AI News, 6 Skills That Won't Be Replaced by Artificial Intelligence artificial intelligence
Dolce & Gabbana apologize for racist ad, just in time for the holidays
When Facebook and Twitter were born, a new era of social media was ushered in, opening the gates for new areas of expertise that hadn’t existed before.
And the truth is that it won’t be in our lifetime that AI can quite process the exact same way a human brain does, even with the advent of quantum computing, so let’s focus on AI’s weaknesses and where marketers can perform where artificial intelligence cannot.
CaliberMind augments B2B sales, Stackla hunts down user-generated content that matches your brand efforts, Nudge analyzes deal risk and measures user account health, and Market Brew digs up tons of data for your SEO strategy.
Together, you can automate, do segmentation and automation, beef up your analytics, but no machine can replicate your innate interest in your customers, your compassion, and your ability to understand human emotions and predict outcomes effectively (because you have a lot more practice at being a human than the lil’
A data scientist friend of mine recently pointed out that if you flip a coin five times and it happens to land on tails every time, AI would analyze that data and predict with 100% certainty that the sixth flip will be tails, but you and I have life experience and know better.
You don’t have to go back to school for data science, but if you can’t read the basic reports that these endless AI tools can create, you’re already behind.
You’ve already refined your skills in creating appealing content, and you already know that it costs less than many traditional lead generating efforts and spending on content is way up.
Because content is what feeds all of these new smart devices, feeding your brand content effectively and utilizing AI tools to augment your efforts will keep you more relevant than ever.
Get ahead of privacy problems Consumers now understand what website cookies are, and know when they’ve opted in (or opted out) of an email newsletter, but to this point, humans have made the decisions of how these data choices are made.
Making sure that you know the ToS of any tool you’re using to mine data is critical so that you don’t put the company in a bad position by violating basic human trust.
The takeaway You’re smart, so you already knew that the robots aren’t taking your job, rather augmenting it, but adding AI into your marketing mix to stay ahead comes with risk and a learning curve.
7 Job Skills Of The Future (That AIs And Robots Can't Do Better Than Humans)
When I talk to people about AI (artificial intelligence) and the 4th industrial revolution, I often get asked what skills we should develop to prepare ourselves.
Jobs AI can do better than humans While AI is making exponential advances year after year, the popular media often like to exaggerate what it is capable of for the sake of eye-catching headlines and anxiety-inducing news soundbites.
Stanford professor Andrew Ng, writing for the Harvard Business Review, has a good rule of thumb for determining which types of jobs are ripe for automation: “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future,” he writes.
So, while there is great potential here to automate the sorts of tasks that require this Input A to Output B kind of model — including scanning security video for suspicious behavior, alerting drivers to pedestrians in the road, tagging hateful or abusive comments online, and so on — using AI to automate these tasks also requires a great deal of investment and work upfront.
Jobs currently held by humans that require this same sort of Input A to Output B scenario are likely to be outsourced to computers, including jobs like receptionists, telemarketers, bookkeeping clerks, proofreaders, delivery couriers, and even retail salespeople.
Where machines could replace humans—and where they can’t (yet)
The technical potential for automation differs dramatically across sectors and activities.
As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern.
Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.
Last year, we showed that currently demonstrated technologies could automate 45 percent of the activities people are paid to perform and that about 60 percent of all occupations could see 30 percent or more of their constituent activities automated, again with technologies available today.
In this article, we examine the technical feasibility, using currently demonstrated technologies, of automating three groups of occupational activities: those that are highly susceptible, less susceptible, and least susceptible to automation.
Toward the end of this article, we discuss how evolving technologies, such as natural-language generation, could change the outlook, as well as some implications for senior executives who lead increasingly automated enterprises.
In discussing automation, we refer to the potential that a given activity could be automated by adopting currently demonstrated technologies, that is to say, whether or not the automation of that activity is technically feasible.2 2.We define “currently demonstrated technologies”
Occupations in retailing, for example, involve activities such as collecting or processing data, interacting with customers, and setting up merchandise displays (which we classify as physical movement in a predictable environment).
The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it.
For example, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent and the cost of the groceries consumers bought by 1.4 percent.3 3.Emek Basker, “Change at the checkout: Tracing the impact of a process innovation,”
Almost one-fifth of the time spent in US workplaces involves performing physical activities or operating machinery in a predictable environment: workers carry out specific actions in well-known settings where changes are relatively easy to anticipate.
Through the adaptation and adoption of currently available technologies, we estimate the technical feasibility of automating such activities at 78 percent, the highest of our seven top-level categories (Exhibit 2).
Since predictable physical activities figure prominently in sectors such as manufacturing, food service and accommodations, and retailing, these are the most susceptible to automation based on technical considerations alone.
Within manufacturing, 90 percent of what welders, cutters, solderers, and brazers do, for example, has the technical potential for automation, but for customer-service representatives that feasibility is below 30 percent.
A service sector occupies the top spot: accommodations and food service, where almost half of all labor time involves predictable physical activities and the operation of machinery—including preparing, cooking, or serving food;
We calculate that 47 percent of a retail salesperson’s activities have the technical potential to be automated—far less than the 86 percent possible for the sector’s bookkeepers, accountants, and auditing clerks.
The heat map in Exhibit 3 highlights the wide variation in how automation could play out, both in individual sectors and for different types of activities within them.4 4.For a deeper look across all sectors in the US economy, please see the data representations from McKinsey on automation and US jobs, on public.tableau.com.
Long ago, many companies automated activities such as administering procurement, processing payrolls, calculating material-resource needs, generating invoices, and using bar codes to track flows of materials.
Examples include operating a crane on a construction site, providing medical care as a first responder, collecting trash in public areas, setting up classroom materials and equipment, and making beds in hotel rooms.
Already, some activities in less predictable settings in farming and construction (such as evaluating the quality of crops, measuring materials, or translating blueprints into work requirements) are more susceptible to automation.
The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent).
For now, computers do an excellent job with very well-defined activities, such as optimizing trucking routes, but humans still need to determine the proper goals, interpret results, or provide commonsense checks for solutions.
Overall, healthcare has a technical potential for automation of about 36 percent, but the potential is lower for health professionals whose daily activities require expertise and direct contact with patients.
One of the biggest technological breakthroughs would come if machines were to develop an understanding of natural language on par with median human performance—that is, if computers gained the ability to recognize the concepts in everyday communication between people.
The actual level will reflect the interplay of the technical potential, the benefits and costs (or the business case), the supply-and-demand dynamics of labor, and various regulatory and social factors related to acceptability.
E-commerce players, for example, compete with traditional retailers by using both physical automation (such as robots in warehouses) and the automation of knowledge work (including algorithms that alert shoppers to items they may want to buy).
The greater challenges are the workforce and organizational changes that leaders will have to put in place as automation upends entire business processes, as well as the culture of organizations, which must learn to view automation as a reliable productivity lever.
Understanding the activities that are most susceptible to automation from a technical perspective could provide a unique opportunity to rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects.6 6.See Aaron De Smet, Susan Lund, and William Schaninger, “Organizing for the future,”
McKinsey Quarterly, January 2016., It could also inspire top managers to think about how many of their own activities could be better and more efficiently executed by machines, freeing up executive time to focus on the core competencies that no robot or algorithm can replace—as yet.
Reshaping Business With Artificial Intelligence
Perhaps the most telling difference among the four maturity clusters is in their understanding of the critical interdependence between data and AI algorithms.
Compared to Passives, Pioneers are 12 times more likely to understand the process for training algorithms, 10 times more likely to understand the development costs of AI-based products and services, and 8 times more likely to understand the data that’s needed for training AI algorithms.
Most organizations represented in the survey have little understanding of the need to train AI algorithms on their data so they can recognize the sort of problem patterns that Airbus’s AI application revealed.
Citrine Informatics, a materials-aware AI platform helping to accelerate product development, uses data from both published experiments (which are biased toward successful experiments) and unpublished experiments (which include failed experiments) through a large network of relationships with research institutions.
knowing what does not work helps it to know where to explore next.3 Sophisticated algorithms can sometimes overcome limited data if its quality is high, but bad data is simply paralyzing.
Other data is fragmented across data sources, requiring consolidation and agreements with multiple other organizations in order to get more complete information for training AI systems.
The need to train AI algorithms with appropriate data has wide-ranging implications for the traditional make-versus-buy decision that companies typically face with new technology investments.
Training AI algorithms involves a variety of skills, including understanding how to build algorithms, how to collect and integrate the relevant data for training purposes, and how to supervise the training of the algorithm.
The chief information officer of a large pharma company describes the products and services that AI vendors provide as “very young children.” The AI tech suppliers “require us to give them tons of information to allow them to learn,” he says, reflecting his frustration.
We believe the juice is not worth the squeeze.” To be sure, for some support functions, such as IT management and payroll support, companies might choose to outsource the entire process (and pass along all of their data).
Even if companies expect to rely largely on external support, they need their own people who know how to structure the problem, handle the data, and stay aware of evolving opportunities.
“Five years ago, we would have leveraged labor arbitrage arrangements with large outsourcers to access lower cost human labor to do that work,” the pharma company CIO says.
Eric Horvitz, director of Microsoft Research, argues that the tech sector is quickly catching up with the new model of offering technology tools to use with proprietary data, or “providing industry with toolsets, computation, and storage that helps to democratize AI.” Many AI algorithms and tools are already in the public domain, including Google’s TensorFlow, GitHub, and application programming interfaces from tech vendors.
The data issues can be pronounced in heavily regulated industries such as insurance, which is shifting from a historic model based on risk pooling toward an approach that incorporates elements that predict specific risks.
But we use machine learning algorithms to assess the model’s non-linear construction, variables and features entered, and as a benchmark for how well the traditional model performs.” As technology races ahead of consumer expectations and preferences, companies and the public sector tread an increasingly thin line between their AI initiatives, privacy protections, and customer service.
Likewise, a technology vendor offers an AI-based service to help call center operators recognize when customers are getting frustrated, using real-time sentiment analysis of voice data.
- On 22. januar 2021
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