Click to learn more about author David Talby.
A shortage of technical talent has long
been a challenge for getting AI projects off the ground. While research shows
that this may still be the case, it’s not the end-all-be-all and certainly not
the only reason so many AI initiatives are doomed from the start.
Deloitte’s recent State of AI in the Enterprise survey found the type of talent most in-demand — AI developers and engineers, AI researchers, and data scientists — was fairly consistent across all levels of AI proficiency. However, business leaders, domain experts, and project managers fell lower on the list. While there’s no disputing that technical talent is valuable and necessary, the lack of attention on the latter titles should be a bigger part of the conversation.
It’s likely that the technical skills gap will persist for the next few years, as university programs play catch up to real-world applications of AI, and organizations implement internal training or opt for outsourcing entirely. That doesn’t mean businesses can wait for these problems to solve themselves or for the talent pool to grow. In order to avoid being one of the 85 percent of AI projects that fail to deliver on their intended promises, there are three areas organizations can focus on to give their projects a fighting chance.
Organizational Buy-In: AI-Driven Product, Revenue, and Customer Success
Understanding how AI will work within a professional and product environment and how it translates to a better customer experience and new revenue opportunities is critical — and that spans far beyond the IT team. Being able to train and deploy accurate AI models doesn’t address the question of how to most effectively use them to help your customers. Doing this requires educating all organizational disciplines — sales, marketing, product, design, legal, customer success — on why this is useful and how it will impact their job function.
When done well, new capabilities
unlocked by AI enable product teams to completely rethink the user experience.
It’s the difference between adding Netflix or Spotify recommendations as a side
feature versus designing the user interface around content discovery. More
aspirationally, it’s the difference between adding a lane departure alert to
your new car versus building a self-driving vehicle that doesn’t have pedals or
wheels. Cross-functional collaboration and buy-in on AI projects is a vital
part of the success and scaling and should be a priority from the get-go.
Realistic Expectations: The Lab vs. the Real World
We’re at an exciting juncture for AI development, and it’s easy to get caught up in the “new shiny object” mentality. While eagerness to implement new AI-enabled efficiencies is a good thing, jumping in before setting expectations is a sure-fire way to end up disappointed. A real instance of the challenges organizations face when implementing and scaling AI projects comes from a recent Google Research paper about a new deep learning model used to detect diabetic retinopathy from images of patients’ eyes. Diabetic retinopathy, when untreated, causes blindness, but if detected early, it can often be prevented. As a response, scientists trained a deep learning model to identify early stages of the disease symptom to accelerate detection and prevention.
Google had access to advanced machines for model training
and data from environments that followed proper protocols for testing. So,
while the technology itself was as accurate, if not more so than human
specialists, this didn’t matter when applied to clinics in rural Thailand.
There, the quality of the machines, lighting in the rooms in the clinic, and
patients’ willingness to participate for a host of reasons were quite different
than the conditions the model was trained on. The lack of appropriate infrastructure
and understanding of practical limitations is a prime example of the discord
between Data Science success and business success.
The Right Foundation: Tools and Processes to Operate Safely
Successful AI products and services
require applied skills in three layers. First, data scientists must be
available, productively tooled, and have domain expertise and access to
relevant data. While AI technology is becoming well understood, from bias
prevention, explainability, concept drift, and similar issues, many teams are
still struggling with this first layer of technical issues. Second,
organizations must learn how to deploy and operate AI models in production.
This requires DevOps, SecOps, and newly emerging “AI Ops” tools and processes
to be put in place, so models continue working accurately in production over
time. Third, product managers and business leaders must be involved from the
start in order to redesign new technical capabilities and how they will be
applied to make customers and end-users successful.
There’s been tremendous progress in
education and tooling over the past five years, but it’s still early days for
operating AI models in production. Unfortunately, design and product management
are far behind, and becoming one of the most common barriers to AI success.
This is why it might be time for respondents of the aforementioned Deloitte
survey to start putting overall business success and organizational buy-in
before finding the top technical talent to lead the way. The antidote for this
is investing in hands-on education and training, and fortunately, from the
classroom to technical training courses, these are becoming more widely
Although a relatively new technology, AI has the power to
change how we work and live for the better. That said, like any technology, AI
success hinges on proper training, education, buy-in, and well-understood
expectations and business value. Aligning all of these factors takes time, so
be patient, and be sure to have a strategy in place to ensure your AI efforts
Deep Science: AI adventures in arts and letters
There’s more AI news out there than anyone can possibly keep up with. But you can stay tolerably up to date on the most interesting developments with this column, which collects AI and machine learning advancements from around the world and explains why they might be important to tech, startups or civilization.
To begin on a lighthearted note: The ways researchers find to apply machine learning to the arts are always interesting — though not always practical. A team from the University of Washington wanted to see if a computer vision system could learn to tell what is being played on a piano just from an overhead view of the keys and the player’s hands.
Audeo, the system trained by Eli Shlizerman, Kun Su and Xiulong Liu, watches video of piano playing and first extracts a piano-roll-like simple sequence of key presses. Then it adds expression in the form of length and strength of the presses, and lastly polishes it up for input into a MIDI synthesizer for output. The results are a little loose but definitely recognizable.
“To create music that sounds like it could be played in a musical performance was previously believed to be impossible,” said Shlizerman. “An algorithm needs to figure out the cues, or ‘features,’ in the video frames that are related to generating music, and it needs to ‘imagine’ the sound that’s happening in between the video frames. It requires a system that is both precise and imaginative. The fact that we achieved music that sounded pretty good was a surprise.”
Another from the field of arts and letters is this extremely fascinating research into computational unfolding of ancient letters too delicate to handle. The MIT team was looking at “locked” letters from the 17th century that are so intricately folded and sealed that to remove the letter and flatten it might permanently damage them. Their approach was to X-ray the letters and set a new, advanced algorithm to work deciphering the resulting imagery.
“The algorithm ends up doing an impressive job at separating the layers of paper, despite their extreme thinness and tiny gaps between them, sometimes less than the resolution of the scan,” MIT’s Erik Demaine said. “We weren’t sure it would be possible.” The work may be applicable to many kinds of documents that are difficult for simple X-ray techniques to unravel. It’s a bit of a stretch to categorize this as “machine learning,” but it was too interesting not to include. Read the full paper at Nature Communications.
You arrive at a charge point for your electric car and find it to be out of service. You might even leave a bad review online. In fact, thousands of such reviews exist and constitute a potentially very useful map for municipalities looking to expand electric vehicle infrastructure.
Georgia Tech’s Omar Asensio trained a natural language processing model on such reviews and it soon became an expert at parsing them by the thousands and squeezing out insights like where outages were common, comparative cost and other factors.
Will Artificial Intelligence Replace Portfolio Managers in The Financial Industry?
I’m an Entrepreneur, Artificial Intelligence expert and Software Engineer with over 15 years of experience.
Artificial Intelligence (AI) chess gamers and poker players have already proven they could beat human masters. What’s to stop AI from doing the same with financial markets? What happens when AI becomes a portfolio player?
To some extent, it already has, even though investment success relies strongly on human interactions. In fact, very few industries depend on employees’ decisions as much as the financial markets. With AI, are these human decisions being overwritten by machine learning?
The reality is that “algorithm trading” has already impinged the market, exacerbating the exclusive Down Jones plummet of 700 points in 20 minutes back in February 2018. Traders and analysts agreed that the growing speed of algorithm trading models and automated sell orders impacted the collapse that day.
There are many positive roles that AI can play in the financial industry. AI algorithms can reduce risk, detect and manage fraud, improve operational efficiencies, and deliver improved customer services.
For example, there are systems currently in place within banks and other financial institutions that flag irregular behavior on accounts. Machine learning takes this function to a higher level by providing information that is much more sophisticated and precise.
The Financial Times reported a case of identity theft that was detected when the criminal used the scroll bar on a user’s site. The actual user consistently used a trackpad when banking online, and based on this inconsistency, the bank’s AI picked up the discrepancy. Technology efficiencies are already in place with many large finance corporations. AI again goes beyond this as algorithms provide highly-functioning robotics for operational processes and customer service.
The greatest intrusion and the biggest impact on the finance sector — whether it’s perceived as good or bad — is that major banks and hedge funds are using information gleaned from machine learning algorithms to advise clients on portfolio investments.
According to Deloitte, the pre-AI analysis relied on financial statements, earnings reports, press releases, investor presentations, blogs, and news articles. The process was painstakingly tedious. Today, AI analytics are now able to process these sources of information. They also process digital and non-traditional massive amounts of data such as web and social media information. This data can be calculated using natural language processing capabilities to give investors instantaneous trading tips. But, here’s the caveat:
Past performance is not necessarily an indicator of future performance.
It’s the footnote on any investment report or statement, and it is a critical factor to keep in mind when selecting investment advisors or portfolio managers based on their AI capabilities. While all the newly available data can improve holding decisions, there are intangible market factors and an advisor’s intuition that won’t always be captured by AI.
Keeping that in mind, prudent investors should do their own research and ask their advisor how AI is playing a part in their company’s processes. It’s safe to say that firms that are not actively gathering big data and employing AI processes with sophisticated algorithms will not remain competitive in the future.
As financial investors continue to have angst over AI and the markets, the logical viewpoint that Mike Chen, an equity portfolio manager at PanAngora, shared with CNN in an interview earlier this year is a great stance, “It’s not man versus machine. It’s man plus machine.”
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UK’s MHRA says it has ‘concerns’ about Babylon Health — and flags legal gap around triage chatbots
The U.K.’s medical device regulator has admitted it has concerns about VC-backed AI chatbot maker Babylon Health. It made the admission in a letter sent to a clinician who’s been raising the alarm about Babylon’s approach toward patient safety and corporate governance since 2017.
The HSJ reported on the MHRA’s letter to Dr. David Watkins yesterday. TechCrunch has reviewed the letter (see below), which is dated December 4, 2020. We’ve also seen additional context about what was discussed in a meeting referenced in the letter, as well as reviewing other correspondence between Watkins and the regulator in which he details a number of wide-ranging concerns.
In an interview he emphasized that the concerns the regulator shares are “far broader” than the (important but) single issue of chatbot safety.
“The issues relate to the corporate governance of the company — how they approach safety concerns. How they approach people who raise safety concerns,” Watkins told TechCrunch. “That’s the concern. And some of the ethics around the mispromoting of medical devices.
“The overall story is they did promote something that was dangerously flawed. They made misleading claims with regards to how [the chatbot] should be used — its intended use — with [Babylon CEO] Ali Parsa promoting it as a ‘diagnostic’ system — which was never the case. The chatbot was never approved for ‘diagnosis.’”
“In my opinion, in 2018 the MHRA should have taken a much firmer stance with Babylon and made it clear to the public that the claims that were being made were false — and that the technology was not approved for use in the way that Babylon were promoting it,” he went on. “That should have happened and it didn’t happen because the regulations at that time were not fit for purpose.”
“In reality there is no regulatory ‘approval’ process for these technologies and the legislation doesn’t require a company to act ethically,” Watkins also told us. “We’re reliant on the health tech sector behaving responsibly.”
The consultant oncologist began raising red flags about Babylon with U.K. healthcare regulators (CQC/MHRA) as early as February 2017 — initially over the “apparent absence of any robust clinical testing or validation,” as he puts it in correspondence to regulators. However with Babylon opting to deny problems and go on the attack against critics his concerns mounted.
An admission by the medical devices regulator that all Watkins’ concerns are “valid” and are “ones that we share” blows Babylon’s deflective PR tactics out of the water.
“Babylon cannot say that they have always adhered to the regulatory requirements — at times they have not adhered to the regulatory requirements. At different points throughout the development of their system,” Watkins also told us, adding: “Babylon never took the safety concerns as seriously as they should have. Hence this issue has dragged on over a more than three-year period.”
During this time the company has been steaming ahead inking wide-ranging “digitization” deals with healthcare providers around the world — including a 10-year deal agreed with the U.K. city of Wolverhampton last year to provide an integrated app that’s intended to have a reach of 300,000 people.
It also has a 10-year agreement with the government of Rwanda to support digitization of its health system, including via digitally enabled triage. Other markets it’s rolled into include the U.S., Canada and Saudi Arabia.
Babylon says it now covers more than 20 million patients and has done 8 million consultations and “AI interactions” globally. But is it operating to the high standards people would expect of a medical device company?
Safety, ethical and governance concerns
In a written summary, dated October 22, of a video call which took place between Watkins and the U.K. medical devices regulator on September 24 last year, he summarizes what was discussed in the following way: “I talked through and expanded on each of the points outlined in the document, specifically; the misleading claims, the dangerous flaws and Babylon’s attempts to deny/suppress the safety issues.”
In his account of this meeting, Watkins goes on to report: “There appeared to be general agreement that Babylon’s corporate behavior and governance fell below the standards expected of a medical device/healthcare provider.”
“I was informed that Babylon Health would not be shown leniency (given their relationship with [U.K. health secretary] Matt Hancock),” he also notes in the summary — a reference to Hancock being a publicly enthusiastic user of Babylon’s “GP at hand” app (for which he was accused in 2018 of breaking the ministerial code).
In a separate document, which Watkins compiled and sent to the regulator last year, he details 14 areas of concern — covering issues including the safety of the Babylon chatbot’s triage; “misleading and conflicting” T&Cs — which he says contradict promotional claims it has made to hype the product; as well as what he describes as a “multitude of ethical and governance concerns” — including its aggressive response to anyone who raises concerns about the safety and efficacy of its technology.
This has included a public attack campaign against Watkins himself, which we reported on last year; as well as what he lists in the document as “legal threats to avoid scrutiny and adverse media coverage.”
Here he notes that Babylon’s response to safety concerns he had raised back in 2018 — which had been reported on by the HSJ — was also to go on the attack, with the company claiming then that “vested interest” were spreading “false allegations” in an attempt to “see us fail.”
“The allegations were not false and it is clear that Babylon chose to mislead the HSJ readership, opting to place patients at risk of harm, in order to protect their own reputation,” writes Watkins in associated commentary to the regulator.
He goes on to point out that, in May 2018, the MHRA had itself independently notified Babylon Health of two incidents related to the safety of its chatbot (one involving missed symptoms of a heart attack, another missed symptoms of DVT) — yet the company still went on to publicly rubbish the HSJ’s report the following month (which was entitled: “Safety regulators investigating concerns about Babylon’s ‘chatbot’”).
Wider governance and operational concerns Watkins raises in the document include Babylon’s use of staff NDAs — which he argues leads to a culture inside the company where staff feel unable to speak out about any safety concerns they may have; and what he calls “inadequate medical device vigilance” (whereby he says the Babylon bot doesn’t routinely request feedback on the patient outcome post triage, arguing that: “The absence of any robust feedback system significant impairs the ability to identify adverse outcomes”).
Re: unvarnished staff opinions, it’s interesting to note that Babylon’s Glassdoor rating at the time of writing is just 2.9 stars — with only a minority of reviewers saying they would recommend the company to a friend and where Parsa’s approval rating as CEO is also only 45% on aggregate. (“The technology is outdated and flawed,” writes one Glassdoor reviewer who is listed as a current Babylon Health employee working as a clinical ops associate in Vancouver, Canada — where privacy regulators have an open investigation into its app. Among the listed cons in the one-star review is the claim that: “The well-being of patients is not seen as a priority. A real joke to healthcare. Best to avoid.”)
Per Watkins’ report of his online meeting with the MHRA, he says the regulator agreed NDAs are “problematic” and impact on the ability of employees to speak up on safety issues.
He also writes that it was acknowledged that Babylon employees may fear speaking up because of legal threats. His minutes further record that: “Comment was made that the MHRA are able to look into concerns that are raised anonymously.”
In the summary of his concerns about Babylon, Watkins also flags an event in 2018 which the company held in London to promote its chatbot — during which he writes that it made a number of “misleading claims,” such as that its AI generates health advice that is “on-par with top-rated practicing clinicians.”
The flashy claims led to a blitz of hyperbolic headlines about the bot’s capabilities — helping Babylon to generate hype at a time when it was likely to have been pitching investors to raise more funding.
The London-based startup was valued at $2 billion+ in 2019 when it raised a massive $550 million Series C round, from investors including Saudi Arabia’s Public Investment Fund and a large (unnamed) U.S.-based health insurance company, as well as insurance giant Munich Re’s ERGO Fund — trumpeting the raise at the time as the largest ever in Europe or U.S. for digital health delivery.
“It should be noted that Babylon Health have never withdrawn or attempted to correct the misleading claims made at the AI Test Event [which generated press coverage it’s still using as a promotional tool on its website in certain jurisdictions],” Watkins writes to the regulator. “Hence, there remains an ongoing risk that the public will put undue faith in Babylon’s unvalidated medical device.”
In his summary he also includes several pieces of anonymous correspondence from a number of people claiming to work (or have worked) at Babylon — which make a number of additional claims. “There is huge pressure from investors to demonstrate a return,” writes one of these. “Anything that slows that down is seen [a]s avoidable.”
“The allegations made against Babylon Health are not false and were raised in good faith in the interests of patient safety,” Watkins goes on to assert in his summary to the regulator. “Babylon’s ‘repeated’ attempts to actively discredit me as an individual raises serious questions regarding their corporate culture and trustworthiness as a healthcare provider.”
In its letter to Watkins (screengrabbed below), the MHRA tells him: “Your concerns are all valid and ones that we share.”
It goes on to thank him for personally and publicly raising issues “at considerable risk to yourself.”
Babylon has been contacted for a response to the MHRA’s validation of Watkins’ concerns. At the time of writing it had not responded to our request for comment.
The startup told the HSJ that it meets all the local requirements of regulatory bodies for the countries it operates in, adding: “Babylon is committed to upholding the highest of standards when it comes to patient safety.”
In one aforementioned aggressive incident last year, Babylon put out a press release attacking Watkins as a “troll” and seeking to discredit the work he was doing to highlight safety issues with the triage performed by its chatbot.
It also claimed its technology had been “NHS validated” as a “safe service 10 times.”
It’s not clear what validation process Babylon was referring to there — and Watkins also flags and queries that claim in his correspondence with the MHRA, writing: “As far as I am aware, the Babylon chatbot has not been validated — in which case, their press release is misleading.”
The MHRA’s letter, meanwhile, makes it clear that the current regulatory regime in the U.K. for software-based medical device products does not adequately cover software-powered “health tech” devices, such as Babylon’s chatbot.
Per Watkins there is no approval process, currently. Such devices are merely registered with the MHRA — but there’s no legal requirement that the regulator assess them or even receive documentation related to their development. He says they exist independently — with the MHRA holding a register.
“You have raised a complex set of issues and there are several aspects that fall outside of our existing remit,” the regulator concedes in the letter. “This highlights some issues which we are exploring further, and which may be important as we develop a new regulatory framework for medical devices in the U.K.”
An update to pan-EU medical devices regulation — which will bring in new requirements for software-based medical devices and had been originally intended to be implemented in the U.K. in May last year — will no longer take place, given the country has left the bloc.
The U.K. is instead in the process of formulating its own regulatory update for medical device rules. This means there’s still a gap around software-based “health tech” — which isn’t expected to be fully plugged for several years. (Although Watkins notes there have been some tweaks to the regime, such as a partial lifting of confidentiality requirements last year.)
In a speech last year, health secretary Hancock told parliament that with the government aimed to formulate a regulatory system for medical devices that is “nimble enough” to keep up with tech-fueled developments such as health wearables and AI while “maintaining and enhancing patient safety.” It will include giving the MHRA “a new power to disclose to members of the public any safety concerns about a device,” he said then.
In the meanwhile the existing (outdated) regulatory regime appears to be continuing to tie the regulator’s hands — at least vis-a-vis what they can say in public about safety concerns. It has taken Watkins making its letter to him public to do that.
In the letter the MHRA writes that “confidentiality unfortunately binds us from saying more on any specific investigation,” although it also tells him: “Please be assured that your concerns are being taken seriously and if there is action to be taken, then we will.”
“Based on the wording of the letter, I think it was clear that they wanted to provide me with a message that we do hear you, that we understand what you’re saying, we acknowledge the concerns which you’ve raised, but we are limited by what we can do,” Watkins told us.
He also said he believes the regulator has engaged with Babylon over concerns he’s raised these past three years — noting the company has made a number of changes after he had raised specific queries (such as to its T&Cs, which had initially said it’s not a medical device but were subsequently withdrawn and changed to acknowledge it is; or claims it had made that the chatbot is “100% safe” which were withdrawn — after an intervention by the Advertising Standards Authority in that case).
The chatbot itself has also been tweaked to put less emphasis on the diagnosis as an outcome and more emphasis on the triage outcome, per Watkins.
“They’ve taken a piecemeal approach [to addressing safety issues with chatbot triage]. So I would flag an issue [publicly via Twitter] and they would only look at that very specific issue. Patients of that age, undertaking that exact triage assessment — ‘okay, we’ll fix that, we’ll fix that’ — and they would put in place a [specific fix]. But sadly, they never spent time addressing the broader fundamental issues within the system. Hence, safety issues would repeatedly crop up,” he said, citing examples of multiple issues with cardiac triages that he also raised with the regulator.
“When I spoke to the people who work at Babylon they used to have to do these hard fixes … All they’d have to do is just kind of ‘dumb it down’ a bit. So, for example, for anyone with chest pain it would immediately say go to A&E. They would take away any thought process to it,” he added. (It also of course risks wasting healthcare resources — as he also points out in remarks to the regulators.)
“That’s how they over time got around these issues. But it highlights the challenges and difficulties in developing these tools. It’s not easy. And if you try and do it quickly and don’t give it enough attention then you just end up with something that is useless.”
Watkins also suspects the MHRA has been involved in getting Babylon to remove certain pieces of hyperbolic promotional material related to the 2018 AI event from its website.
In one curious episode, also related to the 2018 event, Babylon’s CEO demoed an AI-powered interface that appeared to show real-time transcription of a patient’s words combined with an “emotion-scanning” AI — which he said scanned facial expressions in real time to generate an assessment of how the person was feeling — with Parsa going on to tell the audience: “That’s what we’ve done. That’s what we’ve built. None of this is for show. All of this will be either in the market or already in the market.”
However neither feature has actually been brought to market by Babylon as yet. Asked about this last month, the startup told TechCrunch: “The emotion detection functionality, seen in old versions of our clinical portal demo, was developed and built by Babylon‘s AI team. Babylon conducts extensive user testing, which is why our technology is continually evolving to meet the needs of our patients and clinicians. After undergoing pre-market user testing with our clinicians, we prioritized other AI-driven features in our clinical portal over the emotion recognition function, with a focus on improving the operational aspects of our service.”
“I certainly found [the MHRA’s letter] very reassuring and I strongly suspect that the MHRA have been engaging with Babylon to address concerns that have been identified over the past three-year period,” Watkins also told us today. “The MHRA don’t appear to have been ignoring the issues but Babylon simply deny any problems and can sit behind the confidentiality clauses.”
In a statement on the current regulatory situation for software-based medical devices in the U.K., the MHRA told us:
The MHRA ensures that manufacturers of medical devices comply with the Medical Devices Regulations 2002 (as amended). Please refer to existing guidance.
The Medicines and Medical Devices Act 2021 provides the foundation for a new improved regulatory framework that is currently being developed. It will consider all aspects of medical device regulation, including the risk classification rules that apply to Software as a Medical Device (SaMD).
The U.K. will continue to recognize CE marked devices until 1 July 2023. After this time, requirements for the UKCA Mark must be met. This will include the revised requirements of the new framework that is currently being developed.
The Medicines and Medical Devices Act 2021 allows the MHRA to undertake its regulatory activities with a greater level of transparency and share information where that is in the interests of patient safety.
The regulator declined to be interviewed or respond to questions about the concerns it says in the letter to Watkins that it shares about Babylon — telling us: “The MHRA investigates all concerns but does not comment on individual cases.”
“Patient safety is paramount and we will always investigate where there are concerns about safety, including discussing those concerns with individuals that report them,” it added.
Watkins raised one more salient point on the issue of patient safety for “cutting edge” tech tools — asking where is the “real-life clinical data”? So far, he says the studies patients have to go on are limited assessments — often made by the chatbot makers themselves.
“One quite telling thing about this sector is the fact that there’s very little real-life data out there,” he said. “These chatbots have been around for a good few years now … And there’s been enough time to get real-life clinical data and yet it hasn’t appeared and you just wonder if, is that because in the real-life setting they are actually not quite as useful as we think they are?”
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The best books and podcasts on data science and AI for 2021
Data science and AI are among the best (and highest paying) careers in the world right now, so it makes sense to keep increasing your knowledge, and learning from the best. But doing that in an age of information overload isn’t easy.
One way to stay ahead of the game is to ensure you’re reading the best material, and being inspired by the greats while you work from home (or wherever is safe and possible right now), and that means picking the best books and podcasts.
But with a dizzying amount of choice on these two important subjects, it can be hard to know what to read or listen to.
So we’ve done the hard work for you, and chosen the best recent books, and the top podcasts, on both data science and AI, so that you can save time and become better, faster.
Whether you want to brush up on data structures and algorithms, understand the intricacies of machine learning, gain direction and discover good processes, hear from giants in the industry, or be inspired with new ideas, the books and podcasts featured here will accelerate your learning.
Books on data science and AI
This excellent book is for those that find it difficult to grasp what is going on thanks to other texts being heavy on math jargon and obtuse concepts. It sets out to demystify computer science fundamentals, and does a fantastic job of doing so.
Broken into two distinct books, this resource breaks everything down into simple, easy-to-follow explanations of the foundations behind machine learning, from mathematical and statistical concepts to the programming behind them.
This book will take you from little or no Python experience to being able to use it and various Python libraries, including numpy, matplotlib, random, pandas, and sklearn, for problem solving. It covers computational techniques, and some data science tools and techniques, as well as machine learning.
Billed as an “atlas” rather than focusing on helping you understand just one aspect of computer intelligence and data science, this books aims to help you chart your path to encompass all of the aspects of our field, and ultimately become something different; a pure network scientist.
AI and data science podcasts
While not solely about pure data science and AI, this podcast – which is billed as “conversations about the nature of intelligence, consciousness, love, and power” – is a real treasure trove of amazing discussions and interviews that will keep you inspired and engaged.
While the team behind this podcast is taking a break to reflect on important issues such as Black Lives Matter, hosts Katherine Gorman and Neil Lawrence have built up an impressive library of episodes that include discussions with experts in the field, industry news, and useful answers to your machine learning questions.
Ted Sarvata and Brandon Sanders are (at the time of writing) 70 episodes into their deep dive on how AI is affecting our daily lives, and whether it presents a risk to humanity, as many have suggested in recent history.
Centered on data science, machine learning, and artificial intelligence, Data Skeptic digs into each topic in detail. For example, a recent episode is a conversation with Yuqi Ouyang, who in his second year of PhD study at the University of Warwick in England, gives details on his work “Video Anomaly Detection by Estimating Likelihood of Representations.”
So there you have it. Four books and four podcasts to help you get ahead in data science and AI. Enjoy, and grow.
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