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Jack Vaughan

OpenAI GPT-4o Lands with Mini Thud or: Generative AI balances Hype and Reality in Chatbot Market Quest

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It’s still too early to gauge Generative AI’s limits. That is another way of saying a circus atmosphere of hyperbole and demo theatrics is far from played out. The word “plateau” is heard today, and maybe a leveling off is only natural.

But now the uncertain space of ‘what it can’t do yet’ is mined each day. If Generative AI efforts plateau, and it merely changes the chatbot market as we know it, Generative AI will go down as a really big large-language disappointment.

This week’s OpenAI rollout of GPT-4o didn’t help. One can’t blame the OpenAI crew for trying their best to present awe inspiring on-stage demos as they saddle onto Danish Modern furniture set that bodes a comforting future. After all, there’s need to show their labs’ work is world changing or — barring that — fun.

The upstart’s was one among other episodes in the week’s AI Wars, as OpenAI’s demo was joined by Google I/O’s product roll outs on another stage in another corner of the Web, reported by Sean Michael Kerner and others.

For their part, the OpenAI crew walked through pet tricks, such as, asking the applications to translate “Why do we do linear equations?” into sparkling Italian.

Google’s show was just as breathless.

Yes, for OpenAI, the free app is a step into a new realm. (Although, as George Lawton points out, “free” is always an onion to unpeel.) And, yes, it vastly surpasses a voice-to-text demo of the 1990s. Does it move the bar much further than the Smart Speaker did in the mid-2010s? Let an army of pundits ponder this.

Our take: OpenAI’s announcement of something akin to a free-tier product was a bit short on awe. We’d second Sharon Goldman of Fortune who marked GPT-4o as “OpenAI’s emotive on steroids voice assistant.”

Of course, more accessible and easier entry for a wider range of people is OpenAI’s ticket to broadening into consumer markets. That’s where the killer app that justifies big valuations may be. Gain the consumer, and the enterprise follows.

That’s where OpenAI will meet the public and duke it out with friends like AWS, Google and Microsoft. There’s Apple too, which is likely now prepping spirited demos that show it has heard the bubbling cries of drowning users of Siri.

The next battle will be different than what has come before for heavily financed OpenAI. This stage in the technology’s evolution brings the OpenAI boffins down from the high ground, They say “hello whitebread-light demo patter” — just like Google, AWS or Microsoft product managers!

For a company that’s gained outsized attention in big headline deals for crucial infrastructure for big cloud players, it’s time to move toward apps. If it is to gain ground on a big scale, it will have to reach consumers. We take that as a less than nuanced theme in the GPT-4o roll out.

Cousin IoT: Brave New World Update

As if by chance, we sat in on Transforma’s report on IoT markets this very same week. While ably detailing the currents and eddies of IoT in the decades to come It seemed to convey a message relevant to Gen AI’s future course.

It’s been a long time since IoT first promised a brave new technology future — and such promises were never quite on the scale of Generative AI — but IoT has been grinding away gainfully, nevertheless.

IoT industry players have faced the same kind of existential challenge that GenAI is about to encounter. That is: The need to find a killer consumer app that it can power.

Transforma’s recent survey reports that there were 16.1 billion active IoT devices at the end of 2033. Annual device sales will grow from 4.1 billion in 2023 to 8.7 billion (a CAGR of 8%).

Yet, the world — even the industrial market within the bigger world — seems little changed. IoT’s top use cases, now and looking forward, fall short in terms of the energetic dynamism represented in early visions of IoT that looked more like StarTrek or the Jetsons.

Transforma looking toward the top three IoT use cases in 2033 cited 1- electronic labels; 2-building lights; and 3-headphones. You can come knocking because the van is not rocking, at least in terms of excitement. Still, these use cases represent real businesses.

Now, the assertion here — that Generative AI will be viewed in the future much as IoT is viewed today — is tentative. The agreement here is likely inexact … but may be useful for predicasts. Finally, my purpose here is not to put-down these young technologists’ efforts, but just to suggest that OpenAI and underlying Generative AI are in for a tough fight. — Jack Vaughan

Observer Monitor Dispatch: Dynatrace, StarTree, More

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Kubernetes defense in focus as Dynatrace rolls with Runecast tech

Kubernetes’ quick rise to prominence in cloud computing may have left a few holes in applications’ defenses. That is something Dynatrace looks to address with Kubernetes Security Posture Management (KSPM) software. It’s said to employ observability data to enable quick response and mitigation of risks.

Dynatrace has a lineage when it comes to AI, originally arising out of the movement that placed AI agents on network nodes in order to track activity. KSPM employs  Dynatrace’s Davis hypermodal AI which combines predictive AI, Causal AI and Generative AI methods. The company said KSPM, thus accoutered, can ably convey immediate context for decision making as threats occur.

The company said  KSPM follows the integration of Runecast cloud native technology into the Dynatrace platform following the company’s successful acquisition earlier this year. Runecast technology supports continuous Kubernetes vulnerability scans, security compliance based on best practices, and remediation recommendations.

AI continues to find  renewed influence in the observability space. Updates are coming quickly, as this follow latest follows up on Dynatrace’s January roll-out of AI Observability extensions for large language models (LLMs).

 

StarTree Cloud extends observability

StarTree Cloud gains new observability and anomaly detection capabilities as well as vector search capabilities for its underlying Apache Pinot database engine with a new release reported by StarTree.

StarTree offers these services to customers of a cloud-based database-as-a-service that is specially targeting  analytics jobs. Like Confluent, StarTree arose out of the open-source activity of LinkedIn during the 2010s.

While Confluent has focused most effectively on data ingestion, StarTree has concentrated on data analytics, based on an implementation of the Pinot OLAP distributed columnar database.

The StarTree product suite is said to serve user-facing applications where a broad user base can query real-time data. The company noted DoorDash as a customer in this regard. The company said it partners with cloud and big data players such as AWS, Google Cloud, Microsoft, Confluent, Databricks and others in customer engagements.

The observability functionality new to the platform should allow  StarTree users, serving as developers or system administrators, to troubleshoot issues that arise within their cloud-based applications.

StarTree announced the general availability of StarTree ThirdEye software, offering multidimensional anomaly detection. As well, a write API supporting real-time sync for ELT pipelines such as Debezium, Fivetran, or dbt , is now in “private preview,” and integrations with visualization platforms, including Tableau and Grafana are available now.

Like others, StarTree is bringing nearest-neighbor vector search capabilities to its users, as well as users of the open-source Apache Pinot project.

Besides DoorDash, StarTree cites customers such as  Citi, Stripe, Nubank, and Zomato. For its part, in March, Citi announced a strategic investment in StarTree.

At the time,  Katya Chupryna, Director, Markets Strategic Investments at Citi marked StarTree and the underlying Pinot engine for speed of data ingestion.

“User-facing analytics have seen profound growth in recent years across all industries, accelerating the need for enterprise-ready, real-time data solutions. StarTree and Pinot’s speed of ingestion is unmatched on complex queries over rapidly changing data,” Chupruna said.

Also on tap

Cisco announced a virtual appliance for its AppDynamics On-Premises application observability offering. It’s aimed at users looking for customer-managed observability for on-premises deployments or cloud-based deployments where the customer retains control of all data and associated operations. AI-Powered Detection and Remediation with Cisco’s Cognition Engine is said to speed anomaly detection and root cause analysis … Riverbed announced Riverbed Unified Agent which allows IT to add SaaS-delivered visibility modules – for example, for end user experience and network monitoring – without adding more agents. Riverbed’s Platform initially launches with approximately 35 pre-built application integrations for third-parties including ServiceNow, Dynatrace, AppDynamics and DataDog. A Topology Viewer generates dynamic mapping of connected devices, while Riverbed NPM+, using the Riverbed Unified Agent, is said to overcome network blind spots created by remote work, public cloud, and encrypted architectures such as Zero Trust environments. This, while extending packet visibility to collected decrypted data at every user and server endpoint, including gaps such as encrypted tunnels in Zero Trust architectures. [PG]

Net neutrality is on the books again

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[Updatte: In August 2024, the Sixth Circuit Court of Appeals issued a temporary stay on the implementation of these rules.]

[April 26, 2024] – The stars and planets may seem in their usual places this morning, but change came overnight, as Net Neutrality returned to America. If you blink you missed it, and it’s not guaranteed to stick.

Net Neutrality is again on the books, as the Federal Communications Commission on Thursday voted to restore rules requiring telecommunications companies to treat all apps and sites equally.

“It is incredibly important that there be ongoing oversight for the most important network of the 21st century. It’s that simple,” former FCC chief Tom  Wheeler said on  the Politico Tech podcast on the eve of that vote.

Voted in when Wheeler led the FCC during the Obama era, and then out during the Trump Administration, net neutrality is a technology hot potato in this partisan political climate. Like other Trump era edicts US President Joe Biden has reversed, this one will be challenged via litigation and legislation going forward.

But a return to the Internet as a regulated utility is important. That means no throttling, no superfast lanes, no favors for golf buddies. As the Three Stooges would say: “None of this and none of that.”

That look at blocking is a look back, and not the point today, Wheeler says.

“To define it in terms of no blocking and no throttling. I mean, that is so much yesterday’s issue. The broader question here is, will there be an ongoing expectation for all of the activities of this really important 21st century network? And will there be flexibility on the part of the FCC to deal with those?”

Wheeler’s caution comes as regulation of any kind is widely derided, even as Artificial Intelligence doomsaying predicts an internet more volatile than the often rocky network it is today.

Wheeler, a former cable industry executive who now teaches at Harvard’s Kennedy School of Government outlines the issues in his  “Techlash – Who makes the rules in the digital Gilded Age?” – a recent book that looks for guides in the history of communications.

The premise of the book is that it’s valuable to look to the 19th century Gilded Age as a clue to what’s going on now in communications.

In the 19th century there were great waves of industrialization as steam, trains and electricity gained traction, workers were exploited, farms gave way to cities, and Robber Barrons leveraged all.

Today, we all agree, there’s been great waves of technology changing the way we live, and how that is communicated. What we came to call mass communications – evolved out of the telegraph, the telephone, radio and television. These all took time to settle….

This is how Wheeler poses it in Techlash:

“While the economic model is still about maximizing revenue, it is no longer about the need for balance and veracity. Like the early ideological media, the new media profits by playing to users’ preferences and prejudices. The difference is that software algorithms organize the information to deliver what each user likes in order to hold the user’s attention to see as many revenue generating ads as possible.”

Technology companies make money “through the most sophisticated and secret content curation ever devised,” Wheeler writes. Let’s take this to include Big Data, algorithms, collaborative filtering, portals, recommendation engines, personalization engines and a parade of machine learning models.

With a next age of AI bubbling in a slew of Large Language Models, that secret curation model has already remade media and society. Having an FCC that works for a neutral network is important as that next age looms. – J Vaughan

Observer Monitor: SolarWinds views observability on multicloud

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Today’s observability movement pits newbies fresh out of a few consulting gigs against established performance players with the “burden” of long-time customers. Established performance players have revenue, but they surf choppy waters.

The long-time performance players come to the fray with strong roots in tooling for applications, web monitoring and so on.

They continue to update their offerings in pursuit of observability. The observability software space is heavy on the metrics that can derive from log monitoring and, increasingly, full-out to include cloud native development, shift-left security, and multicloud support. This keeps vendors busy.

SolarWinds is among the performance incumbents that have embraced logs and gone cloud native. The company is building toward observability from foundations in mostly mid-market network performance and management products and services.

The company boosted its log analytics efforts markedly in 2018, with its acquisition of Loggly, which it has continued to field as an independent entity, even as it has integrated Loggly capabilities into its SolarWinds Observability platform.

Bridging the cloud gap

We spoke recently with Jeff Stewart, vice president for product management at SolarWinds. Under discussion were recent updates to SolarWinds Observability, originally released in 2023 to cloud-native and on-premises observability offerings.

The moves build on the 2022 release of SolarWinds Observability, which takes on application, infrastructure, database, network, log, and user experience observability, in the form of a SaaS platform.

Recent updates include query-oriented database monitoring enhancements, as well as improvements to visual explainer plan software.

The company is building out to the cloud at the same time that some users are reacting to rising cloud costs by more carefully picking what will be cloud bound. They will also judge what will be most closely monitored.

Said Stewart:

Customers are in different camps on their journey to the cloud, and migration of cloud workloads from on-premises to clouds like  Azure or AWS. We’ve seen customers that have gone full steam to the cloud, only to figure out that maybe it wasn’t the best idea to move all of their workloads, and that they should have been more selective based on security needs, budget needs or even performance needs, depending on where the application sits. And then there are people that have been very successful with their migration to the cloud. For existing customers that need visibility into different clouds, whether Azure or AWS, we’ve added capabilities in our hybrid cloud observability offering to support them on that journey. But we’ve also enabled them, as they make a decision to go more into the cloud to instantaneously start to send their data up to our SaaS offering.

What has appeared is a visibility gap on networks and security as users enter the realm of multicloud, according to Stewart, who touts Solar Wind’s lineage in network monitoring here. He said:

When applications or workloads are deployed across multicloud, we see some configurations where a part of the application is talking to another part of the application in a different cloud, which becomes very cost prohibitive. So, providing visibility into how traffic is traversing multicloud environments, as well as traffic that’s going from on-premises to the cloud, is a visibility gap that we see and are working to address with our offerings.

Database visibility

Clearly, visibility of database performance is no longer an isolated, on-premises event. And the database query in the multicloud space can introduce new complexity. The runaway query, which was a feature of early client-server’s darkside, is taking on a new tenor as hybrid and multicloud use grows wider.

Stewart said Solar Winds’ background in database performance positions it to deal with the new distributed computing paradigm. That’s where a variety of databases are in place, with locations that can span the globe.

Now, even high-level executives can view this activity, as observability metrics are rolled up. In e-commerce on-line selling, where efficient customer facing applications directly translate into revenue, they find query issues particularly telling.

My Take

Despite clear interest in observability tooling, the complex demands for monitoring modern systems challenge vendors and users alike.

Deeper and wider hybrid cloud environments can cause costs to rapidly escalate, requiring that IT users carefully pick and choose what they monitor.

Like others in the market, SolarWinds faces the challenge of keeping best-of-breed tools fresh for compartmentalized networking and database users, while building out an ever-broader platform of capabilities intended to run across broad multiclouds. – Jack Vaughan

 

Multicloud: Evolving technology paradigms create rising complexity

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Late last year I had the opportunity to cover Multicloud issues for Muse at SDxCentral, and it was a bit of an eye opener in this regard. We were writing about cloud over 30 years ago, and utility and grid computing before that. But a visit to cloud computing today finds obstacles yet to confront.

The Multicloud milieu today seemed to say there was still very little in the way of deep integration between clouds, all these years later. It seems another case in the evolution of computing  where every new solution seems to hold the seeds of its own obsolescence.

Read Evolving technology paradigms create rising complexity on Medium