.

Customer support providers can now start to reimagine customer experience with Conversational AI and take advantage of the Google Cloud-Automation Anywhere partnership to modernize their operations. 

Bringing together the Google Cloud Contact Center AI (CCAI) solution and the Automation Anywhere cloud-native Automation 360 platform, the partnership helps contact centers remain competitive by improving their performance metrics and customer satisfaction to exceed the continuously growing expectations.

Information here, there, anywhere—with limited access

Over the years, many contact centers have accumulated a multitude of systems, often creating a disconnected infrastructure. As a result, both human and virtual agents alike are challenged to keep up with growing customers’ expectations for timely, accurate, and complete service. Without up-to-date software and infrastructure, human agents have to perform a “swivel-chair” maneuver, logging in to the different systems, sifting through records, copying the needed information, and deciding what the next action should be. This approach is not conducive to achieving lower average handle times (or AHT), reduced processing errors, or increased customer satisfaction.

In such a siloed environment, virtual agents may not connect to all the relevant data systems and applications. That also limits what they can do to support human agents. Typically, a virtual agent can handle basic customer requests, such as providing banking customers with their account balances. For more complex requests, such as applying for a line of credit, a virtual agent still must transfer customers to human agents, and the swivel-chair maneuver begins. 

Enter the RPA-assisted AI-powered virtual agent

The combination of Google Cloud Contact Center AI and Automation Anywhere’s Automation 360 RPA platform can help contact centers get the maximum benefit from utilizing virtual agents, enriching customer engagements. Further, by integrating automation, opening up APIs, and creating new processes for virtual agents, customer experience teams can help streamline operations by enabling users to quickly access the information they require. This helps minimize the need for the “swivel-chair” maneuver.

Automation 360 makes it possible for the CCAI virtual agents to access all systems and applications in both legacy and modern infrastructure, helping resolve every case quickly and easily. Not only can the virtual agents respond faster with answers, but they can complete more complex end-to-end requests. With RPA, customers are reporting 66% improved efficiency of contact center operations while exceeding their AHT reduction goals. 

This video illustrates how such automation can dramatically reduce the processing time of a customer service request, and you can read more about the details of contact center automation, as well.

Additionally, a comprehensive development platform, the Google Cloud CCAI Dialogflow, powers virtual agents—chatbots and voicebots—with Conversational AI to deliver lifelike customer experiences anytime a customer reaches out to a brand. CCAI Agent Assist helps human agents with turn-by-turn guidance, ready-to-deliver answers, and ready-to-send responses. CCAI Insights identifies key metrics such as call drivers and customer sentiment for workflow optimization. 

Living up to their potential

TELUS International, a leading digital customer experience provider and long-time partner of both Google Cloud and Automation Anywhere, has been leveraging virtual agents to enhance the employee and customer experience.  

“We are very proud of the enormous value that we can provide our clients through combining our expertise in customer experience and digital transformation alongside the innovative solutions of our technology partners,” Jim Radzicki, CTO of TELUS International, explains. “For instance, through leveraging Google Cloud Contact Center AI and Automation Anywhere’s RPA integration, we are able to expand the capabilities of virtual agents to process a wider variety of customer requests while allowing our team members to focus on the most critical conversations and creating a meaningful connection with every customer.” 

With CCAI and Automation 360, virtual agents can help contact centers deliver 24/7, comprehensive, accurate service, all of which helps eliminate wait times—even with heavy traffic. And this is just the start. 

With deeper integration planned between Automation 360 and CCAI, RPA can augment CCAI Agent Assist’s abilities to help human agents by bringing untapped case-sensitive information to their fingertips. Furthermore, Automation 360 Bot Insight can complement CCAI Insights’ as well as TELUS International’s Intelligent Insights, a tool-agnostic platform to monitor and manage RPA solutions and bots, with backend data access metrics.

At Google Cloud and Automation Anywhere, we’ll continue developing our contact center solution to extend automation capabilities for better customer service and greater customer satisfaction. Instead of constantly swiveling, agents can once again be at the center of the call center action, taking their work—and the experience of their customers—to the next level.

At Google Cloud, we continue to invest in key technologies to progress towards our true north star on invisible security: making strong security pervasive and simple for everyone. Our investments are based on insights from our world-class threat intelligence teams and experience helping customers respond to the most sophisticated cyberattacks. Customers can tap into these capabilities to gain perspective and visibility on the most dangerous threat actors that no one else has. 

Recent advances in artificial intelligence (AI), particularly large language models (LLMs), accelerate our ability to help the people who are responsible for keeping their organizations safe. These new models not only give people a more natural and creative way to understand and manage security, they give people access to AI-powered expertise to go beyond what they could do alone. 

At the RSA Conference 2023, we are excited to announce Google Cloud Security AI Workbench, an industry-first extensible platform powered by a specialized, security LLM, Sec-PaLM. This new security model is fine-tuned for security use cases, incorporating our unsurpassed security intelligence such as Google’s visibility into the threat landscape and Mandiant’s frontline intelligence on vulnerabilities, malware, threat indicators, and behavioral threat actor profiles. 

Google Cloud Security AI Workbench powers new offerings that can now uniquely address three top security challenges: threat overload, toilsome tools, and the talent gap. It will also feature partner plug-in integrations to bring threat intelligence, workflow, and other critical security functionality to customers, with Accenture being the first partner to utilize Security AI Workbench. 

The platform will also let customers make their private data available to the platform at inference time; ensuring we honor all our data privacy commitments to customers. Because Security AI Workbench is built on Google Cloud’s Vertex AI infrastructure, customers control their data with enterprise-grade capabilities such as data isolation, data protection, sovereignty, and compliance support. 

Preventing threats from spreading beyond the first infection

We already provide best-in-class capabilities to help organizations immediately respond to threats. But what if we could not just identify and contain initial infections, but also help prevent them from happening anywhere else? With our AI advances, we can now combine world class threat intelligence with point-in-time incident analysis and novel AI-based detections and analytics to help prevent new infections. These advances are critical to help counter a potential surge in adversarial attacks that use machine learning and generative AI systems. That’s why we’re excited to introduce:  

• VirusTotal Code Insight uses Sec-PaLM to help analyze and explain the behavior of potentially malicious scripts, and will be able to better detect which scripts are actually threats. 
• Mandiant Breach Analytics for Chronicle leverages Google Cloud and Mandiant Threat Intelligence to automatically alert you to active breaches in your environment. It will use Sec-PaLM to help contextualize and respond instantly to these critical findings.

These new updates build on the existing AI in Google’s industry-leading solutions. For example, Chronicle Security Operations already uses frontline intelligence, integrated reasoning, and machine learning to identify initial infections, prioritize impact, and contain threats. Another example is reCAPTCHA Enterprise, which uses image noising capabilities to help protect your site from adversaries that leverage novel AI advances, greatly enhancing our defenses against bots. 

Adding intelligence to reduce toil

At Google Cloud, we help organizations modernize security wherever they are, in part by simplifying their security tools and controls whenever possible. Advances in generative AI can help reduce the number of tools organizations need to secure their vast attack surface areas and ultimately, empower systems to secure themselves. This will minimize the toil it takes to manage multiple environments, to generate security design and capabilities, and to generate security controls. Today, we’re announcing: 

• Assured OSS will use LLMs to help us add even more open-source software (OSS) packages to our OSS vulnerability management solution, which offers the same curated and vulnerability-tested packages that we use at Google.
• Mandiant Threat Intelligence AI, built on top of Mandiant’s massive threat graph, will leverage Sec-PaLM to quickly find, summarize, and act on threats relevant to your organization. 

These announcements build on existing capabilities that help customers centralize visibility and control, detect targets, and improve security across their platform. For example, Security Command Center (SCC) uses always-on machine learning to detect malicious scripts executing in the customer container environment and immediately alert the customer. In addition, Cloud Data Loss Prevention leverages machine learning to find and classify data, and with Confidential Computing you can collaborate on, train, and deploy sensitive and regulated AI models in the cloud, all while preserving confidentiality. 

Evolving how practitioners do security to close the talent gap 

At Google, we believe that to truly democratize security, we need to first acknowledge that AI will soon usher in a new era for security expertise that will profoundly impact how practitioners “do” security. Most people who are responsible for security — developers, system administrators, SRE, even junior analysts — are not security specialists by training. 

Imagine a world where novices and security experts are paired with AI expertise to free themselves from repetition and burnout, and accomplish tasks that seem impossible to us today. To help power this evolution, we’re embedding Sec-PaLM-based features that can make security more understandable while helping to improve effectiveness with exciting new capabilities in two of our solutions: 

• Chronicle AI: Chronicle customers will be able to search billions of security events and interact conversationally with the results, ask follow-up questions, and quickly generate detections, all without learning a new syntax or schema.
• Security Command Center AI: Security Command Center will translate complex attack graphs to human-readable explanations of attack exposure, including impacted assets and recommended mitigations. It will also provide AI-powered risk summaries for security, compliance, and privacy findings for Google Cloud.

These new releases bolster our existing efforts to tackle these issues through capabilities like IAM Recommender, which suggests permissions better suited to actual usage patterns. We will soon be augmenting this capability to cover organizational policies, further enabling the administrator to help improve the security posture of their organization. In addition, Mandiant Automated Defense applies machine learning to help reduce the repetitive Tier 1 alert triage problem and address alert fatigue. 

Offering availability 

VirusTotal Code Insight, available now in Preview, is our first example of putting Security AI Workbench to work for our customers. We will be rolling out other offerings to trusted testers in coming months, and they will be available in Preview more broadly this summer. Click here for the demo. 

Security AI Workbench, including Sec-PaLM and partner integrations, in addition to the product innovations described in our demo, are all building blocks for a larger effort to elevate security across the ecosystem. So far, that effort: 

• Provides assistive functions to rapidly develop IT generalist talent to Tier 1 security operator status in a way that wasn’t previously feasible. Security Command Center now can summarize threat intelligence insights and findings for Google Cloud, and Chronicle can quickly generate YARA-L rules or other detections. 
• Provides advanced functions such as iterative query and multivariate detection generation, conversational filtering and interaction with results, and smart case awareness to empower advanced Tier 2 and 3 security operators to focus on threat analysis instead of struggling with process and toil. Mandiant Threat Intelligence users now can elevate their core competencies to hunt, investigate, and remediate threats — using the same tools our own Mandiant experts use. 
• Fuses threat intelligence and AI-based analytic capabilities, which are unsurpassed in the market. VirusTotal Code Insight enables security teams to help gain insights and identify threats in suspicious code. This can significantly enhance their ability to detect and mitigate potential attacks.

However, this is just an initial step. We’ll continue to iterate and innovate, and we encourage customers and partners to leverage Security AI Workbench in new and exciting ways. Moving forward, we anticipate many new use cases to emerge over time. 

Building a safer future

While generative AI has recently captured the imagination, Sec-PaLM is based on years of foundational AI research by Google and DeepMind, and the deep expertise of our security teams. This work includes new efforts to expand our partner ecosystem to provide businesses with security capabilities at every layer of the cybersecurity stack. We have only just begun to realize the power of applying generative AI to security, and we look forward to continuing to leverage this expertise for our customers and drive advancements across the security community.