Ben Dickson

About Me

Ben Dickson is an experienced software engineer and tech blogger. He contributes regularly to major tech websites such as the Next Web, the Daily Dot, PCMag.com, Cointelegraph, VentureBeat, International Business Times UK, and The Huffington Post.

Why is edge AI important?

Artificial intelligence can predict stocks, diagnose patients, hire job applicants, play the games of chess and go, and do many more tasks on par or better than humans. Humans still have an advantage, however: They have intelligence at the edge.

Why should cloud companies care about blockchain?

Why would a cloud company that makes billions of dollars from its cloud business endorse a technology that many believe obviates the need for the cloud? Blockchain does not necessarily come at the price of destroying traditional businesses. There are excellent ways for centralized businesses to adopt the transparency, security and open source nature of blockchain technology. But the technology is still struggling with many challenges, one of the most prominent being the storage of large data. In that context, the cloud and blockchain can coexist.

The limits and challenges of deep learning

Deep learning, the spearhead of artificial intelligence, is perhaps one of the most exciting technologies of the decade. There’s no doubt that machine learning and deep learning are super-efficient for many tasks. Deep learning is often compared to the mechanisms that underlie the human mind. However, they’re not a silver bullet that will solve all problems and override all previous technologies. Beyond the hype surrounding deep learning, in many cases, its distinct limits and challenges prevent it from competing with the mind of a human child.

What is natural language processing and generation (NLP/NLG)?

Behind the revolution in digital assistants and other conversational interfaces are natural language processing and generation (NLP/NLG), two branches of machine learning that involve converting human language to computer commands and vice versa. NLP and NLG have removed many of the barriers between humans and computers, not only enabling them to understand and interact with each other, but also creating new opportunities to augment human intelligence and accomplish tasks that were impossible before. Maybe NLP and NLG will remain focused on fulfilling more and more utilitarian use cases.

Should the AI industry fear the coming of GDPR?

One of the domains where the General Data Protection Regulation (GDPR)will leave its mark prominently is the artificial intelligence industry. Data is the bread and butter of contemporary AI, and under previously lax regulations, tech companies had been helping themselves to users’ data without fearing the consequences. That will change on May 25, when GDPR comes into effect. GDPR requires all companies that collect and handle user data in the European Union to be more transparent about their practices and more responsible for the security and privacy of their users. 

Why we should focus on weak artificial intelligence for the moment

Spurred by the capabilities of deep learning, and how it has so far defied the norms set by traditional software, many organizations and visionaries are thinking once again that strong AI is on the horizon and want to catch it before others do.  But while all this talent focuses on finding a way to create strong AI that can compete with the human brain, we’re missing out on plenty of the opportunities and failing to address the threats that current weak AI technology presents.

How to counter the threat of AI-based forgery

Foundation of trust is slowly fading as a new generation of AI-doctored videos find their way into the mainstream. Famously known as “deepfakes,” the synthetic videos are created using an application called FakeApp, which uses artificial intelligence to swap the faces in a video with those of another person. Educating people on the capabilities of AI algorithms will be a good measure to prevent the bad uses of applications like FakeApp having widespread impact—at least in the short term. We also need technological measures to back up our ethical and legal safeguards against deepfakes and other forms of AI-based forgery.

AI, big data and the future of humanity

Those who control the data control the future not just of humanity, but the future of life itself because today, data is the most important asset in the world. The combination of data and computation creates a power that surpasses that of the most powerful spy agencies of past centuries. This is changing thanks to the rise of machine learning and deep learning, smart artificial intelligence software that can mine huge sets of data and find meaningful patterns that would go unnoticed to the biologically limited minds of human beings.

The benefits and challenges of tokenizing social media

While many of the industries professing to make revolutionary use of distributed ledgers hardly offer a relevant use case, social media is one of the fields that can benefit immensely from blockchain and tokenized economies. Social media data is where blockchain can help. In a nutshell, blockchain is a distributed ledger or database. When you port social media to the blockchain, the immediate benefit users will gain is exclusive ownership of their data. 

Why the difference between AI and machine learning matters

There’s much confusion surrounding artificial intelligence and machine learning. Some people refer to AI and machine learning as synonyms and use them interchangeably, while other use them as separate, parallel technologies. In many cases, the people speaking and writing about the technology don’t know the difference between AI and ML. In others, they intentionally ignore those differences to create hype and excitement for marketing and sales purposes. This post disambiguates the differences between artificial intelligence and machine learning to help you distinguish fact from fiction where AI is concerned.

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