They are claiming 2021 was the year of data and 2024 is the year of Agents. Computers doing part of the work for your workforce.
Artificial Intelligence
Deepak Chopra and A.I.
Renowned medication expert is leaning into A.I. to help find peace. In his newest book Digital Dharma, Chopra talks about how A.I. can help with spiritual development. Now Chopra is available as an A.I. twin. It seems it’s basically ChatGPT for his books.
Dharma is the life you are supposed to live but is interfacing with a computer really the key to spiritual enlightenment and peace? Or is it unplugging and reflecting in nature?
You can check out Digital Deepak here.
Why Deepak Chopra believes AI could be a “spiritual guide” (msn.com)
Somatic’s Bathroom Cleaning Robot
This robot only costs a $5.60 per hour and locks itself back in the closet after it’s done cleaning the bathrooms.
What Is the Next Big Career AFTER A.I.?
After AI, the next big career trend could revolve around quantum computing, biotechnology, sustainability engineering, neuroscience, and space technology. While AI will continue to evolve and be integrated into many industries, these fields are likely to experience significant growth and demand for skilled professionals in the coming decades. Let’s explore these potential career paths:
1. Quantum Computing
- Why it’s next: Quantum computing promises to revolutionize problem-solving by performing calculations exponentially faster than classical computers. This could transform industries such as cryptography, materials science, drug discovery, and financial modeling.
- Career opportunities:
- Quantum Software Engineer: Developing algorithms and software for quantum computers.
- Quantum Hardware Engineer: Designing and maintaining quantum hardware systems.
- Quantum Cryptographer: Creating new forms of encryption that are resistant to quantum attacks.
- Quantum Research Scientist: Conducting research to improve quantum computing capabilities.
- Key skills: Quantum mechanics, advanced mathematics, quantum algorithms, computer science, and physics.
2. Biotechnology and Genetic Engineering
- Why it’s next: Advances in CRISPR gene-editing, synthetic biology, and bioinformatics are opening doors to new healthcare solutions, personalized medicine, and agricultural innovation. This could lead to breakthroughs in curing genetic diseases, enhancing human health, and improving food security.
- Career opportunities:
- Genetic Engineer: Using CRISPR and other technologies to edit genes for therapeutic or agricultural purposes.
- Bioinformatics Specialist: Analyzing biological data using software and machine learning for personalized medicine or research.
- Biotech Product Developer: Creating biotechnological products such as biofuels, lab-grown meat, or pharmaceuticals.
- Pharmaceutical Data Scientist: Leveraging AI and data science to discover new drugs or therapies.
- Key skills: Molecular biology, genetics, bioinformatics, computational biology, machine learning, and biomedical engineering.
3. Sustainability and Environmental Engineering
- Why it’s next: As climate change accelerates, careers in sustainability, renewable energy, and environmental engineering will grow rapidly. This field focuses on developing technologies and systems to combat climate change, promote circular economies, and create sustainable cities.
- Career opportunities:
- Renewable Energy Engineer: Developing solar, wind, and geothermal energy solutions.
- Sustainable Architect/Engineer: Designing eco-friendly infrastructure and energy-efficient buildings.
- Carbon Capture Scientist: Innovating new methods for reducing carbon emissions and storing carbon.
- Circular Economy Consultant: Advising businesses on how to minimize waste, reuse resources, and create sustainable supply chains.
- Key skills: Environmental science, energy systems, material science, ecological engineering, and sustainability policy.
4. Neuroscience and Brain-Machine Interfaces
- Why it’s next: Advances in neuroscience and brain-computer interfaces (BCIs) have the potential to unlock new frontiers in medical treatment, cognitive enhancement, and even human-computer interaction. BCIs could help treat neurological conditions or enhance human capabilities.
- Career opportunities:
- Neuroscientist: Researching how the brain works and applying that knowledge to improve mental health, cognitive enhancement, and disease treatment.
- Brain-Machine Interface Engineer: Developing devices that connect the brain to computers, allowing for direct control of machines or medical prosthetics.
- Neuroethicist: Exploring the ethical implications of brain-enhancing technologies and BCI advancements.
- Cognitive Data Scientist: Analyzing brainwave data and applying AI to interpret brain activity for health or productivity applications.
- Key skills: Neuroscience, electrical engineering, AI, brain-computer interface development, neuroimaging, and machine learning.
5. Space Technology and Exploration
- Why it’s next: With growing interest in space exploration (e.g., NASA’s Artemis program, SpaceX’s Mars ambitions), space technology will become a major frontier for both scientific discovery and commercial endeavors. This includes satellite technology, space mining, colonization, and space tourism.
- Career opportunities:
- Space Engineer: Designing spacecraft, satellites, and systems for space exploration and habitation.
- Astrobiologist: Studying the potential for life beyond Earth and developing methods for detecting extraterrestrial life.
- Space Mining Engineer: Developing technologies for extracting resources from asteroids or other celestial bodies.
- Space Tourism Designer: Creating safe and comfortable space travel experiences for commercial passengers.
- Key skills: Aerospace engineering, robotics, astrophysics, planetary science, and materials science.
6. Advanced Robotics and Automation
- Why it’s next: Robotics will continue to evolve, particularly in fields like healthcare (surgical robots), logistics (automated warehouses), and home automation. As robots become more intelligent and autonomous, there will be demand for advanced robotics engineers and AI integration specialists.
- Career opportunities:
- Robotics Engineer: Designing and building robots for various applications, from manufacturing to healthcare.
- Automation Specialist: Creating systems that automate complex tasks in industries like agriculture, logistics, and medicine.
- Robot Ethics Consultant: Advising on the ethical use of autonomous systems and robots in society.
- Wearable Robotics Developer: Developing exoskeletons or assistive devices to help people with disabilities or enhance human physical abilities.
- Key skills: Mechanical engineering, robotics, AI, control systems, and human-robot interaction.
7. Cybersecurity and Digital Trust
- Why it’s next: As AI, quantum computing, and IoT (Internet of Things) become more prevalent, securing digital systems will become increasingly complex. Cybersecurity will expand beyond traditional IT systems to protect AI models, smart cities, autonomous vehicles, and digital identities.
- Career opportunities:
- Quantum Cryptographer: Developing encryption methods that are resistant to quantum computing attacks.
- AI Security Specialist: Protecting AI systems from adversarial attacks and ensuring the integrity of machine learning models.
- Ethical Hacker: Finding and fixing vulnerabilities in digital infrastructure, particularly in AI and IoT systems.
- Digital Identity Architect: Creating systems to protect individual privacy and manage digital identities in a secure way.
- Key skills: Cybersecurity, cryptography, AI security, quantum computing, and ethical hacking.
8. Ethics and Policy for Emerging Technologies
- Why it’s next: As technologies like AI, biotech, quantum computing, and space exploration advance, there will be a need for professionals who can guide policy, regulation, and ethics. Balancing innovation with societal impact will be a crucial challenge.
- Career opportunities:
- Tech Policy Analyst: Working with governments, NGOs, or corporations to develop policies that govern the ethical use of emerging technologies.
- AI Ethics Consultant: Ensuring that AI systems are developed and deployed in ways that align with human values and fairness.
- Environmental Tech Regulator: Crafting policies that regulate the development and use of technologies related to sustainability and environmental protection.
- Biotech Ethics Advisor: Guiding ethical decision-making around genetic modification, synthetic biology, and other biotech innovations.
- Key skills: Law, ethics, public policy, and understanding of emerging tech such as AI, biotech, and quantum computing.
In conclusion, after AI, the next big career fields will likely involve quantum computing, biotechnology, sustainability, neuroscience, space exploration, and advanced robotics. Each of these fields will offer unique opportunities for individuals with the right skills and expertise, allowing them to shape the future of technology and society.
How does Salesforce use A.I.?
Salesforce integrates AI into its platform primarily through Salesforce Einstein, a suite of AI-powered features designed to help businesses improve customer relationships, sales, service, and marketing. Here’s how Salesforce uses AI:
1. Einstein for CRM (Customer Relationship Management)
Salesforce Einstein enhances the CRM experience by automating tasks, predicting outcomes, and providing actionable insights. It uses machine learning, natural language processing (NLP), and computer vision to assist businesses in managing customer relationships more efficiently.
- Einstein Prediction Builder: It allows users to create custom AI models to predict business outcomes, like customer churn or sales performance, without needing coding skills.
- Einstein Discovery: This tool analyzes business data and identifies patterns, then recommends actions to optimize outcomes.
- Einstein Voice: Enables voice-activated interactions, such as updating records or querying data through voice commands.
2. Einstein Analytics
Salesforce uses AI-driven analytics to provide deeper insights from data. This helps businesses understand trends, customer behavior, and sales forecasts.
- Sales Forecasting: AI predicts future sales based on historical data, helping teams make better decisions.
- Automated Insights: Einstein Analytics surfaces hidden trends, key insights, and areas for improvement in the business.
3. Sales Cloud Einstein
In the Sales Cloud, Einstein helps sales teams prioritize leads and opportunities based on predictive analytics, scoring, and insights.
- Lead Scoring: Einstein assigns a score to leads based on the likelihood of conversion, helping salespeople focus on high-potential opportunities.
- Opportunity Insights: AI analyzes sales data and customer interactions to offer guidance on how to close deals more effectively.
4. Service Cloud Einstein
In customer service, AI helps improve response times, automate case management, and provide personalized customer support.
- Einstein Bots: AI-driven chatbots can resolve common customer issues, reducing response times and freeing up human agents to handle more complex cases.
- Next Best Action: AI provides customer service reps with recommendations on the best course of action based on past interactions and case history.
5. Marketing Cloud Einstein
For marketing teams, Einstein provides predictive insights to help create personalized customer journeys and targeted campaigns.
- Predictive Content and Audience Segmentation: AI automatically segments audiences and suggests the best content or product recommendations for individual customers.
- Engagement Scoring: Predicts the likelihood of customer engagement based on previous behavior, helping to optimize marketing efforts.
6. Commerce Cloud Einstein
Salesforce AI is also embedded in e-commerce experiences, helping businesses deliver personalized recommendations and optimize the shopping experience.
- Personalized Product Recommendations: AI-driven product recommendations based on browsing and purchase history enhance cross-selling and up-selling opportunities.
- Search Recommendations: AI improves on-site search by predicting what customers are looking for and providing relevant results.
7. Einstein for Developers
Salesforce also offers AI tools for developers to build custom AI solutions within the Salesforce platform, such as:
- Einstein Language: NLP tools to analyze customer sentiment, categorize inquiries, and build custom language models.
- Einstein Vision: Provides image recognition and computer vision tools for tasks like product identification or brand recognition in images.
8. AI for Workflow Automation
AI is integrated into workflow automation in Salesforce, enabling businesses to streamline processes by automating repetitive tasks such as data entry, email generation, or case routing.
By leveraging these AI-driven tools, Salesforce empowers businesses to make smarter decisions, improve operational efficiency, and create more personalized customer experiences across sales, service, marketing, and commerce.
The Best A.I. Resume on the Internet
This was in our LinkedIn feed and is fantastic. This is the true job description of the future.
Forget about Free Speech, Worry about A.I. Driven Sentiment
I keep hearing this term ‘protect free speech’.
In today’s age this is nonsense.
We are so overpowered by what is being injected into our psyches from scrolling that it won’t matter if we have free speech or not. The machines are making us think what they and others want.
Unplug and remove social apps from your phone! Get a bird feeder and a book.
Can Tesla ($TSLA) Reach $32 Trillion Market Cap
I used to think no company could reach $32 Trillion in Market Cap. Companies like Apple have just passed $1 Trillion.
After I listened to Elon Musk speak recently, I am not so sure this is out of question. $32 Trillion sounds insane. How can a car company ever be valued like that you ask?
Firstly, the cars will only be a fraction of their revenue in the near future. Tesla is also a leading battery company and now with the new Optimus robot, he’s making a case it could be very very profitable in the future. This doesn’t include how they will monetize autonomy.
With more than a 1,000 Optimus robots scheduled to start working in Tesla factories this year, Musk believes Tesla is well positioned to own the humanoid robotics market. His calculations assume at least 1 robot for every person on earth.
At a $10K cost to build and a sales price of $20k, the Optimus robots could actually be a huge cash cow for the company.
The question in my opinion: do we all actually need and want a personal robot for $20k.
I am not sure I do…? Plus the human form freaks me out due to the Uncanny Valley.
Watch Elon speak at the 2024 Tesla ($TSLA) Shareholder meeting below.
Why does A.I. Require So Much Electricity
Currently organizations involved in the A.I. race are also buying or building nuclear power plants. Why? AI models, especially large-scale deep learning models, require significant computational resources, which in turn demand substantial amounts of electricity for several reasons:
- Computational Intensity: Training AI models involves processing vast amounts of data through complex mathematical operations, such as matrix multiplications and convolutions. Deep learning models, especially those with millions or even billions of parameters (like GPT-3 or large-scale neural networks used in computer vision), require immense computational power to train effectively.
- Hardware Requirements: To handle the computational workload, AI training often relies on specialized hardware like Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs). These processors are designed to perform parallel computations efficiently, but they consume more power compared to traditional CPUs due to their high-performance capabilities.
- Data Center Operations: AI training typically occurs in large-scale data centers equipped with racks of servers and cooling systems to manage heat generated by intensive computations. Running these data centers requires substantial amounts of electricity to power the servers and maintain optimal operating conditions.
- Model Iterations: Training AI models is an iterative process where models are trained, evaluated, adjusted, and re-trained multiple times to achieve desired performance. Each iteration requires running computations over the entire dataset, contributing to overall energy consumption.
- Research and Development: Beyond training models, AI research and development involve running simulations, experiments, and testing various algorithms, all of which can also be computationally intensive and energy-demanding.
Efforts are underway to optimize AI algorithms, develop more energy-efficient hardware, and implement sustainable practices in data center operations to mitigate the environmental impact of AI’s electricity consumption. However, the inherent computational demands of AI tasks mean that electricity consumption remains a significant consideration in deploying and scaling AI technologies.
What is a Token
In the context of training AI models, a token generally refers to a single unit of input data that the model processes at one time. The term “token” can be used in various ways depending on the specific type of AI model and its architecture, but here are a few common interpretations:
- Natural Language Processing (NLP): In NLP tasks such as language modeling or machine translation, a token usually represents a word or a subword unit (like parts of words created by algorithms such as Byte-Pair Encoding or WordPiece). For instance, in a sentence “I love natural language processing,” each word would typically be considered a token.
- Computer Vision: In image processing tasks, a token could represent a pixel or a patch of pixels. Sometimes, tokens in computer vision are used in the context of transformer models where patches of an image are treated as tokens for processing.
- Audio Processing: In speech recognition or audio processing tasks, a token could correspond to a small segment of audio data, often represented as a spectrogram or a waveform.
- Reinforcement Learning: In reinforcement learning, a token might refer to a state-action pair in a specific environment, which the model uses to learn and improve its decision-making over time.
The size and nature of tokens can vary greatly depending on the specific AI model architecture and the requirements of the task. Tokens are fundamental to how models perceive and process input data, making them a critical concept in AI training and inference.