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What Will Working with AI Really Require?



Despite concerns about machines replacing human workers, research challenges the overhyped claims of ascendant AI. In most knowledge-intensive tasks, workers will more likely find themselves augmented in partnership with machines than automated out of a job. Humans and machines will simultaneously collaborate and compete with one another, like a track team competing in various events. In some events, like the 100-yard dash, teammates compete against each other, but in others, such as the relay race, they work together towards a common goal.

In such a relationship, humans and AI systems both need distinct competitive and cooperative skills. Competitive skills refer to the unique advantages that either humans or AI possess over the other, while cooperative skills enhance the ability of humans and AI to work together effectively. To foster a symbiotic relationship between humans and AI, organizations must find the appropriate balance between investing in human skills and technological capabilities — and think strategically about how they attract and retain talent.

Humans’ competitive and cooperative skills

AI may not replace workers in a human-centered workplace, but it could fundamentally transform their work. In order to remain relevant and indispensable, humans need to work with and against the machines.

Humans’ cooperative skills

Effectively collaborating with AI systems — working with them — requires data-driven analytical abilities, but also understanding about the capabilities and limitations of the machines (areas where human intervention is most required), how to interpret and contextualize AI-generated insights, and the ethical considerations of AI-powered decision making. These include:

Data-centric skills: The ability to understand the results generated by algorithms to inform and support decision-making. A recent survey highlighted (1) the ability to distinguish relevant data and evaluate its credibility, (2) capability to validate results by testing hypotheses through A/B testing, and (3) skill in creating and tailoring clear and comprehensible visualizations to communicate results to multiple stakeholders.

AI literacy: Understanding how algorithms work, how they can support and augment human decision-making as well as the limitations and biases that may be present in their decision-making processes. Area experts will likely take on the responsibility of developing fairness criteria for algorithmic outcomes that promote equity, especially for vulnerable populations, and continuously auditing algorithmic results against these criteria.

Algorithmic communication: Understanding how to articulate human needs and objectives to algorithms, as well as how to interpret and explain the results generated by algorithms to others is important and research shows that we often err by talking to machines – even advanced AI tools – as though they were human. We do better when we recognize that we should talk to machines in specific ways that build on their strengths. For example, through “prompt engineering,” or crafting prompts to elicit most effective responses from AI systems, humans can teach AI models to produce the desired results for specific tasks.

Humans’ competitive skills

People also need to hone the human-centered skills and abilities that cannot be replicated by machines — that help them work against AI partners — such as those rooted in emotional intelligence (e.g., communication skills for interacting with other human stakeholders), a strategic and holistic perspective, critical thinking and intuitive decision making. These include:

Emotional intelligence: The ability to recognize one’s own emotions and reflect on them in the context of interacting with algorithms, as well as understanding and communicating the emotional implications of algorithm-generated results. For example, the human customer service agents may not solely rely on scripts or real-time advice provided by the AI agents, but instead personalize solutions by empathetically comprehending the customers’ requirements or feelings.

Holistic and strategic thinking: The ability to consider the big picture and understand how algorithmic results fit into the larger context of a problem or decision. For example, algorithmic inference can inform pathologists, but they still need to consider factors such as patients’ medical history, lifestyle, and overall health to arrive at an informed and comprehensive diagnosis.

Creativity and outside-the-box thinking: The ability to think creatively and use algorithms in novel and innovative ways. For instance, AI systems are used to analyze massive consumer data and identify patterns in the interests and behavior of a target audience, but it is the creative thinking of marketers that will craft a message that resonates with the audience.

Critical and ethical thinking: The ability to critically assess machine inferences, and to understand the ethical implications and responsibilities associated with using algorithms, including privacy and accountability. As generative AI such as ChatGPT are increasingly integrated into various products, experts in different business domains are needed to work alongside these systems to continuously address potential false or biased information to which these systems are prone.

AI’s competitive and cooperative skills

It is not only humans that must acquire new capabilities. While AI systems are rapidly expanding their competitive abilities over humans, they still need to improve their cooperative skills in order to be widely adopted by organizations. In particular, the lack of explainability remains a challenge in high-stakes decisions, hindering accountability and compliance with legal requirements. For example, if the AI’s decision-making process remains opaque to medical professionals, it will impede the adoption of these systems in healthcare, even if these systems deliver near-optimal decisions.

AI’s cooperative skills

To work effectively with human partners, AI systems need skills such as:

NLP (Natural Language Processing): The ability to process, analyze, understand and mimic human language. Systems like ChatGPT excel at interacting with humans because they make it easy for people to ask questions and express themselves in a natural way, including expressing emotions like excitement, frustration, or surprise. The reality, however, is that these systems are far from sentient. Situations that go beyond a function are best done by a human or with human supervision. For example, AI can analyze and reveal patterns in healthcare data, but it should not replace a physician’s role in providing individualized care to patients.

Explainability: The ability to provide humans with clear and understandable explanations of its decision-making process and results. The inherent inscrutability of deep-learning AI is an ongoing challenge that requires multiple solutions, including building an “explainability framework” that addresses the risks of AI black boxes to specific industries and organizations. Technological solutions may also involve adding explainability engines, which offer human-readable explanations for AI ‘systems’ decisions and predictions, particularly for critical areas like healthcare and finance.

Adaptability and personalization: The ability to learn from previous interactions and personalize responses based on individual users. For example, personal intelligent assistants are growing in importance in helping people tackle information and communication overload. By analyzing a user’s activities, these assistants work collaboratively with workers in an individualized manner, enhancing their productivity in areas such as time management, meeting organization, and communication assistance.

Context awareness: The ability to understand the context in which an interaction is taking place and respond accordingly. For instance, in e-commerce websites, chatbots that present context awareness can analyze a user’s previous inquiries and purchase history to offer solutions or recommendations that are more pertinent to the customer’s needs.

AI’s competitive skills

AI systems continue to present unique competitive advantages, such as:

Analytical capacities: The ability to perform complex calculations, process large amounts of data, and identify patterns and relationships within the data. For example, AI systems are becoming more competent at detecting fraudulent transactions in massive datasets of credit card transactions.

Generativity: The ability to generate novel and unique outputs that are not simply reproductions of existing data. Using large models and neural networks to analyze patterns, generative AI is transforming the creation of image, text, and even music that resembles those created by human experts. These systems automated content generation, improve content quality, increase content variety, and offer personalized content.

Performance at scale: The ability to scale operations efficiently, handle a large number of real-time transactions and support large-scale applications without sacrificing performance. For example, AI systems have demonstrated superior ability to process thousands of credit card applications real-time or offer “algorithmic management” of thousands of Uber drivers and riders simultaneously, creating a structured and consistent operational framework at an unprecedented scale.

Racing with and against the machines

The challenge for organizations trying to build a strategy for using new and more AI tools lies in designing organizational systems that effectively balance the competitive and cooperative skills of humans and AI. Organizations that seek to strike this balance should consider the following:

Democratize data to foster the continuous development of competitive human and machine skills. AI systems can generate data insights at scale and detect patterns often missed by the human eye, but translating that competitiveness into business growth and agility requires the very human skills of strategic thinking and creativity. To enable this type of collaboration, companies should democratize access to data throughout all levels of their organization. Nearly every role within your organization should be working alongside data analytics to inform how to make the workflows more efficient, make data-driven decisions and ultimately inform a better understanding of how to service the end customer. The more data visibility AI can give your workforce, the greater ability for humans to apply and develop their uniquely competitive skills.

Look outside your own organization’s walls for cooperative human skills. A recent Deloitte study found that nearly half (49%) of traditional workers — full-time employees — updated their skills more than a year ago or have never engaged in skills development, whereas 60% of the alternative workforce — defined as gig workers, freelancers, independent workers, and crowd workers — updated their skills within the past six months. In fact, 44% of alternative workers at large organizations hold a postgraduate degree according to new research by Upwork. This is likely due to the fact that most technical skill sets, according to research by IBM, experience a half-life of 2.5 years. And, according to Upwork’s database, the top in-demand skills are technical and related to web, mobile and software development. If your organization is struggling to keep up with cooperative human skills to work alongside machines, it may be time to engage a broader ecosystem of skills outside your organization.

Don’t let geography limit the skills your company is hiring for. The pandemic ushered in a new era of work as many organizations learned work could be done remotely. Technical work can be done almost anywhere in the world as machines have largely made geography irrelevant to finding the skills you need to cooperate with machines. Enabling remote work strategies will ensure your organization is equipped to capture the ever-changing talent landscape and help you win in the race with and against the machines.

By focusing on the balance of these skills, organizations can reap the benefits of an infinity loop between AI and human competitive skills. In this balance, humans may work toward “coopetition” as an arrangement where parties engage in both cooperative and competitive behavior. In such a relationship with AI systems, humans may leverage both the partnership with machines and their own competitive edge against the machine. This relationship helps to maintain their relevance and indispensability as algorithms are increasingly working as team members or even managers (i.e., algorithmic management).

This formulation offered here helps shape the future of education and skill development, by emphasizing the importance of focusing on skills that give humans a competitive advantage over machines, rather than those that we have already lost to machines. For example, the use of calculators and spell checkers no longer serve as our advantage as we surrendered these tasks to technology long ago.


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Revolutionizing Marketing: The Power of AI in the Digital Age



Embracing AI-Powered Marketing: Transforming Brands in the Digital Marketplace

In the crowded digital marketplace, standing out is challenging. Enter AI-powered marketing, a revolutionary upgrade transforming brands into digital powerhouses.

Hyper-Personalized Campaigns: Beyond Basic Personalization

Gone are the days of generic marketing. Today’s gold standard is AI-driven hyper-personalization. This approach uses customer data analysis to create deeply resonant, individualized marketing campaigns. With AI’s ability to segment audiences based on intricate criteria, including purchasing history and browsing behavior, your messages can hit the mark every time.

Enhanced Customer Journey Mapping

AI’s capabilities extend to mapping the entire customer journey. By predicting needs and preferences at each stage, AI aids in crafting narratives that guide customers from discovery to purchase, integrating your brand into their personal stories.

SEO Wizardry: Mastering Search Engine Dynamics

With ever-changing algorithms, SEO is a complex puzzle. AI serves as a sophisticated navigator, deciphering these changes through machine learning. It aids in keyword optimization, understanding search intent, and aligning content with search trends.

Predictive SEO

AI tools offer predictive SEO, anticipating search engine and user behavior changes. This proactive stance ensures your brand’s prominent visibility in search results, capturing the right audience at the right time.

Social Media Mastery: Crafting a Digital Narrative

AI transforms social media strategies from uncertain to precise. By analyzing vast social data, AI provides insights into resonating content.

Content Optimization

AI analyzes performance data to recommend effective content types. This data-driven approach refines your social media content strategy.

Engagement Analysis

AI examines user interaction nuances, understanding engagement patterns. It helps tailor interactions for maximum impact, including adjusting posting schedules and messaging for increased relevance.

Conclusion: Navigating the AI-Driven Marketing Landscape

AI-powered marketing is essential for thriving in the digital age, offering precision and personalization beyond traditional methods. For small businesses, it’s a chance to leverage AI for impactful, data-driven strategies.

As we embrace the AI revolution, the future of marketing is not just bright but intelligently radiant. With AI as your digital ally, your brand is equipped for a successful journey, making every marketing effort and customer interaction count.

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AI: Your Small Business Ally in a Digital Age



In the ever-evolving landscape of modern commerce, small business owners find themselves at a crossroads of opportunity and obsolescence. Enter Artificial Intelligence (AI) – once the exclusive domain of tech behemoths, it now stands as the great equalizer, offering small businesses a competitive edge previously unthinkable. The emergence of AI as a wingman for small businesses is not just a fleeting trend but a fundamental shift in how entrepreneurs can leverage technology to revolutionize their operations.

The 24/7 Customer Service Hero: Chatbots

In the digital storefront, customer service is the heartbeat of business survival and success. Chatbots emerge as the indefatigable heroes of this domain. Envision a customer service agent that never clocks out an entity that requires no sleep or sustenance yet delivers consistently and instantaneously. These AI-driven chat interfaces embody the essence of your brand’s voice, capable of handling a barrage of customer queries with a speed that outpaces the swiftest of typists. They are the embodiment of efficiency – ensuring that customer satisfaction is not just met but exceeded around the clock.

Unearthing Market Treasures: Data Dive

AI’s prowess in pattern recognition has catapulted data analytics into a realm once considered the stuff of science fiction. Small business owners armed with AI tools can sift through vast swathes of data to extract actionable insights. These algorithms act as modern-day oracles, predicting market trends, discerning customer behaviors, and offering sales forecasts with remarkable accuracy. Equipped with: this knowledge, small businesses, can navigate the market with the foresight and precision of an experienced captain steering through foggy seas.

Personalization at Scale: Customize Like a Boss

The age-old business mantra of the customer is king is given new potency with AI’s personalization capabilities. Tailoring the customer experience is no longer a luxury but a necessity. AI enables small businesses to offer bespoke experiences to consumers, making them feel like the sole focus of their attention. It’s personalization executed with such finesse that customers are left marveling at the thoughtfulness and individual attention, fostering loyalty and establishing deep-rooted brand connections.

Offloading the Mundane: Task Slayers

Repetitive tasks are the bane of creativity and innovation. AI steps in as the ultimate task slayer, automating routine chores that once consumed disproportionate amounts of time. From scheduling appointments to managing inventory, AI liberates entrepreneurs from the drudgery of administrative duties, freeing them to refocus on the creative and strategic endeavors that propel business growth.

Mastering Social Media: Social Savants

Social media – the pulsing vein of modern marketing – demands astuteness and agility. AI emerges as the savant of social media, capable of demystifying platform algorithms to optimize content delivery. It knows the optimal times to post, the types of content that resonate with audiences, and the strategies that convert passive scrollers into engaged customers. By automating your social media presence, AI transforms your brand into an online sensation, cultivating a digital community of brand ambassadors.

The Verdict: Embracing AI

For a small business owner, AI is not about an overnight overhaul but strategic integration. The goal is to start small, allowing AI to shoulder incremental aspects of your business, learning and scaling as you witness tangible benefits. The transition to AI-enablement does not necessitate a background in technology; it requires a willingness to embrace change and a vision for the future.

In summary, as the digital revolution marches forward, AI stands ready to partner with small businesses, providing them with tools once deemed the province of giants. This partnership promises to elevate the small business landscape, ushering in an era of democratized technology where every entrepreneur can harness the power of AI to write their own David vs. Goliath success story. AI, the once-distant dream, is now the most loyal wingman a small business can enlist in its quest for growth and innovation.

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Apple’s October Scary Fast Event: Everything revealed about the new MacBook Pro, iMac and M3 chips



It’s time for another Apple event, with a spooky twist. The company announced a surprise “Scary Fast” event last week, prompting the rumor mill to speculate that Apple would be revealing new chips to power a new lineup of Macs.

As our resident Apple expert Brian Heater wrote, a new 24-inch iMac and a MacBook Pro refresh would be the most likely new announcements to expect from the October event, and as it turns out, he was spot-on. Apple’s new M3 chip lineup was the focal point of the event, powering each of the devices Apple showcased in their half-hour prerecorded event that had some fog, some bats and ominous choir music…but no big surprises for those following the rumor mill.

Since the event kicked off off at the uncharacteristically late time of 8pm ET / 5pm PT, so you might have missed out out on the reveals while putting the finishing touches on your Halloween decorating, or watching Monday Night Football. No judgement, we’re here to recap everything the October Apple event showcased in one spot.

New M3 chips

Credit: Apple

The “scary fast” part of the Apple event, as expected, are the new M3 chips. Apple has announce a M3, M3 Pro and M3 Max, which will be included in Apple’s new 24-inch iMac, MacBook Pros.

This time around, Apple has placed an emphasis on graphical horsepower, with hardware-accelerated ray tracing, mesh shading and Dynamic Caching, which Apple claims “dramatically increases the average utilization of the GPU” by allotting exact amount of local memory to given tasks. These new chips were frequently benchmarked against their M1 predecessor, with Apple claiming the M3 renders at 2.5x the speed of the M1 and its CPU is 30% faster than the M1.

Check out the full rundown of the three M3 chips right here.

New MacBook Pro models

Apple MacBook Pro 2023 Update in Space Black Color

Credit: Apple

Yes, the new 14-inch and 16-inch MacBook Pros come with upgraded internals, but the first thing you might notice is the new color: Space Black. Beneath that color, you’ll find that new line of M3 chips. The 14-inch MacBook Pro can contain any of the trio, while the 16-inch model will only come with the M3 Pro or M3 Max chips.

As we’ve noted, the M3 chips packed into both models are putting an emphasis on getting the most out of the new GPU, though Apple also boasts that both form factors’ battery can last 22 hours on a single charge.

Both are available for preorder tonight, with the 14-inch MacBook Pro starting at $1,599 and going to $1,999 with the M3 Pro. The baseline 16-inch MacBook Pro goes for $2,499 and the pricing for the M3 Max chip upgrade for both models has yet to be disclosed.

And that space black color is exciting news for any Mac fan still pining for the 2006 MacBook, whose dark tone hadn’t been replicated in the MacBook iterations that followed, even those Midnight MacBook Airs.

Check out the full rundown on the new MacBook Pros here.

New M3 iMac

2023 M3 iMac Spec Rundown

Credit: Apple

Apple’s iMac line is getting a colorful refresh, with an added M3 chip to add horsepower to the palette change. Apple is sticking with the 24-inch form factor, and upgrading the screen with a 4.5K retina display, 1080p FaceTime camera and a six-speaker system supporting Dolby Atmos and Spatial Audio. The new iMac will be available for preorder with green, yellow, orange, pink, purple, blue and silver options starting tonight.

The $1,299 baseline comes with a 8-core GPU and 8-core CPU, with a $1,499 version upgrading you to a 256 SSD.

For more info about what else is new in the M3 iMac, head here.

An sneaky iPhone showcase

You may not have noticed it, but at the very end of the event, Apple dropped a quick note on the stream: “This event was shot on iPhone and edited on Mac.” It’s a bit of a victory lap, but as our other Apple expert Darrell Etherington notes, it’s a pretty impressive flex for Apple to shoot its half-hour hardware showcase entirely on a phone.

Recap the full Scary Fast event

If you want to just dive right in and experience the October event all over again or for the first time, you can catch the entire archive via the YouTube embed below right on Apple’s website.

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