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In a Google SEO office hours session, Googles Duy Nguyen of the search quality team answered a question about links on spam sites and how trust has something to do with it.

It was interesting how the Googler said they were protecting the anchor text signal. Its not something thats commonly discussed.

Building trust with Google is an important consideration for many publishers and SEOs.

Theres an idea that trust will help get a site indexed and properly ranked.

Its also known that there is no trust metric, which sometimes confuses some in the search community.

How can algorithm trust if its not measuring something?

Googlers dont really answer that question but there are patents and research paper that give an idea.

The person who submitted a question to the SEO office hours asked:

If a domain gets penalized does it affect the links that are outbound from it?

The Googler, Duy Nguyen, answered:

I assume by penalize you mean that the domain was demoted by our spam algorithms or manual actions.

In general, yes, we dont trust links from sites we know are spam.

This helps us maintain the quality of our anchor signals.

Googlers talk about trust and its clear that theyre talking about their algorithms trusting something or not trusting something.

In this case its not about not counting links that are on spam sites, in particular, this is about not counting the anchor text signal.

The SEO community talks about building trust but in this case, its really about not building spam.

Not every site is penalized or receives a manual action. Some sites arent even indexed and thats the job of Googles Spam Brain, an AI platform that analyzes webpages at different points, beginning at crawl time.

The spam brain platform functions as:

The way the Spam Brain platform works is that it trains an AI on the knowledge Google has about spam.

Google commented on how spam brain works:

By combining our deep knowledge of spam with AI, last year we were able to build our very own spam-fighting AI that is incredibly effective at catching both known and new spam trends.

We dont know what knowledge of spam Google is talking about, but there are various patents and research papers about it.

Those who want to take a deep dive on this topic may consider reading an article I wrote about the concept of link distance ranking algorithms, a method for ranking links.

I also published a comprehensive article about multiple research papers that describe link related algorithms that may describe what the Penguin algorithm is.

Although many of the patents and research papers are within the last ten or so years, there havent really been anything else published by search engines and university researchers since.

The importance of those patents and research papers is that its possible that they can make it into Googles algorithm in a different form, such as for training and AI like Spam Brain.

The patent discussed in the link distance ranking article describes how the method assigns ranking scores for pages based on the distances between the a set of trusted seed sites and the pages that they link to. The seed sites are like starting points for calculating what sites are normal and which sites are not (i.e. spam).

The intuition is that the further a site is from a seed site the likelier the site can be considered spammy. This part, about determining spamminess through link distance is discussed in research papers cited in the Penguin article I referenced earlier.

The patent, (Producing a Ranking for Pages Using Distances in a Web-link Graph), explains:

The system then assigns lengths to the links based on properties of the links and properties of the pages attached to the links.

The system next computes shortest distances from the set of seed pages to each page in the set of pages based on the lengths of the links between the pages.

Next, the system determines a ranking score for each page in the set of pages based on the computed shortest distances.

The same patent also mentions whats known as a reduced link graph.

But its not just one patent that discusses reduced link graphs. Reduced link graphs were researched outside of Google, too.

A link graph is like a map of the Internet that is created by mapping with links.

In a reduced link graph the low quality links and associated sites are removed.

Whats left is whats called a reduced link graph.

Heres a quote from the above cited Google patent:

A Reduced Link-Graph

Note that the links participating in the k shortest paths from the seeds to the pages constitute a sub-graph that includes all the links that are flow ranked from the seeds.

Although this sub-graph includes much less links than the original link-graph, the k shortest paths from the seeds to each page in this sub-graph have the same lengths as the paths in the original graph.

Furthermore, the rank flow to each page can be backtracked to the nearest k seeds through the paths in this sub-graph.

Its a kind of an obvious thing that Google doesnt trust links from penalized websites.

But sometimes one doesnt know if a site is penalized or flagged as spam by Spam Brain.

Researching to see if a site might not be trusted is a good idea before going through the effort of trying to get a link from a site.

In my opinion, third party metrics should not be used for making business decisions like this because the calculations used to produce a score are hidden.

If a site is already linking to possibly spammy sites that themselves have inbound links from possible paid links like PBNs (private blog networks), then its probably a spam site.

Featured image by Shutterstock/Krakenimages.com

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Read the original here:
Google On Protecting Anchor Text Signal From Spam Site Influence - Search Engine Journal

Open a Business Bank Account

Keep your business and personal finances separate. If your business structure is a company, trust or partnership, then you legally need a separate bank account for tax purposes. Even if you are a sole trader, setting up a separate bank account will help you manage your finances and keep track of business expenditure and profits. Many Australian banks offer dedicated business accounts, many with no monthly fee, so its worth doing your homework on which one would suit you best.

This business bank account can be used for your business transactions, such as paying suppliers or invoicing customers. Most times, a bank will require a separate business bank account in order to issue a business loan or line of credit.

If you sell a product, you need an inventory function in your accounting software to manage and track inventory. The software should have ledger and journal entries and the ability to generate financial statements.

Some software programs double as bookkeeping tools. These often include features such as check writing and managing receivables and payables. You can also use this software to track your income and expenses, generate invoices, run reports and calculate taxes.

There are many bookkeeping services available that can do all of this for you, and more. These services can be accessed online from any computer or mobile device and often include features such as invoicing. Check out the range of accounting software for small businesses in the market, or see if you want to handle the bookkeeping yourself.

Before you fund your business, you must get an idea of your startup costs. To determine these, make a list of all the physical supplies you need, estimate the cost of any professional services you will require, determine the price of any licenses or permits required to operate and calculate the cost of office space or other real estate. Add in the costs of payroll and overheads, if applicable.

Businesses can take years to turn a profit, so its better to overestimate the startup costs and have too much money than too little. Many experts recommend having enough cash on hand to cover six months of operating expenses.

When you know how much you need to get started with your business, you need to know the point at which your business makes money. This figure is your break-even point.

Break-even point = Fixed cost Contribution margin

In contrast, thecontribution margin = total sales revenue cost to make product

For example, lets say youre starting a small business that sells miniature birdhouses for fairy gardens. You have determined that it will cost you $500 in startup costs. Your variable costs are $0.40 per birdhouse produced, and you sell them for $1.50 each.

Lets write these out so its easy to follow:

This means that you need to sell at least 456 units just to cover your costs. If you can sell more than 456 units in your first month, you will make a profit.

Read more here:
How To Start A Business In 11 Steps (2023 Guide) - Forbes

The Korean Invention Promotion Association delegation met with a cross-disciplinary team of Ocean Tomo experts from Valuation & Intellectual Property Strategy, Transaction Advisory & Investment Banking, and from Intellectual Property Disputes, Financial Expert Testimony.

CHICAGO (PRWEB) May 09, 2023

Ocean Tomo, a part of J.S. Held, signs a Memorandum of Understanding(MOU) with the Korea Invention Promotion Association of the Republic of Korea.

At the signing ceremony, Ocean Tomo Managing Director Gregory Campanella, who oversees Intellectual Property (IP) Strategy and Valuation services, represented Ocean Tomo. Singing on behalf of the Korea Invention Promotion Association was Yongook Son, President.

The agreement between Ocean Tomo and the Korean Invention Promotion Association acknowledges both organizations shared goals to: promote exchange in technical areas of mutual interests, including intellectual property management, commercialization consulting, and tech transfer; mutually support inventors by promoting the utilization of intellectual property, supported by valuation, at both regional and international levels; present joint human resources training and exchange programs, among others.

The delegation from the Korean Invention Promotion Association met with a cross-disciplinary team of experts from Ocean Tomo, including representatives from Valuation and Intellectual Property Strategy consulting, including Managing Director Greg Campanella and Director Noor Al Banna and Manager Dan Principe. Representing Ocean Tomo Advisory Services, which includes transaction advisory and investment banking, Trevor Krajewski and from the Intellectual Property Disputes, Financial Expert Testimony group, Managing Directors, Robert McSorley, and Alex Clemons. Greg Campanella highlighted several intellectual property mega-trends impacting the global market for intellectual property and further background on how as part of J.S. Held, Ocean Tomo is uniquely suited to assist corporations, insurers, law firms, governments and institutional inventors related to complex technical, scientific, and financial matters across all assets and value at risk.

Representatives of the Korea Invention Promotion Association include Yongook Son, President; Su Jung Yoon, Ph.D., Expert Advisor, IP Valuation; Seo Eunkyung, Ph.D., Consultant, IP Transactions, and Ha Young Yoon, Manager, International Cooperation.

About Ocean Tomo, a part of J.S. Held

Established in 2003, Ocean Tomo provides Financial Expert, Management Consulting, and Advisory Services related to intellectual property (IP) and other intangible assets; corporate accounting investigations; regulatory and reporting obligations; solvency and restructuring; and contractual or competition disputes. Practice offerings address economic damage calculations and testimony; accounting investigations and financial forensics; technology and intangible asset valuation; strategy and risk management consulting; mergers and acquisitions; debt and equity private placement; and IP brokerage. Subsidiaries of Ocean Tomo include Ocean Tomo Investments Group, LLC, a registered broker-dealer.

As a part of J.S. Held, Ocean Tomo works alongside more than 1500 professionals globally and assists clients corporations, insurers, law firms, governments, and institutional investors on complex technical, scientific, and financial matters across all assets and value at risk.

J.S. Held is a global consulting firm providing technical, scientific, and financial expertise across all assets and value at risk. Our professionals serve as trusted advisors to organizations facing high-stakes events demanding urgent attention, staunch integrity, clear-cut analysis, and an understanding of both tangible and intangible assets. The firm provides a comprehensive suite of services, products, and data that enable clients to navigate complex, contentious, and often catastrophic situations.

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Ocean Tomo, a part of J.S. Held Welcomes Delegation from Korea ... - PR Web

Lionel Messi has returned to Paris Saint-Germain after his suspension was lifted following an unsanctioned trip to Saudi Arabia last week.

The Argentine has apologised to the club and his team-mates are he took a two-day trip to the Middle East last week which wasnt authorised.

PSG suspended the 35-year-old for two weeks and was also fined. However, he came back into the fold on Monday, and was photographed training.

Lionel Messi has returned to PSG regular training session today morning at Camp des Loges after saying sorry with the public statement. #PSG

Situation looks more calm after tension aroind Saudi trip but its still unclear if Messi will be available for game vs Ajaccio. pic.twitter.com/ptcq8Oahdj

Fabrizio Romano (@FabrizioRomano) May 8, 2023

After missing Sundays 3-1 win over Troyes in Ligue 1, Messi could come back into the squad for Saturdays game against Ajaccio.

Messi and his family travelled to Saudi Arabia last Monday as part of the World Cup winners 25million-a-year role as a tourism ambassador for the country.

The former Barcelona star released a statement via Instagram on Friday.

Hello, well, I wanted to make this video after everything that is happening,

First of all, I apologise to my teammates, to the club. Honestly, I thought we were going to have a day off after the game, as had been happening in previous weeks.

I had this trip to Arabia organised and I couldnt cancel it. It had already cancelled it before.

I repeat again and apologise for what I did. Here I am, waiting for what the club decides.

Messis future at the club is uncertain beyond this season and has been heavily linked to the Saudi Pro League in which he could face old rival Cristiano Ronaldo for one final battle.

The rest is here:
Lionel Messi Returns To PSG Training After Suspension Lifted - Sports Lens

Integrating a third-party LLM-powered virtual assistant with a plug-in or other API is the quickest and easiest option to reach new customers in a generative AI world. The use of platforms to offer services is a proven way for companies to easily engage with a large and established customer baseone that appreciates having a wide variety of services accessible from a single location. Although conversational AI (such as chatbots) still have significant ground to make up compared to established platforms like WeChat and Amazon, the novelty of the experience is driving customer engagement. And that engagement is accelerating at record pace the three powerful flywheels that drive platform successscale, learning, and network. The success of the platform is also likely to drive the success of companies on the platform. (See Exhibit 2.)

Scale Effect. The cost of large, generalized models (which are the most likely models to be used for virtual assistants, because of their broad functionality and superior conversational ability) is notoriously high. (See Building a Best-in-Class Large Language Model.) But we expect that LLM providers will be able to distribute their substantial R&D and running costs over what will be a large userbase, giving them valuable economies of scale. As a result, companies that want to engage with customers with virtual assistants can do so without building the models themselves.

The total cost to build LLMs depends on the size, complexity, and capability required. Training a large, general-purpose LLM (like GPT-4), can range from $30 million to $100 million and up. Building an industry specific LLM (like BloombergGPT) can cost between $10 million to $50 million and up depending on the level of complexity.

Building a small, single-task model is often more cost effective, ranging from $100,000 to $5 million and up depending on the complexity of prepping the data and the functional requirements of the desired task. For example, a well-known regional bank trained a small, task-specific language model for internal loan adjudication purposes and spent between $150,000 to $200,000 and up end-to-end for their foundation model implementation.

In contrast to building a model, the cost to modify (for example, fine-tune) an existing model is the most affordable option, ranging from $10,000 to $100,000 and up.

The key ingredient to train or fine-tune these models is access to high-quality proprietary data. The data also needs to be cleaned, sometimes labeled (for particular use cases), and ideally anonymized for use in fine-tuning or training an LLM. This is no small ask: BloombergGPT was trained on a massive 363 billion token dataset using Bloombergs extensive, pre-existing financial dataset (which includes proprietary Bloomberg data), the FinPile dataset (a compilation of financial documents from the Bloomberg archives), and external sources such as press reports.

Learning Effect. The excitement surrounding generative AI is encouraging users to experiment with applications such as ChatGPT and Bard. Both chatbots have benefited from the learning effect (also known as the direct network effect) generated by this surge in experimentation: They improve as more people use them. For companies that decide to offer services through an established platform, this learning effect provides a significant advantagetheyll have access to superior user experience and best-in-class conversational interfaces.

Learn More About Generative AI

Learn More About Generative AI

Generative artificial intelligence is a form of AI that uses deep learning and GANs for content creation. Learn how it can disrupt or benefit businesses.

This powerful technology has the potential to disrupt nearly every industry, promising both competitive advantage and creative destruction. Heres how to strategize for that future.

Same-Side and Cross-Side Network Effect. As more companies join LLM platforms, consumers will find greater value and new users will gravitate to the platform (the same-side network effect), which in turn drives more companies to integrate their services with the platform (the cross-side network effect). These network effects present a significant opportunity for companies to engage with a wide user base and attract high volumes of customers.

Many companies today are concerned about the operational risks of using an LLMs interface. For example, providing services through an LLM-powered virtual assistant could potentially expose a companys proprietary data to the LLM vendor. However, many of these risks can be mitigated with technology implementations and vendor contracting.

But companies also face strategic risks that may not currently be on their radar. One key risk, commoditization from intermediation, emerges when an intermediary between a company and its customers reduces emphasis on the companys unique selling points. Much like search engines, virtual assistants will have to prioritize which services are displayed to the customer and can take commissions on sales. The result is often lower margins and standardization of servicesmaking brand recognition and promotion of premium offerings more difficult. This risk grows as more companies join the platform. The question of how an LLM-powered virtual assistant will select (or help the customer select) one companys service or product out of a list of common services and products is unknown, putting companies at higher risk for commoditization.

There is also an inherent risk in relying too significantly on a third-party sales channel. This risk is illustrated by the vacation planning example above. When a customer books through a third-party virtual assistant rather than with the airline or hotel chains that provide the actual service, the virtual assistant provider has control over the engagement logs and how services are selected, and heavily influences customer buying behavior. As a result, companies could lose direct connections with customers, and the critical engagement data that enables them to build brand loyalty and cultivate ongoing customer relationships.

Companies that have access to valuable, domain-specific, proprietary data may choose to double down on their competitive advantagecreating their own LLM-driven customer experiences with generative AI. The tradeoff is typically in the homegrown user experience, compared to LLM-powered virtual assistants where providers are pouring resources into optimizing human engagement. Specialized models designed in-house need to be user-friendly enough to support their customer offerings and encourage customers to return.

The good news is that many small models, such as Alpaca (a 7-billion-parameter language model created at Stanford University) and Dolly (a 12-billion-parameter language model created by Databricks), are not as cumbersome and costly to customize as those required for the more expansive virtual assistants. And creating specialized models, for example, those built through fine-tuning or retraining, with proprietary data can provide superior performance for a specialized task. The better the data is, the better the model is at performing the task that the data is related tothough possibly at a cost of its language capabilities.

It is also possible to add functionality and value to raw data by adding a layer of analysis. BloombergGPT (a 50-billion-parameter language model), for example, outperformed general purpose models for highly specific financial tasks, such as financial risk assessment.

Companies that choose to create their own customized experiences can maintain exclusive access to their valuable, proprietary data and ensure it remains secure. In-house control allows companies greater flexibility to create unique functionalities and user experiences without depending on another companys technical roadmap. In the case of BloombergGPT, the user gets more refined and accurate financial data, and in return, Bloomberg gets more tailored user-interaction data that can be used to continuously update their LLMs.

When companies keep direct access to their customer base, they can benefit from the rich data gleaned from customer engagement. This allows companies to better understand their customers and cultivate stronger, mutually beneficial relationships. It also strengthens companies ability to build customer trust by providing a sense of security and confidentiality, while promoting their brand name. For more sensitive interactions, such as viewing a bank statement, this is particularly valuable; consumers typically prefer to use a service offered directly from the bank itself.

The obvious operational risk surrounding this option will be the simple fact that investing in in-house capabilities can be cost prohibitive. But companies dont need to take the most expensive approach and build from scratch: they can fine-tune free, open-source models or bring in someone elses model and incorporate it into their own website.

Leaders also need to consider the less-obvious strategic risks. For one, theyll need to keep up with the requirements to build and maintain best-in-class capabilities in-house. (See Building a Best-in-Class Large Language Model.) Specialized models need to have good enough functionality and usability to attract and retain customers. But the definition of good enough for customers will evolve alongside the experiences of best-in-class models and platforms. And the data science and engineering talent needed to manage these models is currently a scarce resource.

In addition, the R&D necessary to maintain a best-in-class model likely wont be feasible for most companies, as LLM research becomes more proprietary. Making that task more difficult is the fact that some best-in-class model providers dont allow companies to customize the model for their own purposes.

Companies that choose this option also risk missing out on a critical customer engagement channel. If companies dont put any of their services on a popular LLM-powered virtual assistant, they could become alienated from their customer basemany of whom may have grown accustomed to using that assistant instead of coming to the companies website.

The generative AI world is one of constant motion, making it challenging to track how the market dynamics are evolving. It may be tempting to integrate an LLMs plug-in today, no questions asked. And for some companiesfor instance, those with a small market share, a small customer base, or low-quality data or lack of access to strong proprietary data, and that dont have a strong user experiencethis will be a smart strategic move.

But with every benefit comes a risk. And companies with a strong customer base and unique offering may be better served by maintaining control of their user experience and providing a virtual assistant service in-house.

Read this article:
Engaging Consumers in a Generative AI World - BCG