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How Does Scaled Data Labelling Work With Marketing?

Data labeling turns raw data into useful information that can then be utilized for optimized marketing. However, turning raw data into labeled data takes time. When done internally by humans, this can be costly and inefficient.

The good news is data labeling is scalable when you work with the right data labeling software. You can decrease overall costs while improving efficiency and machine learning processes with the right platform on your side.

Of course, how you use this labeled data is just as important as how it’s done. That’s why this article will delve into the many uses of data labeling for marketing and some platforms you should consider using to get the most out of your data.

 

What Is Data Labeling?

Data labeling is the process of analyzing raw data and labeling it to provide context to machine learning software, algorithms, and end-users. For example, analyzing a photograph and labeling it with the contents of the image.

There are different types of data labeling, including:

• image
• video
• audio
• text

Data labeling is used in various industries, including healthcare, finance, research, automotive, and technology. With the growth of artificial intelligence (AI) technology, data labeling has also become integral to marketing.

 

How Does Data Labeling Work?

To better understand data labeling, you should know it’s a continuing process between humans and machine algorithms. The task is usually completed by a combination of software, processes, and people. The raw data is analyzed and then labeled based on context. The context is determined by the company’s or industry’s needs.

The context is then used to train and develop machine learning algorithms. The initial labeling allows human analysts to select the appropriate variables for the machine learning algorithm to use for future predictions. The more context you provide, the better the AI can learn. This will help to deliver better algorithm results in the future.

Speaking of the algorithm, this is an ambiguous term that refers to any self-defined set of instructions built to solve a problem. The options for an algorithm are seemingly endless, though needs will vary by industry. A few examples of common algorithms include search query results and sort and filter features on an e-commerce website.

 

5 Uses for Data Labeling in Marketing

Data labeling is common in industries that rely heavily on top-of-the-line technology, such as healthcare and finance. As machine learning becomes a more common tool for marketers, though, the need for data labeling will increase. Here are five uses for data labeling in marketing that you may not have considered before.

 

1. Increase Personalization

According to Statista, 90 percent of consumers find marketing personalization very or somewhat appealing. As such, marketers need to personalize the experience from start to finish. Marketing campaigns, social posts, ad copy, and website flow are all included in that process.

Personalization is a way of tailoring a service or product to fit a market demographic. It’s possible to use data labeling to achieve this.

For example, an algorithm can be built to categorize creative assets for various segments of your target audience. An example of this would be utilizing facial recognition on images or video assets so you can deliver that asset to an audience who identifies with those characteristics.

You can also use personalization to improve search query results, provide personalized product recommendations, and enhance product attributes for improved categorization.

 

2. Optimize Prices

There are numerous data sources to consider for analysis. To get ahead of the competition, consider analyzing competitor creatives in campaigns and advertisements.

With the help of data labeling, you can train specialized software to recognize, analyze, and categorize competitor raw data. For price optimization, this will largely be text and images. With this data in hand, you can make price decisions based on data-backed industry trends.

For example, learn when your competitors run a campaign on social media to keep track of seasonal trends. You can even create an algorithm to analyze competitors’ digital ad flyers daily or weekly. While you may not always use this data for immediate decisions, you can always use it to inform future campaigns.

 

3. Analyze Customer Reviews

With more consumers purchasing online than ever before, customer reviews have never been more important. In fact, 93 percent of consumers say their purchase decisions were largely influenced by online reviews.

With scaled data labeling, you can train your algorithm to analyze customer review sentiments. That’s right, data labeling can do more than just categorize data based on keywords. With the right data model, you can teach it to connect the dots and give sentiment scores to each review you receive.

According to ReviewTrackers, 94 percent of consumers say a bad review has convinced them to avoid a business. With a sentiment analysis algorithm, you can find out about and respond to negative reviews almost immediately. This can help to ease consumer concerns.

 

4. Categorize User-Generated Content

Is it really any surprise that consumers trust other consumers more than the brands they buy? That’s why 51 percent of consumers trust user images over brand creatives, according to a survey by Olapic and Cite Research.

How can brands use this to their advantage? By relying on user-generated content (UGC). User-generated content can be used on your website, in email campaigns, and on social media.

By utilizing a data labeling service, your algorithm can effectively categorize this content for use. This means determining which campaigns, social media platforms, and website page the different types of content belong to.

 

For example, an image showing a home brand’s coffee table styled in a customer’s living room should end up on a related category page, like tables or living room decor. The algorithm can even be trained to identify which product exactly matches the one in the image so it can be used on the product display page.

 

5. Enhance Product Offerings and Availability

You can’t market what you don’t have, which is why product assortment and availability are so important to effective marketing campaigns. How can you fill assortment gaps and ensure availability for specific campaigns? With a properly trained machine learning algorithm, of course.

Using data labeling and annotation services, you can determine gaps in your own offering and availability based on an analysis of competitor assortment. With an algorithm developed to crawl and scrape data from competitors, you can save time while also increasing your potential for profit.

 

5 Data Labeling Services

1. Datasaur

If you’re looking for software specializing in data labeling, then consider Datasaur. With dozens of clients across many industries, Datasaur has the expertise you need.

A few key features to consider are automated intelligence, workflow management, and data privacy. With automated intelligence and workflow management, Datasaur handles data analysis and categorization from end to end. It does so efficiently by quickly culling any data that requires more advanced (i.e., human) analysis and working through the rest.

While Datasaur doesn’t list prices on their website, they do have three plans to choose from:

• Free: up to 5,000 labels per month
• Growth: up to 100,000 labels per month
• Enterprise: unlimited labels

With a free plan to start, you can test out the software while also knowing that scaling is possible.

 

2. Isahit

With brands like L’Oreal and Sodexo as clients, Isahit has certainly made a name for itself in the data labeling industry. Built on a platform of agility, transparency, and scalability, Isahit offers in-depth data analysis and labeling services for businesses of all kinds.

As a French-based company, Isahit prices are in Euros. Isahit offers four plans, including:

• Basic: €185 per month
• Priority: €255 per month
• Gold: €331.67 per month
• Residency: €630 per month

Isahit also provides a custom plan option to meet your company’s unique needs.

 

3. Datax

The manual work required for building an effective AI model can be more than most companies can handle internally. Datax handles the entire process including data labeling, AI model customization and validation, and business process automation.

Datax doesn’t include pricing on their website. You can fill out their contact form to receive a customized quote.

 

4. Dataloop

With services like an annotation platform, data management, and production pipelines, Dataloop is an end-to-end solution for numerous industries including e-commerce.

When it comes to data labeling, Dataloop offers a suite of data annotation tools to help you produce highly accurate datasets. A combination of AI model and human analysis, these tools include:

• automatic annotation
• video annotation
• workforce management
• data QA and verification
• integrated labeling services.

Dataloop doesn’t have prices or plans on its website. You can request a demo to learn more about the software and speak with a sales representative.

 

5. Labelbox

With many data labeling platforms to choose from, it can be hard to find the best one for your needs. Labelbox is one such platform, though its list of clients and even its own Academic Program makes it stand out.

Labelbox offers services to various industries, including government, retail, insurance, and e-commerce. Its services include annotation, diagnostics, and prioritization.

The software platform has three plans:

• Free: Access to the full editor suite, rate-limited API access, and up to 10,000 annotations per month.
• Pro: Everything is Free plus unlimited API access, unlimited team access, and customer support.
• Enterprise: Everything in Pro plus model-assisted labeling, advanced queue customization, and premium customer support.

 

Frequently Asked Questions About Data Labeling

 

Data Labeling Conclusion

As your business grows, so too do its needs. If your marketing efforts utilize machine learning in any way, then the time spent to build, train, and maintain this AI will increase, too. That is why marketers should consider investing in data labeling software from the beginning.

Data labeling software like the options listed above enables marketers to continue to grow their datasets to feed to the machine learning algorithm.

This ensures a smarter algorithm that requires less human input over time. Just a few ways marketers can then use this machine learning include increased personalization, optimized pricing, and enhanced product offerings and availability.

Which use for data labeling in marketing would you like to implement first?

Source: feedproxy.google.com

9 AI Tools For Media Creation

 

Artificial intelligence (AI) tools for media let you create content at scale in a previously impossible way and you don’t even need design experience to create something impressive.

Disclosure: This content is reader-supported, which means if you click on some of our links that we may earn a commission.

AI isn’t new.

The film and gaming industries both use it regularly, for instance.

But, these sectors have only scratched the surface of AI capabilities.

On a seemingly daily basis, new AI tools emerge to help people create articles, video scripts, logos, and more.

Naturally, these tools have investors excited.

However, they’ve plenty to offer to content creators, marketers, and digital agencies too.

Let’s look at how some of these AI tools for media can transform the way you work.

 

1. Podcastle

Podcastle, which has raised more than $1 million, is an extension for the Google Chrome browser. This free tool lets you create custom podcasts with just a few clicks.

With Podcastle, you can:

• quickly transcribe your podcasts
• create and edit content with the text editors
• turn text into podcasts
• revoice content
• record live interviews
• make autosave
• access unlimited content publishing and projects

 

Getting Started with Podcastle

Set up a Podcastle account by clicking on the green “create” link in the top right-hand corner.

Depending on what you want to do next, visit the tutorials page for step-by-step instructions.

 

Podcastle Cost

Podcastle’s free service gives you access to:

• 45 minutes of one-time audio recordings
• four hours of monthly audio recordings
• capacity for one host and two interviewees
• magic audio processing

 

And more.

There’s also a “storyteller” package for $11.99 a month. The storyteller has all the above features, plus:

• additional recordings and interviewing capacity10 hours of transcription every month
• high-quality downloads and revoicing
• unlimited magic audio processing

 

2. Let’s Enhance

Let’s Enhance takes any photo you upload and analyzes the photo quality, color, contrast, etc. Then, if the picture needs enhancement, it automatically color corrects and retouches the image with minimum input.

Other features include:

• batch processing
• custom sizing
• resolution increasing
• resizing and upscaling

 

This program isn’t just for personal use.

Let’s Enhance is also recommended for real estate and e-commerce, where you need clear, high-quality images.

 

Getting Started With Let’s Enhance

Upload an image to Let’s Enhance, and the algorithm enhances it as much as possible without compromising quality.

The algorithm provides a slider for tweaking parameters like contrast, hue, brightness, and saturation.

You can also apply additional filters.

 

Let’s Enhance Cost

Let’s Enhance has a subscription, business, and pay-as-you-go options.

Subscriptions for personal use run from $0 for a one-time, five-credit trial to $34 for 500 image credits per month.

Businesses can pay from $72 for 1,000 credits per month to $290 for 5,000 credits per month, plus get API access.

Pay as you go options range from $9 to $39 and let you use the program only when you need it.

 

3. QuillBot

QuillBot is one of the few quality paraphrasing tools on the market.

This comes in handy when you want to avoid being repetitive when writing long pieces.

It may also help you rework product descriptions.

The program has two main modes: one with synonyms and one without.

The first preserves the original sentence’s meaning while only changing certain words.

The second mode alters the word order so the sentence sounds more natural.

Other features include:

• grammar checker
• summarized
• citation generator

 

Getting Started With QuillBot

Upload or paste a document into QuillBot, then hit the “Paraphrase” button.

There’s nothing more to it! (However, be sure to proofread, just to be safe.)

 

QuillBot Cost

The free version gives users a 700 character paraphraser and a 5,000 character summarization limit. Free users also get:

• three synonym options
• three writing modes
• Chrome and doc extensions

 

Premium gives a 25,000 Summarizer and 10,000 Paraphraser character limit and processes 15 sentences simultaneously, as well as:

• four synonym options
• seven writing modes
• a wider range of tones
• sentence shortener and expander

Premium costs $14.95 for month-to-month use, $9.95 a month if paid semi-annually, or $6.67 per month for the annual option.

 

4. KinetiX

Investors believe in Europe-based KinetiX, which recently secured $608k in capital funding.

It turns your videos into 3D animations with quick editing options and filters.

Other features include:

• customization and collaboration
• prototyping
• 3D visualization
• access to characters and animations
• automated workflow

 

Getting Started With KinetiX

KinetiX uses a drag-and-drop interface available on the front page.

Just drop in any .fbx file or paste in a YouTube URL and click the “generate” button underneath.

You can upload a selection of video content, like your own recordings, MP4, and YouTube content.

The tool then creates an animated character in seconds.

 

KinetiX Cost

The KinetiX tech AI tool is free for personal use.

However, if you want to use it for commercial purposes, fees begin at 15€ per month.

 

5. Articoolo

Articoolo is an AI writing assistant that generates content in a fraction of the time it would take to write it yourself.

Additionally, it has a

• quote and image finder
• title generator
• article summarizer
• writer’s helper

 

Most marketers and business owners need content in a hurry at some point, or you may spend time looking for that perfect quote or image.

This AI app can do that quickly.

It’s also great for anyone that wants a starting point with title ideas or to rewrite large volumes of content for repurposing.

 

Getting Started With Articoolo

To start using the tool, enter your topic and then wait for Articoolo to get to work.

That’s all you need to do!

 

Articoolo Cost

Pay per use ranges from $19 for ten articles or $99 for 100 articles.

Subscribers pay $29 a month for 30 pieces and up to $99 a month for 250 articles.

 

6. Word.ai

Word.ai helps copywriters rework content with a natural-sounding voice.

It creates up to 1,000 rewrites of an article, giving users plenty of variety if they want to use the same piece of content in multiple ways.

It can also work as an editing tool.

Other features include:

• sentence restructuring
• split sentences
• quality enhancement
• clarity enhancement

 

Getting Started With Word.ai

1. Click the green button in the top right-hand corner of the screen.
2. The button will take you to the subscription deals.
3. Click the “start my free trial” button for your preferred option.
4. Then Word.ai asks you to sign up and create a password.

 

Word.ai Cost

New users get a free three-day trial. After that, you can pay $57 per month or an annual fee that breaks down to $27 per month (quite the deal in comparison!).

For heavy use, they offer an enterprise-level at various prices.

 

7. Synthesia

If you need to create video content in a hurry, Synthesia could be the tool.

Each video can be up to thirty minutes long, and you can upload any custom backgrounds and your own avatar.

Features include:

• 50 available languages
• PowerPoint capabilities
• synthetic or authentic voices
• background music

 

Aside from marketing videos or product demos, you could use it for:

• training
• personal videos
• video chatbots

 

Getting Started With Synthesia

Click “create account” in the top right-hand corner to create a free demo video and understand Synthesia better.

To start creating your content, enter your text into the browser.

Your video will be ready in minutes.

 

Synthesia Cost

Personal pricing is $30 a month for video credits.

Custom pricing is available too, but you must speak to the sales team.

 

8. Rephrase.ai

Rephrase.ai is a powerful video creation tool that enables people to quickly create and share quality videos in a matter of minutes.

You can add text, images, animations, and video clips within the easy-to-use interface.

It has wide use of applications, including educational and explainer videos, personalized customer touchpoints, digital marketing, and outreach.

To understand how the video creator works, they offer a handy tutorial.

 

Getting Started With Rephrase.ai

1. Begin by signing up and selecting the category you’re interested in.
2. Answer a series of questions about how and why you plan to use the program.
3. Watch a short instructional video, then accept terms and conditions.

 

Rephrase.ai Cost

Plans begin at $25 for ten banner credits a month.

Each credit equals one minute of video. Rephrase.ai also has an enterprise option—contact them for details.

 

9.Designs.ai

Designs.ai incorporates several AI tools for media creation, including a logo and banner maker and a mock-up generator.

You create designs by dragging shapes on the canvas, which you then adjust with your mouse or keyboard input.

You also have the option of changing variables for fonts, colors, and backgrounds.

Features include:

• cloud storage
• massive image library
• speechmaker
• assistive tools
• unlimited usage and projects
• branding kit

 

Getting Started With Designs.ai

Click “Try for free” in the upper right-hand corner, choose which project you want to try out, and follow the prompts to get started.

 

Designs.ai Cost

Design.ai’s basic tier is $29 per month, and its pro-level is currently $69 per month.

If you pay annually, you save 34 percent.

For large-scale work, contact them about an enterprise contract.

 

Frequently Asked Questions About AI Tools for Media Creation

 

 

 

 

AI Tools for Media Creation Conclusion

For a long time, artificial intelligence has helped us perform everyday tasks more quickly and efficiently.

Now, AI became more intelligent, creating media like articles and videos.

With these tools, a marketer can create quality content with ease, even if they don’t have expertise in making videos, creating podcasts, or graphic design.

Even for the experienced, AI tools for media have multiple advantages, saving people time and freeing up their resources for other tasks.

Tools like Rephrase.Ai, Word.AI, and KinetiX are changing how we create content and make it

Source: neilpatel.com

How to Use AI to Bolster Your Customer Journey

 

Wouldn’t it be awesome if you could skyrocket your lead-to-sales conversions to new heights?

Most business owners struggle with this.

Data shows that a whopping 79% of leads never convert to sales.

This highlights how challenging it can be to move leads through the customer funnel journey.

The good news is, Artificial Intelligence (AI) can supercharge your customer funnel journey to help you get more sales.

AI will efficiently weed out your low-quality leads and eliminate repetitive, time-consuming tasks, allowing you to focus on higher-quality and converting ones seamlessly.

AI will help your brand grow by preventing your leads from falling through the cracks at each stage of the customer funnel, which increases your sales-ready prospects for conversion.

Read on to learn more about how you can use AI to bolster your customer funnel journey.

 

Customer Funnel Journey: What is it?

A customer funnel’s structure and components can vary depending on the company or industry, among other factors.

The standard customer journey funnel stages can include:

Awareness.

This stage focuses on raising awareness for your brand and sharing information about your value with your target audiences.

Interest.

Here, your leads learn more about your business, products, and services.

You can introduce your positioning and capture leads by encouraging signups to your email newsletters (among others).

Consideration.

In this step, engage your leads actively by delivering personalized messages that reinforce your brand’s benefits.

Provide specific information to overcome objections and qualify your leads.

Evaluation.

Your prospects start showing interest in your products and services at this phase, deciding whether to buy from you or other brands.

Highlight why prospects should choose you over your competitors to make them stick.

Conversion.

After your leads convert to customers, market your onboarding materials, deliver messages celebrating their decision, and assess satisfaction levels.

This stage is also a great time to run your upselling and cross-selling strategies.

The customer funnel can show you how your leads interact with your brand and where they fall off.

This helps you refine and improve your strategies to keep your leads moving through each stage until they ultimately become paying customers.

A well-defined customer funnel journey helps you optimize your lead capture, engagement, nurturing, qualifying, and converting efforts.

It also informs your strategies to help you create immersive and memorable experiences across devices, channels, platforms, etc. that make your leads stick and convince them to become loyal clients.

 

The importance of using AI in the customer funnel journey

It’s time-consuming and labor-intensive to establish an effective customer funnel journey.

However, AI can do the heavy lifting for your sales and marketing teams through automated methods, such as autonomously identifying more qualified leads quickly.

This reduces the strain on your resources and your teams avoid needlessly chasing less-qualified leads.

AI can serve as your company’s sales and marketing virtual assistant by handling repetitive and tedious tasks.

This frees up your teams so they can focus on more meaningful, higher-level, and revenue-generating work.

The technology can also reduce human error and work 24/7, optimizing your lead engagement, nurturing, and qualifying efforts.

Through machine learning technology, you can “train” your AI to respond to conversations and events the way you would.

Your AI can learn from lead interactions and data, and the more input it accumulates, the more it can optimize its functions and responses on its own.

 

Ways to leverage AI in each stage of the customer funnel journey

Below are several ways AI-based technology can boost your customer funnel journey.

Streamline your lead generation efforts

The customer funnel journey typically begins with raising brand awareness, and this is where a lot of manual work is done.

For instance, you’ll need to build and design campaigns to tailor them to potential customers who haven’t heard of your products and services before.

This includes creating content such as blog posts, articles, lead magnets (ebooks and whitepapers), and website content (among others) to raise brand awareness and capture leads.

However, the content creation process can take a huge amount of time and resources — unless you use an AI-powered platform that can automate your content creation process.

This is where Broca comes in handy.

Broca provides a deep learning AI platform that generates content for your marketing and sales needs in a flash, saving you from the resource-draining aspects of building content.

If you’re creating content for all your campaigns, Broca delivers by instantly generating the content, from ideation to distribution, for your website and marketing channels.

First, provide a campaign brief (in a few sentences) describing your campaign and where you want to direct your traffic, such as your blog post or landing page.

Image source: usebroca.com.

The platform then creates core messaging based on your campaign brief, giving you three key benefits of your brand, an enticing value proposition, and a killer headline.

You can validate and modify all these with your team before proceeding.

Image source: usebroca.com.

Finally, Broca creates promotional content out of your core messaging, providing you with brand-aligned and ready-to-distribute emails, ad copies, and social media posts.

Image source: usebroca.com.

The platform creates your marketing and sales content across channels, which helps ensure your content and messaging remain consistent throughout the customer funnel journey.

Broca also streamlines creating Google ads by analyzing your current ads and generating new suggestions, putting your ad channels on autopilot.

A robust AI-based content-generating platform is a game-changer in your content creation for lead generation.

It significantly cuts down on the time-intensive, tedious, and usually complex process of creating effective content by doing the heavy lifting aspects of the process.

With AI under your tool belt, you and your teams don’t break a sweat but still get high-quality, consistent content at a scale that generates significant leads.

 

Automate your lead nurturing and qualifying processes

To get your leads to interact with your brand and guide them to the next stage of the customer funnel journey, you’ll need to nurture and qualify them.

However, the lead nurturing and qualifying process can take up plenty of time and energy that your sales team can optimize better to implement strategies to close deals.

The solution?

Delegate your lead nurturing and qualifying initiatives to a reliable virtual sales assistant.

If you want to take things a step further, use an AI-powered sales assistant.

One such solution is Exceed.ai.

Exceed.ai is a powerful virtual sales assistant with conversational AI that works alongside your sales and marketing teams to nurture and qualify your leads.

It helps you move your leads along the customer funnel journey until they are sales-ready.

Exceed’s AI-driven platform uses a conversational bot and machine learning technology to provide your leads with highly personalized and contextual interactions.

It uses AI to deliver human-like responses and follow-ups via chat and email, automating your lead engagement, nurturing, and qualifying processes.

First, it engages your leads through your web or chat-like forms through personable interactions.

Here’s an example of Exceed’s AI-generated email, in this case, from their own email marketing operation. (It’s always a good sign when a company uses its own product and Exceed does all of its lead nurturing via their AI assistant.)

The virtual assistant then assists your leads by answering questions, dealing with objections, and responding to their requests while simultaneously gathering data for your Customer Relationship Management (CRM).

Next, it implements automated nurturing and uses intelligent follow-ups to move your leads through the customer journey funnel stages.

The VA acts as a virtual Sales Development Representative (SDR) by validating your leads based on the previous and current conversations and your Sequence or Playbook (more on this later).

Finally, after the VA qualifies your lead, it hands over your sales-ready prospects to your human sales representatives for closing.

It can automatically book a schedule for your leads based on your sales reps’ appointment availability that you and your team configure within the Exceed.ai platform.

The AI-based virtual assistant handles the entire process, from engaging your leads to handing them off to your sales reps.

This saves your team tons of time and energy while ensuring you generate, nurture, and qualify your leads effectively.

Another key Exceed.ai feature is Sequences.

Sequences serve as your playbooks or campaigns with pre-built, customizable chat and email templates.

Each sequence for every campaign includes guided templates for many use cases, such as getting your lead’s attention, engaging them with a product intro, and handover chats or emails.

Customize the template or build one from scratch to set the automated responses your Virtual Assistant sends out for every sequence in your campaign.

All these features streamline your lead interactions as they move along the customer funnel journey.

With AI-led lead nurturing, your sales and marketing teams spend minimum effort and time on your lead nurturing and qualifying processes.

They can divert their resources to closing sales instead, boosting your business’ sales performance.

 

Improve customer conversations when closing sales

Not all prospects end up converting even when they made it as far as one-click or a conversation with your sales reps away from becoming a closed deal.

This makes it crucial to ensure your sales reps can skillfully and effectively steer the conversation so your prospects successfully convert into customers.

Here the Cogito, an AI software enters the scene.

It uses real-time emotional intelligence software and incorporates machine learning with AI. It can analyze voice calls between your reps and prospects (or customers).

The software is primarily designed to augment your call center and sales agents with an AI coaching system.

For instance, if your reps speak too fast during a customer call, Cogito sends a prompt alerting them to talk slower and pace how they speak.

Cogito’s technology combines speech analytics and conventional sentiment with proprietary voice insights, which can help your sales reps spot opportunities on the fly during prospect and customer conversations.

Cogito’s AI coaching software detects and measures the customer’s sentiment by:

• Using AI informed by behavioral science and machine learning to uncover over 200 voice signals, revealing how speakers feel.
• Uncovering trends, patterns, and problems through data compiled in many conversations over time.

It can be almost impossible to detect by humans alone.

Cogito also provides an automatically generated experience score that alerts your agents of the customer’s sentiment in real-time, helping them adjust their speaking style accordingly.

 

Enhance customer loyalty and retention

It can take less of your resources to retain existing customers than convert new leads, making the loyalty stage a critical phase of your customer funnel journey.

AI can help you retain and establish loyal customers through machine learning technology that automatically displays highly personalized right before and after-sales offers.

For instance, AI-based Ecommerce website software integrations can help you build, upsell and cross-sell strategies automatically.

 

This can enhance your customer’s shopping experience further seamlessly while increasing your average revenue per buyer.

These tools can use machine learning and algorithms to deliver relevant and automated cart recommendations without requiring you to pre-set or configure anything.

In a nutshell, using an AI-driven platform that can seamlessly provide supplementary and interesting product offers tailored to each shopper boosts their customer experience.

This leads to a memorable experience with your brand, encouraging repeat business, nurturing trust, and in turn, increasing customer loyalty.

 

Start optimizing your customer funnel journey

AI-based software are powerful tools that can significantly reduce and streamline your busy-work tasks, enhancing the process and experience for your teams and your customers.

The right AI-driven platforms can help ensure your leads avoid slipping through the cracks as they move through each stage of the customer funnel journey.

These tools can help optimize your strategies to effectively convert your leads into paying and loyal customers.

While investing in tools with AI capabilities might not be cheap to start, the benefits can far outweigh the costs, and you will end up with seamless and efficient lead conversion processes in the long run.

The post How to Use AI to Bolster Your Customer Journey appeared first on Content Marketing Consulting and Social Media Strategy.

Source: convinceandconvert.com

Voices in AI – Episode 80: A Conversation with Charlie Burgoyne

Today’s leading minds talk AI with host Byron Reese

About this Episode

Episode 80 of Voices in AI features host Byron Reese and Charlie Burgoyne discussing the difficulty of defining AI and how computer intelligence and human intelligence intersect and differ.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

Transcript Excerpt

Byron Reese:

This is Voices in AI brought you by GigaOm and I’m Byron Reese.

Today my guest is Charlie Burgoyne.

He is the founder and CEO of Valkyrie Intelligence, a consulting firm with domain expertise in applied science and strategy.

He’s also a general partner for Valkyrie Signals, an AI-driven hedge fund based in Austin, as well as the managing partner for Valkyrie labs, an AI credit company.

Charlie holds a master’s degree in theoretical physics from Georgetown University and a bachelor’s in nuclear physics from George Washington University.

I had the occasion to meet Charlie when we shared a stage when we were talking about AI and about 30 seconds into my conversation with him I said we gotta get this guy on the show.

And so I think ‘strap in’ should be a fun episode. Welcome to the show, Charlie.

Charlie Burgoyne:

Thanks so much, Byron for having me, excited to talk to you today.

Let’s start with [this]:

maybe re-enact a little bit of our conversation when we first met.

Tell me how you think of artificial intelligence, like what is it?

What is artificial about it and what is intelligent about it?

Sure, so the further I get down in this field, I start thinking about AI with two different definitions.

Definitions of AI

It’s a servant with two masters.

It has its private sector, applied narrowband applications where AI is really all about understanding patterns that we perform and that we capitalize on every day and automating those — things like approving time cards and making selections within a retail environment.

And that’s really where the real value of AI is right now in the market and [there’s] a lot of people in that space who are developing really cool algorithms that capitalize on the potential patterns that exist and largely lay dormant in data.

In that definition, intelligence is really about the cycles that we use within a cognitive capability to instrument our life and it’s artificial in that we don’t need an organic brain to do it.

Now the AI that I’m obsessed with from a research standpoint (a lot of academics are and I know you are as well Byron) — that AI definition is actually much more around the nature of intelligence itself because, in order to artificially create something, we must first understand it in its primitive state and its in its unadulterated state.

And I think that’s where the bulk of the really fascinating research in this domain is going, is just understanding what intelligence is, in and of itself.

Now I’ll come kind of straight to the interesting part of this conversation, which is I’ve had not quite a hundred guests on the show.

I can count on one hand the number who think it may not be possible to build a general intelligence.

According to our conversation, you are convinced that we can do it. Is that true?

And if so why?

Yes… The short answer is I am not convinced we can create a generalized intelligence, and that’s become more and more solidified the deeper and deeper I go into research and familiarity with the field.

Decision-making with AI

If you really unpack intelligent decision-making, it’s actually much more complicated than a simple collection of gates, a simple collection of empirically driven singular decisions, right?

A lot of the neural network scientists would have us believe that all decisions are really the right permutation of weighted neurons interacting with other layers of weighted neurons.

From what I’ve been able to tell so far with our research, either that is not getting us towards the goal of creating a truly intelligent entity or it’s doing the best within the confines of the mechanics we have at our disposal now.

In other words, I’m not sure whether or not the lack of progress towards a true generalized intelligence is due to the fact that

• (a) the digital environment that we have tried to create said artificial intelligence in is unamenable to that objective or
• (b) the nuances that are inherent to intelligence… I’m not positive yet those are things through which we have an understanding of modeling, nor would we ever be able to create a way of modeling that.

I’ll give you a quick example: If we think of any science fiction movie that encapsulates the nature of what AI will eventually be, whether it’s Her, or Ex Machina or Skynet or you name it.

There are a couple of big leaps that get glossed over in all science fiction literature and film, and those leaps are really around things like motivation.

• What motivates an AI, like what truly at its core motivates AI like the one in Ex Machina to leave her creator and to enter into the world and explore?
• How is that intelligence derived from innate creativity?
• How are they designing things?

How are they thinking about drawings and how are they identifying clothing that they need to put on?

All these different nuances are intelligently derived from that behavior.

We really don’t have a good understanding of that, and we’re not really making progress towards an understanding of that, because we’ve been distracted for the last 20 years with research in fields of computer science that aren’t really that closely related to understanding those core drivers.

So when you say a sentence like ‘I don’t know if we’ll ever be able to make a general intelligence,’ ever is a long time.

So do you mean that literally?

Tell me a scenario in which it is literally impossible — like it can’t be done, even if you came across a genie that could grant your wish.

It just can’t be done.

Like maybe time travel, you know — back in time, it just may not be possible.

Do you mean that ‘may not be possible?

Or do you just mean on a time horizon that is meaningful to humans?

I think it’s on the spectrum between the two.

But I think it leans closer towards ‘not ever possible under any condition.’

I was at a conference recently and I made this claim which admittedly as any claim with this particular question would be based on intuition and experience which are totally fungible assets.

But I made this claim that I didn’t think it was ever possible, and something the audience asked me, well, have you considered meditating to create a synthetic AI?

And the audience laughed and I stopped and I said: “You know that’s actually not the worst idea I’ve been exposed to.”

That’s not the worst potential solution for understanding intelligence to try and reverse engineer my own brain with as few distractions from its normal working mechanics as possible.

That may very easily be a credible aid to understanding how the brain works.

What is behind Gravity?

If we think about gravity, gravity is not a bad analog.

Gravity is this force that everybody and their mother who’s older than, you know who’s past fifth grade understands how it works, you drop an apple you know which direction it’s going to go.

Not only that but as you get experienced you can have a prediction of how fast it will fall, right?

If you were to see a simulation drop an apple and it takes twelve seconds to hit the ground, you’d know that that was wrong, even if the rest of the vector was correct, the scaler is off a little bit. Right?

The reality is that we can’t create an artificial gravity environment, right?

We can create forces that simulate gravity.

Centrifugal force is not a bad way of replicating gravity but we don’t actually know enough about the underlying mechanics that guide gravity such that we could create an artificial gravity using the same techniques, relatively the same mechanics that are used in organic gravity.

In fact, it was only a year and a half ago or so closer to two years now where the Nobel Prize for Physics was awarded to the individuals who identified that it was gravitational waves that permeate gravity (actually that’s how they do gravitons), putting to rest any argument that’s been going on since Einstein truly.

So I guess my point is that we haven’t really made progress in understanding the underlying mechanics, and every step we’ve taken has proven to be extremely valuable in the industrial sector but actually opened up more and more unknowns in the actual inner workings of intelligence.

If I had to bet today, not only is the time horizon on a true artificial intelligence extremely long-tailed but I actually think that it’s not impossible that it’s completely impossible altogether.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

Voices in AI

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Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Source: gigaom.com

The AI Talent Gap: Locating Global Data Science Centers

Good AI talent is hard to find. The talent pool for anyone with deep expertise in modern artificial intelligence techniques is terribly thin. More and more companies are committing to data and artificial intelligence as their differentiator.

The early adopters will quickly find difficulties in determining which data science expertise meets their needs.

And the AI talent?

If you are not Google, Facebook, Netflix, Amazon, or Apple, good luck.

With the popularity of AI, pockets of expertise are emerging around the world.

For a firm that needs AI expertise to advance its digital strategy, finding these data science hubs becomes increasingly important.

In this article we look at the initiatives different countries are pushing in the race to become AI leaders and we examine existing and potential data science centers.

 

Race to Be a Global AI Power

It seems as though every country wants to become a global AI power.

• With the Chinese government pledging billions of dollars in AI funding, other countries don’t want to be left behind.
• In Europe, France plans to invest €1.5 billion in AI research over the next 4 years while Germany has universities joining forces with corporations such as Porsche, Bosch, and Daimler to collaborate on AI research.
• Even Amazon, with a contribution of €1.25 million, is collaborating in the AI efforts in Germany’s Cyber Valley around the city of Stuttgart.
• Not one to be left behind, the UK pledged £300 million for AI research as well.
• Other countries to commit money to AI are Singapore, which committed $150 million, and Canada, which not only committed $125 million but also has large data science hubs in Toronto and Montreal.

Yoshua Bengio, one of the fathers of deep learning, is from Montreal, the city with the biggest group of AI researchers in the world.

Toronto has a booming tech industry that naturally attracts AI money.

 

Data Scientists Worldwide

Examining a variety of sources, data science professionals are spread across the regions where we would expect them.

The graphic below shows the number of members of the site Data Science Central.

Since the site is in English, we expect most of its members to come from English-speaking countries; however, it still gives us some insight as to which countries have higher representation.

Source

Global AI Hubs

It becomes difficult then to determine AI hubs without classifying talent by levels.

One example of this is India; despite its large number of data science professionals, many of them are employed in lower-skilled roles such as data labeling and processing.

So what would be considered a data science hub?

The graphic below defines a hub by the number of advanced AI professionals in the country.

The countries are shown here have AI talent working in companies such as Google, Baidu, Apple, and Amazon.

However, this omits a large group of talent that is not hired by these types of companies.

Source

Global AI Talent Pool

Matching the previous graph with a study conducted by Element AI, we see some commonalities, but also see some new hubs emerge.

The same talent centers remain, but more countries are highlighted on the map.

Element AI’s approach consisted of analyzing LinkedIn profiles, factoring in participation in conferences and publications, and weighing skills highly.

Source

Search for AI Talents

As you search for AI talent, we recommend basing your search on 4 factors:

1. workforce availability,
2. cost of labor,
3. English proficiency,
4. and skill level.

Kaggle, one of the most popular data science websites, conducted a salary survey with respondents from 171 countries. The results can be seen below.

Source

Salaries of AI Talent

Salaries are as expected, but show high variability.

By aggregating salary data and the talent pool map, you can decide which countries suit your goals better.

The EF English Proficiency Index shows which countries have the highest proficiency in English and can further weed out those that may have a strong AI presence or low cost of labor, but low English proficiency.

 

In the end,

you want to hire professionals that understand the problems you are facing and can tailor their work to your specific needs.

With a global mindset, companies can mitigate talent scarcity.

If you are considering sourcing talent globally, we recommend hiring strong leadership locally, who act as AI product managers that can manage a team.

Hire production managers located on-site with your global talent.

They can oversee any data science or AI development and report back to the product manager.

KUNGFU.AI will continue to study these global trends and help ensure companies are equipped with access to the best talent to meet their needs.

Source: gigaom.com

Voices in AI – Episode 79: A Conversation with Naveen Rao

Q-8:

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Q-16:

 

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Source: gigaom.com

AI at the Edge: A GigaOm Research Byte

Artificial intelligence (AI), primarily in the form of machine learning (ML), is making increasing inroads into our lives.

There are several primary reasons for this:

1. The rapidly-increasing capability of computers used to build and train ML models.
2. Greater data-capturing ability across the compute environment, often in the form of inexpensive sensors embedded in everyday consumer, business, and industrial products.
3. The development of new algorithms and approaches that improve the accuracy of ML applications.
4. The creation of software toolkits that make building and training ML applications substantially easier, and therefore less expensive.

In addition to these four ‘truths’, there are two other factors that are often overlooked that are equally as important in bringing AI into our lives.

These factors are not about where AI’s are built and trained, but where they are deployed and used:

• A reduction in cost, and increase in performance, of chips doing AI inference “at the edge.”
• The development of middleware allowing a broader range of applications to run seamlessly on a wider variety of chips.

It is these final two developments that will allow AI to enhance our lives in countless new ways and enable AI in our pockets, cars, houses, and a host of other places.

This report explores these latter two factors, ignoring how AI is built and trained while focusing on the methods by which AI impacts our lives.

It explores the natural architectural migration of AI from central, powerful computers where an AI algorithm or application may have historically been built, trained, and used, to an edge model.

In the edge model, the AI compute happens either on a user device or somewhere in the network stack beneath the traditional cloud, perhaps on an edge server.

This leads to a new AI model that is a match-fit for what is to come: building and training, which will mainly continue on ever-more-powerful (and power-hungry) cloud-based computers, and inference.

The inference will be performed at the device edge, or close to it.

It is where the AI will run on ever-more-powerful (but less power-hungry) chips.

This foundational change in the AI architecture will be the single biggest driver in the advance of AI at scale.

This new architecture has several advantages over a highly centralized or cloud model, specifically:

• More scalable
• Faster
• Lower cost
• More secure
• Lower power

There are tradeoffs in this approach, including the fundamental constraints of the chipset and future upgradability.

Further, there are still several outstanding questions about this shift that only time will answer:

• How far to the edge will AI compute finally be pushed to?
• Which chip design price/performance combinations will prove to be the most popular?
• How disposable will the chips of the future be?

It should be noted that there are use cases where this model of centralized training and edge inference will not be appropriate; cases where decision latency and power consumption are not factors.

One can imagine, for instance, that a large and expensive medical device might ship data back to a central location to be processed and analyzed on a time scale (perhaps measured in seconds or minutes) that would be unacceptable in another application, such as a self-driving car.

We discuss these exceptions as well.

The final part of this report briefly explores the societal impact of this change in architecture.

Winston Churchill once said, “We shape our tools and then the tools shape us.”

We are the generation that is shaping the digital tools of tomorrow, and it is worth reflecting on how they might shape us in return.

Source: gigaom.com

Voices in AI – Episode 78: A Conversation with Alessandro Vinciarelli

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm. I’m Byron Reese.

Today our guest is Alessandro Vinciarelli.

He is a full professor at the University of Glasgow.

He holds a Ph.D. in applied mathematics from the University of Bern. Welcome to the show, Alessandro.

A-1: Alessandro Vinciarelli: Welcome. Good morning.

 

Work in Artificial Intelligence

Q-1: Tell me a little bit about the kind of work you do in artificial intelligence. 

I work on a particular domain that is called social signal processing, which is the branch of artificial intelligence that deals with social psychological phenomena.

We can think of the goal of this particular part of the domain as trying to read the mind of people, and through this to interact with people in the same way as people do with one another.

Subtle Social Signals

Q-2: That is like picking up on subtle social cues that people naturally do, teaching machines to do that?

Exactly. At the core of this domain, there are what we call social signals that are nonverbal behavioral cues that people naturally exchange during their social interactions.

We talk here about, for example, facial expressions, spontaneous gestures, posture, how we talk in a broadcast, the way of speaking – not what people say, but how they say it.

The core idea is that basically, we can see facial expressions with our eyes, can hear the way people speak with our ears… and so it is also possible to sense these nonverbal behavioral cues with common sensors – like cameras, microphones, and so on.

Through automatic analysis of the signal into the application of artificial intelligence approaches, we can map the data information we extract from images, audio recordings, and so on into social cues and their meaning for the people that are involved in an interaction.

Commonness of Social Cues

Q-3: I guess implicit in that is an assumption that there’s a commonness of social cues across the whole human race? Is that the case?

Yes. Let’s say social signals are the point where nature meets nurture.

What does it mean?

It means that in the end, it’s something that is intimately related to our body, to our evolution, to our very natural being.

And in this sense, we all have a disposition of the same expressive means, in the sense that we all have the same way of speaking, the same voice, the same phonetic apparatus.

The face is the same for everybody.

We have the same muscles of disposition in order to express a facial expression.

The body is the same for everybody. So, from the way we talk to our bodies… is the same for all people around the world.

However, at the same time as we are a part of society, part of a context, we somewhat learn from others to express specific meaning, like for example a friendly attitude or a hostile attitude or happiness and so on, in a way that somewhat matches the others.

To give an example of how this can work when I moved to the U.K. … I’m originally from Italy, and I started to teach in this university.

A teaching inspector came to see me and told me, “Well, Alessandro, you have to move your arms a little bit less, because you sound very aggressive.

You look very aggressive to the students.”

You see, in Italy, it is quite normal to move hands a lot, especially when we communicate in front of an audience.

However, here in the U.K., when people use their arms – because everybody around the world does it – I have to do it in a bit more moderate way, in a more let’s say British way, in order to not sound aggressive.

So, you see, gestures communicate all over the world.

However, the accepted intensity you use changes from one place to the other.

Practical Applications of AI

Q-4: What are some of the practical applications of what you’re working on?

Well, it is quite an exciting time for the community working on these types of topics.

After the very pioneering years, if we look at the history of this particular branch of artificial intelligence, we can see that roughly the early 2000s was a very pioneering time.

Then the community was established more or less between the late 2000s and three or four years ago when the technology started to work pretty well.

And now we are at the point where we start seeing applications of these technologies initially developed at the research level in the laboratories in the real world.

To give an idea, think of today’s personal assistants that can not only understand what we say and what we ask but also how we express our requests.

Think of many animated characters that can interact with the actual agents, social robots, and so on.

They are slowly entering into reality and interacting with people like people do – through gestures, through facial expressions, and so on.

We see more and more companies that are involved and active in these types of domains.

For example, we have systems that manage to recognize the emotions of people through sensors that can be carried like a watch on the wrist.

We have very interesting systems.

I collaborate in particular with a company called Neurodata Lab that analyzes the content of multimedia material, trying to get an idea of its emotional content.

That can be useful in any type of service about video on demand.

There is a major force toward more human-computer interfaces or more in general human/machine interfaces that can figure out how we feel in order to intervene appropriately and interact appropriately with us.

These are a few major examples.

Non-verbal Communication

Q-5: So, there’s voice, which I guess you could use over a telephone to determine some emotional state. And there are facial expressions. And there are other physical expressions. Are there other categories beyond those three that bifurcate or break up the world when you’re thinking of different kinds of signals?

Yes, somewhat.

The very fact that we are alive and we have a body somewhat forces us to have nonverbal behavioral cues, how they are called, to communicate through our body.

And even if you try not to communicate, that becomes somewhat of a cue and becomes a form of communication.

And there are so many nonverbal behavioral cues that psychologists group them into five fundamental classes.

One is whatever happens with the head.

Facial expressions, we’ve mentioned, but there are also movements of the head, shaking, nodding, and so on.

Then we have the posture.

Now at this moment, we are talking into a microphone.

But, for example, when you talk to people, you tend to face them. You can talk to them by not facing them, but the type of impression would be totally different.

Then we have gestures. When we talk about gestures, we talk about the spontaneous movements we make.

So, it’s not like the OK gesture with the thumb. It’s not like pointing to something.

These have a pretty specific meaning.

For example, self-touching… that typically communicates some kind of discomfort.

It is restrictive movements we make when we speak from a cognitive point of view.

Speaking and gesturing is a cognitive bimodal unit, so it’s something that gets lumped together.

Then we have the way of speaking, as I mentioned.

Not what we say, but how we say it.

So, the sound of the voice, and so on.

Then there is appearance, everything we can do in order to change our appearance.

So, for example, the attractiveness of the person, but also the kind of clothes you wear, the type of ornaments you have, and so on.

And the last one is the organization of space.

For example, in a company, the more important you are, the bigger your office is.

So space from that point of view communicates a form of social verticality.

Similarly, we modulate our distances with respect to other people, not only in physical tasks but also in social terms.

The closer a person is to us from a social point of view, the closer we let them come from a physical point of view.

So, these are the five wide categories of social signals that psychologists fundamentally recognize as the most important.

Training Artificial Intelligence

Q-6: Well, as you go through them, I guess I can see how AI would be used. They’re all forms of data that could be measured. So, presumably, you can train an artificial intelligence on them. 

That is exactly the core idea of the domain and of the application of artificial intelligence in these types of problems.

So, the point is that to communicate with others, to interact with others, we have to manifest our inner state to our behavior – to what we do.

Because we cannot imagine communicating something that is not observable… Whatever is observable, meaning it is accessible to our senses, is something that is accessible to artificial sensors.

Once you can measure, once you can extract data about something, that is where artificial intelligence comes into play.

At this point, you can extract data, and the data can be automatically analyzed, then you can automatically infer information about the social and psychological phenomena taking place from the data you managed to capture.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Source: gigaom.com

Voices in AI – Episode 77: A Conversation with Nicholas Thompson

About this Episode

Episode 77 of Voices in AI features host Byron Reese and Nicholas Thompson discussing AI, humanity, social credit, as well as information bubbles.

Nicholas Thompson is the editor in chief of WIRED magazine, contributing editor at CBS, co-founder of The Atavist, and also worked at The New Yorker and authored a Cold War-era biography.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

 

Transcript Excerpt

 

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

 

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Source: gigaom.com

Voices in AI – Episode 76: A Conversation with Rudy Rucker

About this Episode

Episode 76 of Voices in AI features host Byron Reese and Rudy Rucker discuss the future of AGI, the metaphysics involved in AGI, and delve into whether the future will be for humanity’s good or ill.

Rudy Rucker is a mathematician, a computer scientist, as well as being a writer of fiction and nonfiction, with awards for the first two of the books in his Ware Tetralogy series.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm, I’m Byron Reese.

Today my guest is Rudy Rucker.

He is a mathematician, a computer scientist, and a science fiction author.

He has written books of fiction and nonfiction, and he’s probably best known for his novels in the Ware Tetralogy, which consists of software, wetware, freeware, and real-ware.

The first two of those won Philip K. Dick awards. Welcome to the show, Rudy.

Rudy Rucker: It’s nice to be here Byron.

This seems like a very interesting series you have and I’m glad to hold forth on my thoughts about AI.

Wonderful. I always like to start with my Rorschach question which is: What is artificial intelligence? And why is it artificial?

Well, a good working definition has always been the Turing test.

If you have a device or program that can convince you that it’s a person, then that’s pretty close to being intelligent.

So it has to master conversation?

It can do everything else, it can paint the Mona Lisa, it could do a million other things, but if it can’t converse, it’s not AI?

No those other things are also a big part of it.

You’d want it to be able to write a novel, ideally, or to develop scientific theories—to do the kinds of things that we do, in an interesting way.

Well, let me try a different tack, what do you think intelligence is?

I think intelligence is to have a sort of complex interplay with what’s happening around you.

You don’t want the old cliche that the robotic voice or the screen with capital letters on it, just not even able to use contractions, “do not help me.”

You want something that’s flexible and playful in intelligence.

I mean even in movies when you look at the actors, you often will get a sense that this person is deeply unintelligent or this person has an interesting mind.

It’s a richness of behavior, a sort of complexity that engages your imagination.

And do you think it’s artificial?

Is artificial intelligence actual intelligence or is it something that can mimic intelligence and look like intelligence, but it doesn’t actually have any, there’s no one actually home?

Right, well I think the word artificial is misleading.

I think as you asked me before the interview about my being friends with Stephen Wolfram, and one of Wolfram’s points has been that any natural process can embody universal computation.

Once you have the universal computation, it seems like, in principle, you might be able to get intelligent behavior emerging even if it’s not programmed.

So then, it’s not clear that there’s some bright line that separates human intelligence from the rest of the intelligence.

I think when we say “artificial intelligence,” what we’re getting at is the idea that it would be something that we could bring into being, either by designing or probably more likely by evolving it in a laboratory setting.

So, on the Stephen Wolfram thread, his view is everything’s computation and that you can’t really say there’s much difference between a human brain and a hurricane, because what’s going on in there is essentially a giant clockwork running its program, and it’s all really computational equivalence, it’s all kind of the same in the end, do you ascribe to that?

Yeah, I’m a convert.

I wouldn’t use the word ‘clockwork’ that you use because that already slips in an assumption that computation is in some way clunky and with gears and teeth, because we can have things—

But it’s deterministic, isn’t it?

It’s deterministic, yes, so I guess in that sense it’s like clockwork.

So Stephen believes, and you hate to paraphrase something as big as like his view on science, but he believes that everything is—not a clockwork, I won’t use that word—but everything is deterministic.

But, even the most deterministic things, when you iterate them, become unpredictable, and they’re not unpredictable inherently, like from a universal standpoint.

But they’re unpredictable from how finite our minds are.

They’re in practice unpredictable?

Correct.

So, a lot of natural processes, like well there’s like when you take Physics I, you say oh, I can predict where if I fire an artillery shot where it’s going to land because it’s going to travel along a perfect parabola and then I can just work it out on the back of an envelope in a few seconds.

And then when you get into reality, well they don’t actually travel on perfect parabolas, they have this odd-shaped curve due to air friction, that’s not linear, it depends how fast they’re going.

And then, you skip into saying “Well, I really would have to simulate this click.”

And then when you get into saying you have to predict something by simulating the process, then the event itself is simulating itself already, and in practice, the simulation is not going to run appreciably faster than just waiting for the event to unfold, and that’s the catch.

We can take a natural process and it’s computational in the sense that it’s deterministic, so you think well, cool, I’ll just find out the rule it’s using and then I’ll use some math tricks and I’ll predict what it’s going to do.

For most processes, it turns out there aren’t any quick shortcuts, that’s actually all.

It was worked on by Alan Turing way back when he proved that you can’t effectively get extreme speed-ups of universal processes.

So then we’re stuck with saying, maybe it’s deterministic, but we can’t predict it, and going slightly off on a side thread here, this question of free will always come up, because we say well, “we’re not like deterministic processes because nobody can predict what we do.”

And the thing is if you get a really good AI program that’s running at its top level, then you’re not going to be able to predict that either.

So, we kind of confuse free will with unpredictability, but actually, unpredictability’s enough.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

 

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Source: gigaom.com