AI News, Beyond the buzzword: What “artificial intelligence” means for ... artificial intelligence
IBM Unveils Its Vision For The Future Of Artificial Intelligence
In a nutshell, it asks what someone should do if faced with the option of pulling a lever so that a trolley avoids killing five people laying on a track, but kills another person in the process.
Beyond the buzzword: What “artificial intelligence” means for marketing leaders, right now
(Though, machine learning can also be semi-supervised.) When a person supervises machine learning, a machine learning algorithm is given a teaching set of data to begin (input) and the possible outcomes (output).
The person supervising the machine learning can double check to make sure the algorithm is learning appropriately with each new product image.
When the algorithm is shown a new image of a running shoe, it can predict that that product image belongs to that merchandise category based on its training.
If the algorithm is given the sales history for your customer base, it can explore the data to find patterns and similarities among buyers.
In both supervised and unsupervised machine learning, the algorithm is able to analyze historical data at a rate far surpassing human ability.
Deep learning occurs when an algorithm is able to process larger datasets and solve more complex issues because it teaches itself the rules.
In one deep learning technique, called clustering, rules are established through a series of questions (called “neural networks”).
Your shoppers are being conditioned to communicate with Siri or Alexa as a tool, without necessarily understanding that these machines filter speech using an application of artificial intelligence called Natural Language Processing.
Once she understands, only then can she search for the answer online, translate it into text, and decide what pitch, tone, and pacing to use when she recalls the information through her own artificial speech.
Companies are accumulating thousands of records from each customer touch point: What customers search for, how long they hover over a product without purchasing, whether they search on a tablet or a mobile phone, etc.
These large sets of data ― commonly referred to as “big data” ― provide precise information about what matters in your business.
The company also implemented a new sales platform, integrating transaction data with the lifestyle data they were gathering from their apps.
Both information sources combined to create a “single-view of the customer.” Early in 2016, IBM Watson and Under Armour announced a partnership that would make the massive amounts of data Under Armour was gathering actionable.
IBM Watson is a technology that leverages multiple applications of artificial intelligence: natural language processing, visual recognition, machine and deep learning, and human-computer interaction.
In the case of Under Armour, IBM Watson is able to analyze lifestyle data in combination with sales data and the latest academic research to provide customized health and wellness coaching to users.
IBM Watson compares a user’s nutrition, training and sleep information with other members of the community of the same gender, age and activity level.
The comparison allows the machine to provide recommendations on nutrition, fitness regimens, sleep quality and length, and even on how the weather forecast will affect a workout.
For example, a man who only sleeps five or six hours a night could receive feedback telling him that other men in his age category and at his fitness level who increase their sleep to seven or eight hours, have a lower body mass index.
Before you begin lobbying for artificial intelligence at your organization, it’s important to understand that a major investment in artificial intelligence platforms does not make sense for every business scenario, nor every business.
As advances in AI make prediction cheaper, economic theory dictates that we’ll use prediction more frequently and widely, and the value of complements to prediction – like human judgment – will rise.
If you’re considering an AI tool, you should ask yourself these three questions: Artificial intelligence technology can lead to reduced labor costs, optimized production and operations, more efficient timing and delivery, more precise customer personas and journey information, and more.
For instance, if your team doesn’t understand the risks and limitations of AI, they may feed the machine bad data, which would skew all of its results.
I think of AI not as artificial intelligence but instead “augmented” intelligence such that the software and technology augments human intelligence and decision-making skills.
The simplest way to think about how artificial intelligence platforms can augment your marketing strategy is to break it down by marketing priority.
We are going to look at how AI might fit into the following four marketing priorities: The most important thing to keep in mind as you read through this section is that tools, even artificially intelligent tools, are not magic.
If creating a delightful customer experience is an important priority for your business, artificial intelligence tools that facilitate customer service may be an option.
These bots leverage natural language processing and generation and machine learning to be able to respond to a customer’s unique enquiries.
It can answer frequently asked questions like “What is your return policy?” or “Where are your stores located?” Or even questions about an item or purchase details.
Content creation platforms, like Wordsmith or Quill, produce template pieces, like financial reports, sports updates, local happenings, and business news.
Tools like this will, theoretically, free up journalists and content creators to do more substantial reporting, rather than spending time creating formulaic content.
In fact, with the right subject matter, it can be hard to spot the difference between content created by a human and that created by an algorithm.
smart content consists of content suggestions based on customer preferences and messages that adapt slightly to the user.
In a product recommendation tool, machine learning algorithms sort through product images, and compare them to a customer’s purchase history in order to recommend a new product.
A machine learning algorithm could analyze your data to find out who bought running shoes, when they bought them, what promotions they’ve responded to in the past, and more.
A machine learning algorithm could analyze their customer information (including demographic and behavioral data), customer purchase history, and their inventory database to find patterns to help improve marketing and promotions.
As experimentation becomes the status quo at leading organizations, business leaders must also consider how artificial intelligence can support optimization and experimentation efforts.
Mike argues that to have a successful experimentation program, you should be running tests to achieve lift and running tests that are purely exploratory.
One 10% win without insights may turn heads your direction now, but a test that delivers insights can turn into five 10% wins down the line.
For example, tools like Sentient Ascend enable marketers to try more ideas within the same timeframe as an A/B test, leveraging genetic algorithms.
Leveraging 8 years worth of landing page data and machine learning algorithms, the tool analyzes your landing page and provides recommendations as to how you could improve your conversion rate.
human element is still required in order to ‘define the rules’ by providing inputs on hypotheses, consumer insights, consumer behavior, human psychology, etc.―all of which are crucial to optimization.
Based on those inputs, these tools then allow faster discovery of the positive combinations, as well as uncover in-depth trends and patterns within segments and cohorts that previously would’ve taken a huge amount of human data analysis to uncover.” –
The human element is still required in order to ‘define the rules’ by providing inputs on hypotheses, consumer insights, consumer behavior, human psychology, etc.―all of which are crucial to optimization.
It’s true that deep learning algorithms can learn with every new layer of information processed by a system, gaining insights at an exponential rate.
They did make strides towards getting the chatbots to compromise — a key tactic of negotiation — through further testing, though.
Within the first 24 hours of its release, people coordinated to feed the tool biased messages, essentially training Tay to accept racist and sexist statements as a “rule” of conversation.
But even in situations where artificial intelligence tools do not experience a coordinated attack like Tay, the algorithms are likely to incorporate the bias of their creators and the input data.
Joshua Gans, co-author of Prediction Machines and Professor of Strategic Management at Rutman at the University of Toronto encourages business leaders to “understand these potential biases and be aware of them when making AI adoption decisions.” It is important to think critically about your dataset(s)―the information that the algorithm’s findings will be based upon.
Having a “data-driven”, “test-and-learn” culture means you are willing to research and test possible solutions, and then measure effectiveness once you’ve implemented the tool.
I’d like to invite anyone with feedback, resource recommendations, follow-up questions, or comments to leave your thoughts in the comments section below.
- On Sunday, August 18, 2019
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