Category: Guides

  • 10 Ways you can use Augmented & Blended AI to Sell on LinkedIn – Now!

    10 Ways you can use Augmented & Blended AI to Sell on LinkedIn – Now!

    There are so many ways you can use AI in sales these days, it’s hard to know where to start, especially if you’re a newbie to all of the excitement. So, this article will focus on something almost everyone in the business world uses, knows, and understands how It can be used right now, on every business persons favorite, LinkedIn.

    Using AI with LinkedIn selling can enhance your sales and marketing efforts by automating tasks, providing valuable insights, and streamlining processes. As noted in previous articles, to maximize AI’s usefulness it requires a combination of Blended AI* and Augmented AI** to maximize its impact.

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    10 ways to use Augmented & Blended AI with LinkedIn

    1.    Personalized messaging: AI-powered tools can analyze data from LinkedIn profiles and interactions to create personalized messages that resonate with individual prospects, increasing engagement and response rates.

    2.    Relationship insights: AI can analyze communication patterns and interactions with prospects to provide valuable relationship insights, such as the best times to reach out or the most effective ways to engage with specific individuals.

    3.    Lead generation and prospecting: AI algorithms can help identify and prioritize potential leads based on specific criteria identified in your sales process, such as job titles, industries, or geographic locations, making your prospecting efforts more efficient.

    4.    Sales forecasting: AI-driven analytics can analyze historical data, sales patterns, and market trends to provide more accurate sales forecasts, helping you plan and strategize your selling efforts better.

    5.    Competitor analysis: AI can gather data on competitors’ activities and strategies on LinkedIn, providing valuable insights to refine your own selling approach and stand out in the market.

    6.    Performance tracking and analytics: AI-driven analytics can measure the effectiveness of your LinkedIn selling efforts, tracking key performance indicators (KPIs) like response rates, conversion rates, and engagement metrics. This data allows you to continuously improve your strategies.

    7.    Content recommendations: AI can analyze user behavior and preferences to suggest relevant content, such as articles, blog posts, or videos, that align with your prospects’ interests and pain points.

    8.    Social listening and sentiment analysis: AI tools can monitor conversations and discussions on LinkedIn to gauge the sentiment around your brand and industry. This information can help you tailor your messaging and identify potential opportunities or challenges.

    9.    Automated follow-ups: AI-powered sales automation tools can handle routine follow-up tasks, sending timely messages and reminders to prospects, helping to maintain engagement and nurture leads.

    10.Chatbots for instant responses: Implement AI-driven chatbots on your LinkedIn profile to provide instant responses to common questions, qualify leads, and engage with prospects at any time, improving customer experience and response times.

    There is no single AI tool (yet) that can accomplish all ten use cases noted on my list and it isn’t necessarily in order of usefulness. Instead, each seller must ascertain what is most important in their sales efforts and then use Blended AI to attack their sales patch in a coordinated and concise manner. Best practices are to pick a vendor that know what is actually working to advise when to use it and when not to, because:

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    The key isn’t using AI.

    The key is using the right AI with your already successful, winning Sales Process and applying the right form of AI at the right stage in the sales process. That is the Winning Ticket!!!

    Bottom line

    By leveraging Augmented and Blended AI in your LinkedIn selling efforts, you can streamline working sales processes, deliver a significantly more personalized experience for your target audience, and make data-driven decisions (not just gut feelings), ultimately increasing each seller’s success rate by connecting with the best prospects and converting them into valuable customers.

    *Blended Artificial Intelligence Is the amalgamation of different artificial intelligence techniques or approaches to achieve more robust and effective solutions. It involves integrating multiple AI models, algorithms, and / or technologies to leverage their respective strengths and compensate for their weaknesses. See Wikipedia:

    ** Augmented artificial intelligence is a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making and new experiences.

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  • 5 Reasons VCs Want You to Use the best CRM / AI Sales Technology for Startup Success

    5 Reasons VCs Want You to Use the best CRM / AI Sales Technology for Startup Success

    Venture Capitalists weren’t born in the Matrix but sometimes it can feel that way if you’re a startup looking for capital.

    They will invest in startups that have the right idea and key people to solve a big hairy problem with a large amount of potential revenue or users, needed for a successful VC exit. Whether its in the Metaverse or Matrix all VC’s want one thing.

    Startups must scale very quickly to be successful.

    Developing a scalable sales tech stack takes time and requires a very flexible and rapidly scalable CRM platform to manage all the sales tech data pipes. Startups must focus on keeping things as simple as possible for end users but still give them the right actions to do at the right time. This can be expensive for startups since they are often forced to pivot as they learn what is working or not. By building flexible and scalable tech systems from the beginning their odds of succeeding improve dramatically.

    Entrepreneurs must consider what a VC or Investor maybe looking at when evaluating their company for a potential investment – at least from “What are they Going to Sell” perspective.

    With that in mind. This is what VCs are looking for when evaluating your startup sales tech stack:

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    5 Reasons Why VCs Want You to use CRM / Sales Technology

    1. Improved Revenue: A high-quality CRM properly implemented can help a Startup manage its sales pipeline more efficiently, resulting in improved sales and revenue. This helps boost the startup’s valuation and obviously more attractive to investors.
    2. Superior Customer Engagement: A CRM can help a startup improve its customer experience by providing insights into customers behavior, preference and needs. This can help the startup develop better products and services, resulting in increased customer loyalty and higher revenue.
    3. Operational Efficiency: A high-quality CRM can help a startup streamline its sales and marketing processes, resulting in improved operational efficiency. This can lead to cost saving and increases profitability, which can make the startup more attractive to investors.
    4. Data Insights: A CRM can provide valuable data insights that can help a startup make more informed business decisions. This can help the startup develop more effective strategies, leading to improved revenue and profitability.
    5. Scalability: A top CRM can help a startup scale its sales and marketing efforts more effectively and quickly. This can help the startup expand its customer base and grow its revenue, which can make it more attractive to investors.

    Investors want startups to succeed or fail fast!

    Their investment window is dependent on some of their investments growing quickly and that is where they need to focus, while others will fail and need to get off the books/harvest patents.

    Many VCs prefer that you use Salesforce as it is the number one selling CRM.

    They know they can get people to configure and implement the system, most sales people are familiar with it and it’s enterprise grade so scaling isn’t a big concern. It ticks a big funding box.

    Next – whether a VC prefers a startup with Salesforce CRM or no CRM would depend on several factors. In general, if a startup already has a Salesforce CRM, it often indicates that they already have a well defined sales process and are using data to inform their decision-making. This makes their evaluation easier as they can see actual numbers vs. a hockey stick graph.

    However, the absence of a CRM doesn’t always mean a startup is less attractive. VC’s maybe willing to invest in early stages of development – especially if a clear value proposition is backed by a clear sales process tracked to achieve rapid growth. Often VC’s have favorite CRM implementation partners that can quickly come in and set things up because they have a repeatable process in place.

    One other way VC’s use CRM’s is to look at the numbers every day. Leading CRM AI packages (Such as Sentia) show VC’s every one of their investments in a simple dashboard showing daily sales, outbound activities and tracking to quota. They don’t have to call the CEO, they see it in real time. Most VCs like this because it reduces surprises and uncertainty.

    Ultimately, whether a startup has a Salesforce CRM or no CRM it’s only one factor that VCs consider when evaluating potential investment opportunities. The most important factors are the startups overall business plan, the problem they plan to solve, quality of chosen team, potential for growth and profitability, and often most importantly how does your startup fit in the VC investment strategy and ten-year plan.

  • Top Tips to Select the Best AI Enabled Salesforce Partner

    Top Tips to Select the Best AI Enabled Salesforce Partner

    Unless you have been like the proverbial Ostrich with its head under the sand AI is here and making its presence felt quickly! Within a couple of years it will no longer be which company is using AI, rather it will be which company isn’t?

     

    Since sales has the most scrutiny and oversight within the majority of companies (see CRO’s Get Fired First) it is often the first place AI is applied, for many good reasons. Sales rep turnover is usually high, there is lots of training (see Blended AI for Sales Rep Training) that needs to take place and companies spend a small fortune on hiring, training and getting reps “field ready). Also reps want new tech that works (if it doesn’t they will refuse to use it) and are used to being the first to use it.

    AI is that “new” tech and many companies have already put AI implementation strategies into place (See CEO’s Must have an AI Strategy Plan) and those that don’t will, or they can fall behind competitors. The biggest challenge is AI is so new that top quality talent is hard to source, evaluate and hold onto. Many organizations (especially those using the number one CRM Salesforce) want to use AI strategy consultants that are also Salesforce partners. They know that these certified partners are the key to plan, strategize, and successfully implement AI.

    The biggest challenge is finding the right one. There aren’t many case studies or client testimonials out there (yet) since it is so new. Falling back on past partner evaluation techniques are a starting point. However, the most advanced AI Salesforce partners are often smaller and harder to find (See Sentia AI).

    With that in mind here are 7 high level tips for Selecting the right Salesforce partner with AI capabilities. Organizations looking to leverage artificial intelligence within their Salesforce ecosystem often have to look far and wide to find them. The following tips will act as a guide to help you in making an informed decision:

     

      1. Check AI Certifications and Expertise: Ensure that the Salesforce partner possesses relevant certifications and a track record in AI. Look for certifications or experience with Salesforce Einstein Analytics and Discovery Consultant, which demonstrate expertise in leveraging AI within the Salesforce platform. If you’re not going to use Einstein / Discover – it isn’t as important.

      1. Evaluate AI Use Cases and Experience: Assess the partner’s experience in developing as well as implementing AI solutions, especially within the Salesforce environment. Look for case studies or examples of successful AI integrations that align with your business needs. A partner with diverse AI use cases demonstrates versatility. Don’t pass on an AI Salesforce certified partner if they have limited or even no case studies – remember AI is in its infancy – be on the people at the company first and foremost, not slick marketing materials.

      1. Assess Industry-Specific Knowledge: Industry expertise is crucial for understanding the unique challenges and opportunities within your sector. Choose a partner with a deep understanding of your industry, as this knowledge will be beneficial when tailoring AI solutions to your specific requirements.

      1. Check Integration Capabilities: AI solutions often require seamless integration with existing systems. Ensure that the Salesforce partner has experience integrating AI capabilities with Salesforce and other relevant tools in your technology stack. This capability is crucial for a cohesive and efficient implementation.

      1. Verify Data Security and Compliance: AI involves handling sensitive data, so it’s vital that your Salesforce partner prioritizes data security and complies with relevant regulations. Request information on their data protection measures and ensure they align with your organization’s compliance requirements.

      1. Evaluate Training and Change Management Support: Assess the partner’s approach to training and change management. AI implementations often necessitate a shift in processes and workflows, so your partner should provide comprehensive training programs and support for change management to ensure successful adoption.

      1. Consider Scalability and Future-Readiness: Select a partner with a focus on scalability and future-readiness. The AI solutions implemented should be scalable to accommodate your organization’s growth, and the partner should stay abreast of advancements in AI technology and Salesforce updates to ensure ongoing relevance.

    Bonus Tips: Look for all in one help. Salesforce partners that can evaluate your business needs, sales process, configure Salesforce, then train your people are far more valuable than having to go out and find 3 separate partners to do each phase. It will cost a lot more in both time and money to do this and perhaps most importantly something is almost always lost with every handoff along the way. Also make sure the partner is going to be with you long term – AI is so new it is guaranteed to require tweaks, adjustments and outright replacement along the way. A partner with a history of run and gun is the last one you need right now. Check references to ensure they will work with you long after the sale is completed.

    Seek Client References and Feedback: Request references from the Salesforce partner’s past clients who have implemented AI solutions. Gather feedback on their experiences, challenges faced, and the overall success of the projects. This firsthand information can be invaluable in your decision-making process.

    By considering these tips, doing your homework and keeping an open mind to new partners that are ahead of the game, you can enhance your chances of selecting a Salesforce partner with the right AI capabilities. Finding that special, highly qualified partner to effectively integrate artificial intelligence into your Salesforce environment is the key to your future AI success!

  • 10 ChatGPT Prompts to Learn Any Language

    10 ChatGPT Prompts to Learn Any Language

    ChatGPT’s voice feature is a game-changer for language learners who want to take their skills to the next level. This feature is not just an extra; it’s a whole new way of learning languages. In this article, we show you ten prompts that use ChatGPT’s voice feature to help you learn any language. These prompts are not just exercises; they are opportunities to immerse yourself in the language and learn from feedback. With ChatGPT’s voice feature, you can learn a language that suits your needs and preferences in the digital era.

    Original Article Here

    1. Bilingual Dialogue for Enhanced Comprehension:

    Imagine having a conversation where you speak in your native tongue, and ChatGPT replies in the language you’re learning. This method is not just about learning new words; it’s about understanding context and developing listening skills in a practical setting.

    2. Slang Translation – Bridging the Cultural Gap:

    Often, the most challenging aspect of a new language is grasping the colloquialisms and slang. ChatGPT helps demystify these, offering insights into everyday speech that traditional learning methods might overlook.

    3. Mastering Basic Greetings with Pronunciation Tips:

    If you’re starting, focusing on greetings and common phrases is crucial. ChatGPT assists not just in learning these phrases but also in perfecting the pronunciation, ensuring you sound just like a native speaker.

    4. Interactive Conversation and Correction:

    Engage in a dynamic conversation with ChatGPT. What’s unique here is the model’s ability to correct your language use, providing a supportive environment for improvement.

    5. Vocabulary Quiz for Enhanced Learning:

    Test your knowledge with an interactive vocabulary quiz. Guess the meanings of foreign words, a fun and effective way to expand your linguistic repertoire.

    6. Basic Sentence Translation for Daily Use:

    Learn how to translate simple sentences, a skill that’s invaluable for everyday conversations. This exercise is about building a foundation in the new language, one sentence at a time.

    7. Roleplaying Real-Life Scenarios:

    Practice makes perfect, especially in language learning. Engage in roleplay scenarios like ordering food in a restaurant. This prompt is about applying your language skills in practical, real-world situations.

    8. Idioms Understanding for Fluent Conversations:

    Idioms are the essence of fluency in any language. ChatGPT helps you not just learn but also use these idioms correctly, enhancing your conversational skills.

    9. Oral Exam Preparation:

    If you’re preparing for a language proficiency test, ChatGPT can simulate an oral exam environment. This practice is invaluable for building confidence and readiness for the actual test.

    10. Listening Comprehension Tests:

    Finally, improve your listening skills by engaging in exercises that involve listening to passages and answering questions. This is crucial for complete language comprehension.

  • A Simple Explanation to what a LLM really is!

    A Simple Explanation to what a LLM really is!

    Imagine you have a giant notebook filled with millions of pages. Each page has different words written on it – some are common words like “hello” and “goodbye,” while others might be more complex or obscure, like “algorithm” or “quantum physics.” Now imagine that someone asks you to find the word “cat” in this giant notebook. You’d have to flip through a lot of pages, right?

    A Large Language Model (LLM) is kind of like that giant notebook but even bigger and more advanced. It’s essentially a computer program or algorithm designed to understand and generate human-like text by learning from vast amounts of data it has been trained on. This training data can include books, articles, websites, social media posts, and other forms of written communication.

    Think of LLMs as super smart autocorrect features for your phone that not only corrects spelling but also understands the context and suggests relevant words or phrases based on what you’re typing. They are used in various applications like chatbots, language translation, content generation, and more. The more data they have to learn from, the smarter and better they become at understanding human language and generating text that seems almost indistinguishable from a real person.

    In summary, an LLM is essentially a computer program or algorithm designed to understand and generate human-like text by learning from massive amounts of written data it has been trained on.

    So what really are Large Language Models?

    Large language models (LLMs) are computer programs that use artificial intelligence and natural language processing to understand and generate human language. They are also known as neural networks, deep learning models, or transformers. LLMs can be used for various tasks such as chatbots, summarization, content generation, sentiment analysis, and more.

    How do Large Language Models Work?

    LLMs work by using a complex system of interconnected nodes that represent words, phrases, sentences, or even larger units of meaning. These nodes are connected by links called edges, which allow the nodes to communicate with each other and share information. Each node has a weight or value that determines how much it contributes to the overall output of the model. The weights are adjusted during training, which is a process of feeding the model with data and updating its parameters based on feedback.

    The main idea behind LLMs is that they can learn from large amounts of text data and discover patterns or relationships in language. For example, an LLM can learn to recognize words as nouns, verbs, adjectives, or other parts of speech, and how they are used together in sentences. It can also learn to generate coherent texts based on a given input or context, such as answering questions, writing summaries, or creating stories.

    Where do Large Language Models Come From?

    LLMs come from the field of artificial intelligence and natural language processing, which is dedicated to creating machines that can understand and produce human language. LLMs are based on a subfield called deep learning, which uses neural networks to model complex patterns in data. Neural networks consist of layers of nodes that learn to represent features or functions from the input data. One of the most popular and powerful types of LLMs is called transformers, which were introduced by Google researchers in 2017. Transformers use an attention mechanism, which allows the model to focus on relevant parts of the input when generating output.

    Where are Large Language Models Headed?

    LLMs are still a rapidly evolving and advancing field, with many possibilities for future applications and improvements. Some of the directions that LLMs could go include:

    – Improving their accuracy, generalization, and diversity by training on larger and more diverse data sets or using different architectures or methods.

    – Expanding their capabilities to handle more complex and nuanced language tasks, such as dialogue, reasoning, or knowledge extraction.

    – Enhancing their security and privacy by using encryption, authentication, or secure storage of user data and model parameters.

    – Developing ethical guidelines and standards for the use and application of LLMs in various domains and contexts.

    What are some Security-Related Challenges of Large Language Models?

    LLMs can pose some security challenges, such as:

    – Leaking sensitive or personal information that was used to train or evaluate the model, which could compromise user privacy or data protection.

    – Generating harmful or offensive content, such as hate speech, abusive language, or misinformation, which could damage reputation, trust, or social cohesion.

    – Being manipulated or exploited by malicious actors, such as hackers, spammers, or terrorists, who could use the model for various nefarious purposes, such as spreading fake news, influencing opinions, or enabling cyberattacks.

    – Breaking down or deviating from their intended purpose or design, which could lead to unpredictable or undesirable outcomes, such as errors, biases, or failures.

    So can a LLM actually think?

    A large language model (LLM) is not the same as a human mind, but it can simulate thinking by using artificial intelligence and natural language processing to understand and generate human language. It can learn from data, discover patterns or relationships, and produce outputs based on its internal representations of the input and task. However, an LLM does not have consciousness, self-awareness, emotions, or intentions like a human being.

  • Demystifying AI: Shared Tenancy vs. New Siloed LLMs – Why Understanding Matters to You!

    Demystifying AI: Shared Tenancy vs. New Siloed LLMs – Why Understanding Matters to You!

    The AI Landscape: A Bird’s Eye View

    Artificial Intelligence has become the driving force behind innovation, transforming the way businesses operate and make decisions. Within this AI landscape, most companies that offer AI, utilize APIs to shared “Large Language Models” (LLMs). I see a distinct issue with this and would like to explore the alternative approach that has emerged – siloed LLMs and other AI models.

    The Shared Tenancy Conundrum:

    Shared tenancy models involve multiple organizations sharing a common AI model fed with data from their respective enterprise systems. It’s like a shared workspace for AI, where diverse data mingles in pursuit of collective insights. However, despite this, concerns arise, particularly in the context of data security and obvious patterns emerging.

    Why I Stand as a Thought Leader:

    As a thought leader in the AI domain, my concerns echo through the corridors of innovation. I’ve observed a trend among many SaaS companies claiming to have tackled the challenges associated with shared tenancy models via the use of anonymizing data and using encryption. However, the reality often falls short of the assurances, leaving organizations vulnerable to potential risks. For example, if a model is asked by several staff members to draft a response to an email, the AI could potentially discover patterns in the content of all the emails being piped through the AI model (from staff in that organization) and then deduce from there, even though specifics on individuals has been redacted.

    Data Security: A House of Cards?

    Shared tenancy models walk a fine line when it comes to data security. While encryption layers are touted as safeguards, the intermingling of data from different organizations raises red flags. My qualms stem from the fact that, in practice, not all companies are as fortified against breaches as they profess to be. The shared nature of these models poses a risk – a breach in one organization’s data could ripple across the shared space, affecting others in the consortium.

    Customization Woes: One Size Fits None:

    A shared model, by its nature, tends to be a jack of all trades, master of none. My concerns deepen when considering the limited room for customization. Organizations, each with unique objectives, find themselves constrained by a model that might not align seamlessly with their specific needs. This lack of adaptability can stifle innovation and hinder the full potential of AI for individual organizations. A powerful integration of AI would mean the appropriate use of multiple different AI models or even better, a series of trained models that are tailored to the organization.

    Siloed LLMs: An Oasis of Security and Customization:

    Contrastingly, siloed LLMs stand as a beacon of security and tailored precision. As a thought leader in this space, I champion the cause of siloed models within secure containers for individual organizations. The benefits are clear – enhanced data security and the freedom to fine-tune the AI model according to the distinct requirements of each organization, coupled with the use of “Vector” storage, will dramatically improve the benefits of AI in a given use case.

    Why Understanding Matters to You:

    Now, why should you care about this AI showdown? As businesses increasingly integrate AI into their operations, your data security and the adaptability of the AI tools at your disposal matter. If you’re relying on a shared tenancy model, it’s vital to scrutinize the assurances provided by your AI service provider. I implore you to question whether your organization’s valuable data is truly shielded from potential breaches, and if the promised customization aligns with your unique goals. This may be hard to ascertain from your vendors, especially when their marketing and comms touts “Trust” but falls short on the delivery.

    My Final Thoughts:

    In the fast and ever-evolving landscape of AI, understanding the nuances between shared tenancy models and siloed LLMs is not just an academic exercise – it’s a practical necessity. My concerns about the nonchalant approach of many vendors are a call to action. As you navigate the AI terrain, consider the path that aligns best with your organization’s security and customization needs. In this journey of demystifying AI, your informed choices pave the way for a more robust, secure, and tailored AI experience.

  • Artificial Intelligence Doesn’t Hallucinate – It’s Just Being Human!

    Artificial Intelligence Doesn’t Hallucinate – It’s Just Being Human!

    Let me explain.

    One of the most obvious outcomes from the covid pandemic & the turbulent political environment over past few years is a recognition that we all tend to cling to our opinions and beliefs very, very tightly.

    “I just know it” is often said; sometimes in the face of overwhelming evidence to the contrary.

    But where does this certainty come from and just how sturdy is that foundation?

    There is a word for this phenomena.

    The Dunning-Kruger effect is a cognitive bias where people with limited competence in a specific domain overestimate their abilities. It occurs because they lack self-awareness and cannot accurately assess their own skills. Essentially, they think they know more than they do.

    The initial study by David Dunning and Justin Kruger in 1999 focused on logical reasoning, grammar, and social skills. It revealed how people often misjudge their abilities due to a lack of metacognition (the ability to analyze one’s own thoughts).

    Fact is, our thoughts and opinions are actually related closely to our memory.

    Typically our opinions, our beliefs and our sense of self comes from,

    1. Cognitive biases: These relates to systematic errors in thinking that affect how people process and interpret information; and they certainly influence their beliefs and decisions. Many cognitive biases are related to memory, such as confirmation bias, which is the tendency to seek out and remember information that confirms one’s existing opinions, or hindsight bias, which is the tendency to remember past events as more predictable or consistent with one’s current views than they actually were.
    2. Emotional memory: This is the memory for events that are emotionally arousing or significant. Emotional memory is enhanced by the release of stress hormones that act on the amygdala, a brain region involved in processing emotions and fear. Emotional memory can affect opinions by making them more vivid, persistent, and more influential on behavior. However, emotional memory can also be inaccurate or intrusive, leading to false or distorted recollections of events that may affect opinions.
    3. Narrative memory: This is memory for autobiographical events that are organized into coherent stories that give meaning and context to one’s life. Narrative memory is influenced by one’s concept of self, goals, values, and culture. Narrative memory can affect opinions greatly by shaping one’s identity, worldview, and sense of purpose. However, narrative memory can also be selective, reconstructive, and influenced by external sources, leading to inconsistencies or gaps in one’s personal history that may affect opinions.

    So, if our opinions are linked inexorably to our memories. How good is yours?

    Perceiving oneself as having a great memory is critical to every individual’s confidence, well-being, and daily functioning.

    • When we trust our memory, we approach tasks with confidence, whether it’s recalling information needed to do your job, approaching an exam or remembering important dates.
    • Feeling that our memory is reliable boosts our overall self-esteem and encourages us to take on challenges.
    • Memory enhances social interactions. Remembering names, details, and shared experiences makes us more likable and relatable.
    • Workplace: Professionals rely on memory for productivity. From meetings to deadlines, memory competence contributes to success.
    • Reducing anxiety: Fear of memory lapses can cause stress. Feeling confident in our memory alleviates anxiety.
    • Nostalgia: A vivid memory lets us relive cherished moments, fostering positive emotions.
    • And, a good memory accelerates learning. We build on past knowledge, adapt, and innovate.

    This all leads us to want to over-estimate just how great our memory is.

    I know that we all want to feel that our own memory is an amazing recording device; accurately taking note of the details of everything that we have experienced, read, visited and heard about. But the unfortunate fact is that it is not!

    Elizabeth Loftus, one of the World’s leading experts on human memory, says, “If memory is the bedrock of the self … it is built on shifting sands.”

    No-one has done more than psychologist Loftus (from the University of California, Irvine,) to expose the fallibility of human memory. In the 1990s, amid growing panic over claims of satanic abuse rings, the psychologist showed how easy it is for people to develop false memories of events that never ever happened.

    All it took was being asked repeatedly to imagine them. At the time, unfortunately, this was a common psychotherapy technique to recover supposedly ‘repressed memories’.

    Over the past three decades, Loftus, has become well-known for her work as an expert witness in legal cases. Her ongoing research on the fallibility of eyewitness testimony has taken on fresh importance in an era of fake news, the Me-Too movement and digital image manipulation. (1)

    Elizabeth Loftus has found ordinary memory to be so malleable that she can easily prompt volunteers to “remember nonexistent broken glass and tape recorders; to think of a clean-shaven man as having a mustache, of straight hair as curly, of hammers as screwdrivers, to place a barn in a bucolic scene that contained no buildings at all, to believe in characters who never existed and events that never happened.”

    “We now have a new paradigm of memory,” notes Loftus, “where memories are understood as a creative blending of fact and fiction, where images are alchemized by experience and emotion into memories.” (2)

    So, we have established that our opinions, the facts we just KNOW, the statements we make, the feelings we believe, in fact most of our day-to-day activity relies heavily on memory.

    So how chilling is it that this memory of ours is so malleable, that it changes over time, that it can be easily re-made by suggestion, and is so tenuous.

    I guess you could say that a lot of the things we truly ‘believe’ could well be hallucinations of sorts?

    Much like AI’s sometimes do.

    AI ‘hallucination’ refers to a phenomenon where large language models (LLMs), such as OpenAI’s GPT-4, Bard AI or Google’s Palm, can generate outputs that contain false or misleading information presented as fact.

    These outputs are sometimes nonsensical or altogether inaccurate and may not align with real data or events at all. Sounds human to me.

    These are often reported on and discussed as a major hurdle to the use of artificial intelligence – but just how different is this really to asking a business colleague for help and assuming they actually know what they are talking about?

    Or perhaps relying on your own memory to solve a problem assuming that your memory must obviously be correct?

    If human memory and knowledge is as fallible as the ‘facts’ suggest, I feel that we may be giving AIs a bad rap! It may be that they are far more human than we would like to admit!

    Food for thought?


    [1] Memory special: Can you trust your memories? | New Scientist

    [2] It’s Magical, It’s Malleable, It’s… Memory | Psychology Today